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Archive for the ‘Intelligent Information Systems’ Category


Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

 

Reporter: Aviva Lev-Ari, PhD, RN

 

BOSTON – May 10, 2018 Vyasa Analytics, a provider of deep learning software and analytics for life sciences and healthcare organizations, today announces three pre-built deep learning analytics modules for its Cortex software at Bio-IT World Conference & Expo. Cortex enables the secure, scalable application of deep learning-based artificial intelligence (AI) analytics to enterprise data, identifying patterns, relationships and concepts across disparate data sources.

 

The new Neural Concept Recognition, Image Analytics and ChemVector analytics modules in Cortex enable life sciences organizations to quickly and easily apply deep learning analytics to large data streams of text, images and chemical structures. Like all deep learning analytical modules in Cortex’s library, these new modules allow users to ask complex questions of their data and use the answers to gain critical insights.

 

“Life sciences and healthcare organizations are using AI tools to advance research and development and deliver better patient care. Deep learning algorithms provide a set of powerful approaches that help us apply analytics more effectively and comprehensively across large scale data sources,” said Dr. Christopher Bouton, founder and CEO of Vyasa. “The idea of AI has been around for decades, but we are now experiencing a perfect storm of GPU-based computing power, deep learning algorithm advances and highly scalable data sources that enables paradigm-shifting machine learning and analytics capabilities.”

 

Vyasa will be demoing three deep learning analytics modules for Cortex at Bio-IT World 2018 in Boston from May 15 to 17, including:

 

  • Neural Concept Recognition. This module can be trained on text concepts (e.g. drugs, diseases, pathways, conditions, side effects, genes) in structured and unstructured data. Users can ask Cortex complex questions across large scale data sets, and discover unexpected relationships between concept types. Concept recognition analytics is applicable to a wide range of use cases from competitive intelligence, to drug repurposing and EHR analytics.

 

  • Life Sciences R&D Specialized Image Analytics. Deep learning enables novel, powerful forms of image analytics, capable of being trained to detect patterns and objects in large scale image data sources. With just a few clicks in Cortex, the user can connect large streams of image data and apply analytics to those sources. Vyasa has finely-tuned this analysis for life sciences images, and it is ideal for cell assay screening, drug manufacturing and post-market screening for counterfeit packaging and tablets.
  • ChemVector de novo Compound Design. This proprietary Cortex module applies deep learning to chemical structures. Users can drag and drop one or more SDF files containing SMILES strings into Cortex, and Cortex can identify and generate novel compounds that optimize critical variables such as log-p, molecular weight and synthetic viability. ChemVector can be used with a range of other chemistry-specific analytical modules also available in Cortex.

 

 

Dr. Bouton, Vyasa’s founder and CEO, received his BA in Neuroscience (Magna Cum Laude) from Amherst College in 1996 and his Ph.D. in Molecular Neurobiology from Johns Hopkins University in 2001. Previously Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.

 

Visit Vyasa and demo Cortex at booth #632, and watch the explainer video at www.vyasa.com.

About Vyasa Analytics

Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations. Cortex is Vyasa’s secure, highly scalable software platform for collaborative knowledge discovery and data analytics. Using Vyasa’s proprietary Neural Concept Recognition technology, Cortex identifies trends and patterns across disparate data sources, empowering project teams to gain insights and drive better decision making. Learn more at www.vyasa.com.

 

 

Angela Zmyslinski
Account Executive
azmyslinski@matternow.com
Office – 401-330-2800

     

SOURCE

From: Angela Zmyslinski <azmyslinski@matternow.com>

Date: Thursday, May 10, 2018 at 2:39 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: Demo deep learning software for life sciences at Bio-IT World 2018

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Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

Linguamatics Introduces Breakthrough Scientific Search Solution

iScite provides end-users with direct access to powerful AI-driven insights from text

Boston — May 9, 2018 — Linguamatics, the leading NLP-based text analytics provider for biomedical applications, today announced the launch of Linguamatics iScite, a breakthrough innovation in scientific search that puts the precision and power of Linguamatics artificial intelligence (AI) technology directly into the hands of scientists, researchers and other knowledge workers. iScite offers a modern, easy-to-use scientific search engine that provides intuitive access to AI-powered searches across key biomedical data sources and delivers insightful answers to search questions.

iScite is designed as a next-generation search experience that empowers non-technical users to conduct their own NLP-based scientific searches to extract data insights. Rather than rely on time- and/or resource-crunched technical experts to create and perform searches, iScite enables users to quickly and independently find precise answers to their high-value questions.

