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Posts Tagged ‘Machine Learning (ML)’

Real Time Conferecence Coverage: Advancing Precision Medicine Conference Philadelphia PA November 1,2 2024  Deliverables

Posted in Artificial Intelligence Applications in Health Care, Big Data, Bio Instrumentation in Experimental Life Sciences Research, Cancer Genomics, Cancer-Immune Interactions, Circulating Tumor Cells (CTC), Conference Coverage with Social Media, Deep Learning, Immuno-Oncology & Genomics, Machine Learning, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Precision Cancer Medicine, Proteomics, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific & Biotech Conferences: Press Coverage, Transcriptomics, Uncategorized, tagged , , , , , , , , , on October 21, 2025| 1 Comment »

Real Time Conferecence Coverage: Advancing Precision Medicine Conference Philadelphia PA November 1,2 2024  Deliverables

Curator: Stephen J. Williams, Ph.D.

Below are deliverables in form of real Time conference coverage from the Advancing Precision Medicine Confererence held this year in Philadelphia, PA.  The meeting brought together scientists and clinicians to discuss the challenges faced in implementing genomics and proteomics into precision medicine decision making workflow.  As summarized by a future release at the 2025 ASCO, there are many issues and hindrances to incorporating data obtained from sequencing to make a personalized medicine strategy.  The meeting focused on two main disease states: oncology and cardiovascular however most of  the live meeting notes are from the oncology tract.  In general it was discussed there are three areas which need to be addressed to correctly and more frequently incorporate precision medicine and genomic panel testing into clinical decision making workflow:

  1.  access to testing panels and testing methodology for both doctors and patients
  2. expert interpretation of results including algorithms needed to analyze the data
  3. more education of molecular biology and omics data and methodology in medical school to address knowledge gaps between clinicians and scientists

The issues can be summarized by a JCO report to ASCO in 2022:

 Helen Sadik, PhD, Daryl Pritchard, PhD https://orcid.org/0000-0003-2675-0371 dpritchard@personalizedmedicinecoalition.org, Derry-Mae Keeling, BSc, Frank Policht, PhD, Peter Riccelli, PhD, Gretta Stone, BS, Kira Finkel, MSPH, Jeff Schreier, MBA, and Susanne Munksted, MS.  Impact of Clinical Practice Gaps on the Implementation of Personalized Medicine in Advanced Non–Small-Cell Lung Cancer. 2022: JCO Precision Oncology; Volume 6. https://doi.org/10.1200/PO.22.00246

Personalized medicine presents new opportunities for patients with cancer. However, many patients do not receive the most effective personalized treatments because of challenges associated with integrating predictive biomarker testing into clinical care. Patients are lost at various steps along the precision oncology pathway because of operational inefficiencies, limited understanding of biomarker strategies, inappropriate testing result usage, and access barriers. We examine the impact of various clinical practice gaps associated with diagnostic testing-informed personalized medicine strategies on the treatment of advanced non–small-cell lung cancer (aNSCLC).

The authors used a  Diaceutics’ Data Repository, a multisource database including commercial and Medicare claims and laboratory data from over 500,000 patients with non–small-cell lung cancer in the United States. They  analyzed the number of patients with newly diagnosed aNSCLC who could have, but did not, benefit from a personalized treatment. The analysis was focused on identifying the gaps and at which steps during care did gaps existed which precipitated either lack of use of precision medicine testing or incorrect interpretation of results.

Their conclusions were alarming:

Most patients