“Traditional search methods are often time-consuming, expensive and ineffective, and the results are imprecise and difficult to sift through,” said Jane Reed, head of life science strategy for Linguamatics. “With iScite, users can take advantage of the power of NLP without the traditional complexities. Our patent-pending Answer-Routing Engine interprets users’ search terms and guides them to the best possible answers to their questions. Searches are seamless across multiple content sources, and users are quickly pointed to the exact content relevant to their search without having to laboriously read through every word of the source documents.”

iScite uses Linguamatics’ award-winning technology stack to handle the nuances of language and the variety of ways people express the same information, ensuring searches are comprehensive and accurate. Using advanced NLP relationship and pattern matching, iScite rapidly guides users directly to the relevant insights extracted from cloud-hosted scientific content. Results are presented in a structured, semantically-meaningful way, with options for dynamic filtering and faceting, and multiple collaboration features to allow easier sharing of insights with co-workers and key stakeholders. Behind the scenes Linguamatics uses a powerful blend of NLP and machine learning-based methods to achieve the best precision and recall.

“By empowering end-user scientists and clinicians with an easy-to-use search engine, we are speeding their access to the right knowledge for decision-making to advance the discovery, development and delivery of therapeutics,” said Linguamatics Executive Chairman John Brimacombe. “iScite has the potential to revolutionize the search process for the biomedical industry by providing everyone with rapid access to the knowledge they need, while freeing data scientists and informaticians to focus on the most challenging, in-depth search projects. iScite is a breakthrough in scientific research, filling an industry demand for a self-service alternative that delivers deep insights in a single search.”

Linguamatics will demonstrate iScite at Bio-IT World 2018 in Boston May 15-17. Visit us at booth #549, or go to our website, http://www.linguamatics.com/iscite, for more information.

 

About Linguamatics
Linguamatics
 transforms unstructured big data into big insights to advance human health and wellbeing. A world leader in deploying innovative text analytics for high-value knowledge discovery and decision support, Linguamatics’ solutions are used by top commercial, academic and government organizations, including 18 of the top 20 global pharmaceutical companies, the US Food and Drug Administration (FDA) and leading US healthcare organizations.

Linguamatics Media contact:
Michelle Ronan Noteboom, Sr. Account Director
Amendola Communications
+ 1 512.426.2870
mnoteboom@acmarketingpr.com

 

SOURCE

From: Chad Van Alstin <cvanalstin@acmarketingpr.com>

Date: Thursday, May 10, 2018 at 11:30 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: Big News from NLP-Leader Linguamatics at Bio IT World – Can I arrange a meeting?

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Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

Curator: Aviva Lev-Ari, PhD, RN

 

Synopsis Day 1: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/23/synopsis-day-1-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

 

Synopsis Day 2: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/24/https-pharmaceuticalintelligence-wordpress-com-p47489previewtruesynopsis-day-2-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachus/

 

Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/25/synopsis-day-3-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Reporter: Aviva Lev-Ari, PhD, RN

 

Synthego Raises $41 Million From Investors, Including a Top Biochemist

Synthego also drew in Dr. Doudna, who had crossed paths with the company’s head of synthetic biology at various industry conferences. According to Mr. Dabrowski, the money from her trust represents the single-biggest check from a non-institutional investor that the start-up has raised.

Synthego’s new funds will help the company take its products to a more global customer base, as well as broaden its offerings. The longer-term goal, Mr. Dabrowski said, is to help fully automate biotech research and take care of much of the laboratory work that scientists currently handle themselves.

The model is cloud technology, where companies rent out powerful remote server farms to handle their computing needs rather than rely on their own hardware.

“We’ll be able to do their full research workflow,” he said. “If you look at how cloud computing developed, it used to be that every company handled their server farm. Now it’s all handled in the cloud.”

SOURCE

Other related articles published in this Open Access Online Scientific Journal include the following:

UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/06/status-interference-initial-memorandum-crisprcas9-the-biotech-patent-fight-of-the-century/

 

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Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microscope uses nanosecond-speed laser and deep learning to detect cancer cells more efficiently

April 13, 2016

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Time-stretch quantitative phase imaging (TS-QPI) and analytics system

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

Abstract of Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

references:

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali. Deep Learning in Label-free Cell Classification. Scientific Reports 6, Article number: 21471 (2016); doi:10.1038/srep21471 (open access)

Supplementary Information

 

Deep Learning in Label-free Cell Classification

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang,Kayvan Reza Niazi & Bahram Jalali

Scientific Reports 6, Article number: 21471 (2016)    http://dx.doi.org:/10.1038/srep21471

Deep learning extracts patterns and knowledge from rich multidimenstional datasets. While it is extensively used for image recognition and speech processing, its application to label-free classification of cells has not been exploited. Flow cytometry is a powerful tool for large-scale cell analysis due to its ability to measure anisotropic elastic light scattering of millions of individual cells as well as emission of fluorescent labels conjugated to cells1,2. However, each cell is represented with single values per detection channels (forward scatter, side scatter, and emission bands) and often requires labeling with specific biomarkers for acceptable classification accuracy1,3. Imaging flow cytometry4,5 on the other hand captures images of cells, revealing significantly more information about the cells. For example, it can distinguish clusters and debris that would otherwise result in false positive identification in a conventional flow cytometer based on light scattering6.

In addition to classification accuracy, the throughput is another critical specification of a flow cytometer. Indeed high throughput, typically 100,000 cells per second, is needed to screen a large enough cell population to find rare abnormal cells that are indicative of early stage diseases. However there is a fundamental trade-off between throughput and accuracy in any measurement system7,8. For example, imaging flow cytometers face a throughput limit imposed by the speed of the CCD or the CMOS cameras, a number that is approximately 2000 cells/s for present systems9. Higher flow rates lead to blurred cell images due to the finite camera shutter speed. Many applications of flow analyzers such as cancer diagnostics, drug discovery, biofuel development, and emulsion characterization require classification of large sample sizes with a high-degree of statistical accuracy10. This has fueled research into alternative optical diagnostic techniques for characterization of cells and particles in flow.

Recently, our group has developed a label-free imaging flow-cytometry technique based on coherent optical implementation of the photonic time stretch concept11. This instrument overcomes the trade-off between sensitivity and speed by using Amplified Time-stretch Dispersive Fourier Transform12,13,14,15. In time stretched imaging16, the object’s spatial information is encoded in the spectrum of laser pulses within a pulse duration of sub-nanoseconds (Fig. 1). Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC). The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging. Detection sensitivity is challenged by the low number of photons collected during the ultra-short shutter time (optical pulse width) and the drop in the peak optical power resulting from the time stretch. These issues are solved in time stretch imaging by implementing a low noise-figure Raman amplifier within the dispersive device that performs time stretching8,11,16. Moreover, warped stretch transform17,18can be used in time stretch imaging to achieve optical image compression and nonuniform spatial resolution over the field-of-view19. In the coherent version of the instrument, the time stretch imaging is combined with spectral interferometry to measure quantitative phase and intensity images in real-time and at high throughput20. Integrated with a microfluidic channel, coherent time stretch imaging system in this work measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing 36 million images per second in flow rates as high as 10 meters per second, reaching up to 100,000 cells per second throughput.

Figure 1: Time stretch quantitative phase imaging (TS-QPI) and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier (EDFA), and a wavelength-division multiplexing (WDM) filter generate and shape a train of broadband optical pulses. http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

 

Box 1: The pulse train is spatially dispersed into a train of rainbow flashes illuminating the target as line scans. The spatial features of the target are encoded into the spectrum of the broadband optical pulses, each representing a one-dimensional frame. The ultra-short optical pulse illumination freezes the motion of cells during high speed flow to achieve blur-free imaging with a throughput of 100,000 cells/s. The phase shift and intensity loss at each location within the field of view are embedded into the spectral interference patterns using a Michelson interferometer. Box 2: The interferogram pulses were then stretched in time so that spatial information could be mapped into time through time-stretch dispersive Fourier transform (TS-DFT), and then captured by a single pixel photodetector and an analog-to-digital converter (ADC). The loss of sensitivity at high shutter speed is compensated by stimulated Raman amplification during time stretch. Box 3: (a) Pulse synchronization; the time-domain signal carrying serially captured rainbow pulses is transformed into a series of one-dimensional spatial maps, which are used for forming line images. (b) The biomass density of a cell leads to a spatially varying optical phase shift. When a rainbow flash passes through the cells, the changes in refractive index at different locations will cause phase walk-off at interrogation wavelengths. Hilbert transformation and phase unwrapping are used to extract the spatial phase shift. (c) Decoding the phase shift in each pulse at each wavelength and remapping it into a pixel reveals the protein concentration distribution within cells. The optical loss induced by the cells, embedded in the pulse intensity variations, is obtained from the amplitude of the slowly varying envelope of the spectral interferograms. Thus, quantitative optical phase shift and intensity loss images are captured simultaneously. Both images are calibrated based on the regions where the cells are absent. Cell features describing morphology, granularity, biomass, etc are extracted from the images. (d) These biophysical features are used in a machine learning algorithm for high-accuracy label-free classification of the cells.

On another note, surface markers used to label cells, such as EpCAM21, are unavailable in some applications; for example, melanoma or pancreatic circulating tumor cells (CTCs) as well as some cancer stem cells are EpCAM-negative and will escape EpCAM-based detection platforms22. Furthermore, large-population cell sorting opens the doors to downstream operations, where the negative impacts of labels on cellular behavior and viability are often unacceptable23. Cell labels may cause activating/inhibitory signal transduction, altering the behavior of the desired cellular subtypes, potentially leading to errors in downstream analysis, such as DNA sequencing and subpopulation regrowth. In this way, quantitative phase imaging (QPI) methods24,25,26,27 that categorize unlabeled living cells with high accuracy are needed. Coherent time stretch imaging is a method that enables quantitative phase imaging at ultrahigh throughput for non-invasive label-free screening of large number of cells.

In this work, the information of quantitative optical loss and phase images are fused into expert designed features, leading to a record label-free classification accuracy when combined with deep learning. Image mining techniques are applied, for the first time, to time stretch quantitative phase imaging to measure biophysical attributes including protein concentration, optical loss, and morphological features of single cells at an ultrahigh flow rate and in a label-free fashion. These attributes differ widely28,29,30,31 among cells and their variations reflect important information of genotypes and physiological stimuli32. The multiplexed biophysical features thus lead to information-rich hyper-dimensional representation of the cells for label-free classification with high statistical precision.

We further improved the accuracy, repeatability, and the balance between sensitivity and specificity of our label-free cell classification by a novel machine learning pipeline, which harnesses the advantages of multivariate supervised learning, as well as unique training by evolutionary global optimization of receiver operating characteristics (ROC). To demonstrate sensitivity, specificity, and accuracy of multi-feature label-free flow cytometry using our technique, we classified (1) OT-IIhybridoma T-lymphocytes and SW-480 colon cancer epithelial cells, and (2) Chlamydomonas reinhardtii algal cells (herein referred to as Chlamydomonas) based on their lipid content, which is related to the yield in biofuel production. Our preliminary results show that compared to classification by individual biophysical parameters, our label-free hyperdimensional technique improves the detection accuracy from 77.8% to 95.5%, or in other words, reduces the classification inaccuracy by about five times.     ……..

 

Feature Extraction

The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig. 2 (see Section Methods: Image Reconstruction). These images are used to obtain biophysical fingerprints of the cells8,36. With domain expertise, raw images are fused and transformed into a suitable set of biophysical features, listed in Table 1, which the deep learning model further converts into learned features for improved classification.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f2.jpg

The optical loss images of the cells are affected by the attenuation of multiplexed wavelength components passing through the cells. The attenuation itself is governed by the absorption of the light in cells as well as the scattering from the surface of the cells and from the internal cell organelles. The optical loss image is derived from the low frequency component of the pulse interferograms. The optical phase image is extracted from the analytic form of the high frequency component of the pulse interferograms using Hilbert Transformation, followed by a phase unwrapping algorithm. Details of these derivations can be found in Section Methods. Also, supplementary Videos 1 and 2 show measurements of cell-induced optical path length difference by TS-QPI at four different points along the rainbow for OT-II and SW-480, respectively.

Table 1: List of extracted features.

Feature Name    Description         Category

 

Figure 3: Biophysical features formed by image fusion.

(a) Pairwise correlation matrix visualized as a heat map. The map depicts the correlation between all major 16 features extracted from the quantitative images. Diagonal elements of the matrix represent correlation of each parameter with itself, i.e. the autocorrelation. The subsets in box 1, box 2, and box 3 show high correlation because they are mainly related to morphological, optical phase, and optical loss feature categories, respectively. (b) Ranking of biophysical features based on their AUCs in single-feature classification. Blue bars show performance of the morphological parameters, which includes diameter along the interrogation rainbow, diameter along the flow direction, tight cell area, loose cell area, perimeter, circularity, major axis length, orientation, and median radius. As expected, morphology contains most information, but other biophysical features can contribute to improved performance of label-free cell classification. Orange bars show optical phase shift features i.e. optical path length differences and refractive index difference. Green bars show optical loss features representing scattering and absorption by the cell. The best performed feature in these three categories are marked in red.

Figure 4: Machine learning pipeline. Information of quantitative optical phase and loss images are fused to extract multivariate biophysical features of each cell, which are fed into a fully-connected neural network.

The neural network maps input features by a chain of weighted sum and nonlinear activation functions into learned feature space, convenient for classification. This deep neural network is globally trained via area under the curve (AUC) of the receiver operating characteristics (ROC). Each ROC curve corresponds to a set of weights for connections to an output node, generated by scanning the weight of the bias node. The training process maximizes AUC, pushing the ROC curve toward the upper left corner, which means improved sensitivity and specificity in classification.

….   How to cite this article: Chen, C. L. et al. Deep Learning in Label-free Cell Classification.

Sci. Rep. 6, 21471; http://dx.doi.org:/10.1038/srep21471

 

Computer Algorithm Helps Characterize Cancerous Genomic Variations

http://www.genengnews.com/gen-news-highlights/computer-algorithm-helps-characterize-cancerous-genomic-variations/81252626/

To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm called REVEALER. [UC San Diego Health]

Scientists at the University of California San Diego School of Medicine and the Broad Institute say they have developed a new computer algorithm—REVEALER—to better characterize the functional context of genomic variations in cancer. The tool, described in a paper (“Characterizing Genomic Alterations in Cancer by Complementary Functional Associations”) published in Nature Biotechnology, is designed to help researchers identify groups of genetic variations that together associate with a particular way cancer cells get activated, or how they respond to certain treatments.

REVEALER is available for free to the global scientific community via the bioinformatics software portal GenePattern.org.

“This computational analysis method effectively uncovers the functional context of genomic alterations, such as gene mutations, amplifications, or deletions, that drive tumor formation,” said senior author Pablo Tamayo, Ph.D., professor and co-director of the UC San Diego Moores Cancer Center Genomics and Computational Biology Shared Resource.

Dr. Tamayo and team tested REVEALER using The Cancer Genome Atlas (TCGA), the NIH’s database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.

For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.

REVEALER requires as input high-quality genomic data and a significant number of cancer samples, which can be a challenge, according to Dr. Tamayo. But REVEALER is more sensitive at detecting similarities between different types of genomic features and less dependent on simplifying statistical assumptions, compared to other methods, he adds.

“This study demonstrates the potential of combining functional profiling of cells with the characterizations of cancer genomes via next-generation sequencing,” said co-senior author Jill P. Mesirov, Ph.D., professor and associate vice chancellor for computational health sciences at UC San Diego School of Medicine.

 

Characterizing genomic alterations in cancer by complementary functional associations

Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, et al.

Nature Biotechnology (2016)              http://dx.doi.org:/10.1038/nbt.3527

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

 

Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.close

(a) This heatmap illustrates the use of the REVEALER approach to find complementary genomic alterations that match the transcriptional activation of β-catenin in cancer. The target profile is a TCF4 reporter that provides an estimate of…

 

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models

Jonathan P. Celli, Imran Rizvi, Adam R. Blanden, Iqbal Massodi, Michael D. Glidden, Brian W. Pogue & Tayyaba Hasan

Scientific Reports 4; 3751  (2014)    http://dx.doi.org:/10.1038/srep03751

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

The attrition rates for preclinical development of oncology therapeutics are particularly dismal due to a complex set of factors which includes 1) the failure of pre-clinical models to recapitulate determinants of in vivo treatment response, and 2) the limited ability of available assays to extract treatment-specific data integral to the complexities of therapeutic responses1,2,3. Three-dimensional (3D) tumour models have been shown to restore crucial stromal interactions which are missing in the more commonly used 2D cell culture and that influence tumour organization and architecture4,5,6,7,8, as well as therapeutic response9,10, multicellular resistance (MCR)11,12, drug penetration13,14, hypoxia15,16, and anti-apoptotic signaling17. However, such sophisticated models can only have an impact on therapeutic guidance if they are accompanied by robust quantitative assays, not only for cell viability but also for providing mechanistic insights related to the outcomes. While numerous assays for drug discovery exist18, they are generally not developed for use in 3D systems and are often inherently unsuitable. For example, colorimetric conversion products have been noted to bind to extracellular matrix (ECM)19 and traditional colorimetric cytotoxicity assays reduce treatment response to a single number reflecting a biochemical event that has been equated to cell viability (e.g. tetrazolium salt conversion20). Such approaches fail to provide insight into the spatial patterns of response within colonies, morphological or structural effects of drug response, or how overall culture viability may be obscuring the status of sub-populations that are resistant or partially responsive. Hence, the full benefit of implementing 3D tumour models in therapeutic development has yet to be realized for lack of analytical methods that describe the very aspects of treatment outcome that these systems restore.

Motivated by these factors, we introduce a new platform for quantitative in situ treatment assessment (qVISTA) in 3D tumour models based on computational analysis of information-dense biological image datasets (bioimage-informatics)21,22. This methodology provides software end-users with multiple levels of complexity in output content, from rapidly-interpreted dose response relationships to higher content quantitative insights into treatment-dependent architectural changes, spatial patterns of cytotoxicity within fields of multicellular structures, and statistical analysis of nodule-by-nodule size-dependent viability. The approach introduced here is cognizant of tradeoffs between optical resolution, data sampling (statistics), depth of field, and widespread usability (instrumentation requirement). Specifically, it is optimized for interpretation of fluorescent signals for disease-specific 3D tumour micronodules that are sufficiently small that thousands can be imaged simultaneously with little or no optical bias from widefield integration of signal along the optical axis of each object. At the core of our methodology is the premise that the copious numerical readouts gleaned from segmentation and interpretation of fluorescence signals in these image datasets can be converted into usable information to classify treatment effects comprehensively, without sacrificing the throughput of traditional screening approaches. It is hoped that this comprehensive treatment-assessment methodology will have significant impact in facilitating more sophisticated implementation of 3D cell culture models in preclinical screening by providing a level of content and biological relevance impossible with existing assays in monolayer cell culture in order to focus therapeutic targets and strategies before costly and tedious testing in animal models.

Using two different cell lines and as depicted in Figure 1, we adopt an ECM overlay method pioneered originally for 3D breast cancer models23, and developed in previous studies by us to model micrometastatic ovarian cancer19,24. This system leads to the formation of adherent multicellular 3D acini in approximately the same focal plane atop a laminin-rich ECM bed, implemented here in glass-bottom multiwell imaging plates for automated microscopy. The 3D nodules resultant from restoration of ECM signaling5,8, are heterogeneous in size24, in contrast to other 3D spheroid methods, such as rotary or hanging drop cultures10, in which cells are driven to aggregate into uniformly sized spheroids due to lack of an appropriate substrate to adhere to. Although the latter processes are also biologically relevant, it is the adherent tumour populations characteristic of advanced metastatic disease that are more likely to be managed with medical oncology, which are the focus of therapeutic evaluation herein. The heterogeneity in 3D structures formed via ECM overlay is validated here by endoscopic imaging ofin vivo tumours in orthotopic xenografts derived from the same cells (OVCAR-5).

 

Figure 1: A simplified schematic flow chart of imaging-based quantitative in situ treatment assessment (qVISTA) in 3D cell culture.

(This figure was prepared in Adobe Illustrator® software by MD Glidden, JP Celli and I Rizvi). A detailed breakdown of the image processing (Step 4) is provided in Supplemental Figure 1.

A critical component of the imaging-based strategy introduced here is the rational tradeoff of image-acquisition parameters for field of view, depth of field and optical resolution, and the development of image processing routines for appropriate removal of background, scaling of fluorescence signals from more than one channel and reliable segmentation of nodules. In order to obtain depth-resolved 3D structures for each nodule at sub-micron lateral resolution using a laser-scanning confocal system, it would require ~ 40 hours (at approximately 100 fields for each well with a 20× objective, times 1 minute/field for a coarse z-stack, times 24 wells) to image a single plate with the same coverage achieved in this study. Even if the resources were available to devote to such time-intensive image acquisition, not to mention the processing, the optical properties of the fluorophores would change during the required time frame for image acquisition, even with environmental controls to maintain culture viability during such extended imaging. The approach developed here, with a mind toward adaptation into high throughput screening, provides a rational balance of speed, requiring less than 30 minutes/plate, and statistical rigour, providing images of thousands of nodules in this time, as required for the high-content analysis developed in this study. These parameters can be further optimized for specific scenarios. For example, we obtain the same number of images in a 96 well plate as for a 24 well plate by acquiring only a single field from each well, rather than 4 stitched fields. This quadruples the number conditions assayed in a single run, at the expense of the number of nodules per condition, and therefore the ability to obtain statistical data sets for size-dependent response, Dfrac and other segmentation-dependent numerical readouts.

 

We envision that the system for high-content interrogation of therapeutic response in 3D cell culture could have widespread impact in multiple arenas from basic research to large scale drug development campaigns. As such, the treatment assessment methodology presented here does not require extraordinary optical instrumentation or computational resources, making it widely accessible to any research laboratory with an inverted fluorescence microscope and modestly equipped personal computer. And although we have focused here on cancer models, the methodology is broadly applicable to quantitative evaluation of other tissue models in regenerative medicine and tissue engineering. While this analysis toolbox could have impact in facilitating the implementation of in vitro 3D models in preclinical treatment evaluation in smaller academic laboratories, it could also be adopted as part of the screening pipeline in large pharma settings. With the implementation of appropriate temperature controls to handle basement membranes in current robotic liquid handling systems, our analyses could be used in ultra high-throughput screening. In addition to removing non-efficacious potential candidate drugs earlier in the pipeline, this approach could also yield the additional economic advantage of minimizing the use of costly time-intensive animal models through better estimates of dose range, sequence and schedule for combination regimens.

 

Microscope Uses AI to Find Cancer Cells More Efficiently

Thu, 04/14/2016 – by Shaun Mason

http://www.mdtmag.com/news/2016/04/microscope-uses-ai-find-cancer-cells-more-efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.

Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”

Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.

“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.   …..  see also http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

Chen, C. L. et al. Deep Learning in Label-free Cell Classification.    Sci. Rep. 6, 21471;   http://dx.doi.org:/10.1038/srep21471

 

 

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How do we address medical diagnostic errors?

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

It is my observation over the course of 50 years in medicine that there is no simple resolution to a frequently identified problem, which has grown more serious with the implementation of hospital-wide and system-wide health information technology.  There has been reduction of hospital staff, nurses and physicians, with an increased patient load even while the technology has advanced significantly.  Over this investment in technology and manpower the hospital staff time has become dependent on the medical record and a large portion of their time is drawn away from patient care.  This is not a substantial improvement in the last decade.  The problem has been a lack of focus on the correct design of these systems.  They should be interactive and they should have incorporated anticipatory logic.  Instead, they are not that much more than large electronic filing cabinets. There are large sources of information in laboratory, radiology and pharmacy and are siloed.  When one drives a car, one does not need to look under the hood, a sentiment that has been expressed by a real expert.

 

Diagnostic Errors Get the Attention of the Institute of Medicine, Reinforcing Efforts by Nation’s Clinical Pathology Laboratory Scientists to Improve Patient Safety

March 25, 2016         DARK DAILY info@darkreport.com via cmail2.com

 

Along with its assessment of the rate of errors in diagnosis, the IOM has a plan to improve, but will doctors accept the IOM’s advice, or continue business as usual?

Diagnostic errors in the American healthcare system is a problem that is now on the radar screen of policymakers at the Institute of Medicine (IOM). Pathologists andclinical laboratory professionals will welcome this development, because recommendations from the IOM carry weight with Congress.

Thus, should the IOM develop specific actions items intended to reduce medical errors, not only are these suggestions likely to involve more effective use of medical laboratory tests by physicians, but there is a strong probability that Congress might eventually write these recommendations into future healthcare legislation.

The Institute of Medicine is a division of the National Academies of Sciences, Engineering, and Medicine. The IOM recently convened a committee that released a list of recommendations to address the problem of diagnostic errors in medicine. Those recommendations, however, are running up against ingrained mindsets and overconfidence on the part of physicians who are reluctant to include decision-support technology in the diagnostic process.

Misdiagnoses in healthcare has led to tragic consequences. Over the years, the IOM has worked to increase the public’s awareness of the problem of misdiagnosis. It has also encouraged health information technology (HIT) developers to provide clinicians with the decision-support systems they need to prevent misdiagnosis from happening in the first place. But getting physicians to use the new tools has become a challenge.

IOM Dedicated to Improving Diagnostic Accuracy 

Preventing diagnostic errors has been a primary target in medical reform for many years. A 1999 report by the U.S Institute of Medicine (IOM) titled: “To Err Is Human: Building a Safer Health System” first drew attention to the rate of preventable medical errors in the U.S.

Since then, much has been written about the problem of diagnostic inaccuracy. Although many provider organizations are working to improve it, progress has been slow.

Click here to see image

Edward Hoffer, MD (above), FACP, FACC, FACMI, is a faculty member at the Massachusetts General Hospital Laboratory of Computer Science. He told Modern Healthcare that getting doctors to use diagnostic software is a hard sell. “The main problem we face is trying to convince physicians that they actually need to use it,” he concluded. (Image copyright: Massachusetts General Hospital, Laboratory of Computer Science.)

Solutions for Preventing Errors Face Many Challenges

From the first evaluation of the patient, to the clinical laboratory, to physician follow-up; errors can occur throughout the chain of care. And a culture that discourages reporting of medical errors can make it very difficult for healthcare organizations to resolve these issues.

For example, an investigation by the Milwaukee Journal Sentinel uncovered what they say is a “secretive system [that] hides [medical] lab errors from the public and puts patients at risk.”

To make matters worse, according to the Journal Sentinel article, federal agencies that are tasked with oversight of the more than 35,000 clinical laboratories in the U.S. (such as the Joint Commission) are overloaded and thus may be missing many violations that would lead to sanctions or outright loss of accreditation.

The Milwaukee Journal Sentinel stated that, “even when serious violations are identified, offending labs are rarely sanctioned except in the most extreme cases.” And that “in 2013, just 90 sanctions were issued—accounting for not even 1% of the 35,000 labs that do high-level lab testing in the United States.”

Additionally, there are different types of misdiagnoses, which also complicates matters. A study by three professors at the Dartmouth Center for Healthcare Deliver Science investigated the cost of what they termed “silent misdiagnoses.”

According to a Dartmouth Now article outlining the study, the disparity between “the treatments patients want and what doctors think they want” accounts for a great number of misdiagnoses. The study’s authors call these misdiagnoses “silent” because they are rarely reported or recorded.

Click here to see image

Albert G. Mulley, Jr., MD, MPP (above), is Managing Director, Global Health Care Delivery Science, The Dartmouth Institute for Health Policy and Clinical Practice; Professor of Medicine, Geisel School of Medicine at Dartmouth; and the lead author of the Dartmouth study. Mulley says that listening to the patient is more important than ever. “Today, the rise in treatment options makes this even more critical, not only to reach a correct medical diagnosis but also to understand fully the patients’ preferences, and reduce the huge waste in time and money that comes from delivery of services that patients often neither want nor need.” (Photo copyright: Dartmouth Now.)

IOM Committee on Diagnostic Error in Healthcare Releases Recommendations

In September, 2015, the IOM released its latest report in the series of reports about medical errors and diagnostic problems that goes back 17 years. The series started in 1999 with “To Err is Human: Building A Safer Health System” in 1999. This was followed in 2001 by “Crossing the Quality Chasm: A New Health System for the 21st Century.”

This newest report is titled, “Improving Diagnosis in HealthCare.” It comes from the IOM’s National Academies of Medicine’s Committee on Diagnostic Error in Healthcare. The study lists recommendations that the IOM says will help improve the rate of diagnostic errors. Included are:

• Facilitate more effective teamwork in the diagnostic process;

• Enhance education in the healthcare community on the topic of improving diagnostics;

• Incorporate diagnostic support into electronic health records (EHRs), and make EHRs fully interoperable;

• Set up systems to identify, learn from, and reduce diagnostic errors;

• Establish a culture that encourages investigation of mistakes;

• Develop a collaborative reporting system that involves clinicians, government agencies, and liability insurers that allows everyone to learn from diagnostic errors and near misses;

• Design a payment system that supports correct diagnosis; and

• Provide funding for research into how to improve the diagnostic process.

Click here to see video

This video (above) is of the IOM’s public release in September, 2015, of the Committee on Diagnostic Error in Healthcare’s report, “Improving Diagnosis in Health Care.” A full text of the report can be downloaded by clicking here, or by visiting http://iom.nationalacademies.org/reports/2015/improving-diagnosis-in-healthcare.

Technology Exists to Support Accurate Diagnoses

One area of development that offers real hope for lowering the rate of misdiagnosis is medical decision-support software—specifically in clinical informatics, where the integration of health data with health information technology (HIT) supports physician decision making at the point of care.

Whether that software is part of an organization’s EHR system, or is a standalone program dedicated to reducing diagnostic errors, can make a difference. For example, a research study published in the Journal of Clinical Bioinformatics) suggests that health information technology has an important role to play in improving diagnostic accuracy.

The challenge is that, although the technology currently exists, diagnostic error rates remain high. A research study published in the Journal of the American Medical Association Internal Medicine (JAMA) in 2013 may explain why. It states that overconfidence could prevent some physicians from re-evaluating questionable diagnoses.

In a Modern Healthcare article, Edward Hoffer, MD, FACP, FACC, FACMI, a faculty member at the Massachusetts General Hospital Laboratory of Computer Science, said that getting doctors to use diagnostic software is a hard sell. “The main problem we face is trying to convince physicians that they actually need to use it,” he concluded.

All of the building blocks for improving the diagnostic process are either in place, or as in the case of EHR interoperability, in the works. Clinical laboratory personnel are uniquely positioned to assist physicians in improving through communication and the careful use of technology.

—Dava Stewart

Related Information:

To Err Is Human

Weak Oversight Allows Lab Failures to Put Patients at Risk  

Dartmouth Study: ‘Silent’ Misdiagnoses by Doctors Are Common, and Come at Great Cost 

Press Release: Doctors ‘Silent’ Misdiagnoses Cost Patients Dearly . . . And US Health Care Billions

Improving Diagnosis in Health Care

Clinical Decision Support Systems for Improving Diagnostic Accuracy and Achieving Precision Medicine 

Using Software To Avoid Misdiagnoses 

In Conversation with…Mark L. Graber, MD 

 

All of the building blocks for improving the diagnostic process are either in place, or as in the case of EHR interoperability, in the works.   Nothing could be further from reality than what has been stated.   There is no minimization of keystrokes.  There is no anticipatory logic.  There is no automated comparison of medication and diagnoses electronically.  There is no check to determine the consistency of the laboratory results with a probability of diagnosis, or automated identification of tests that would clarify the situation.

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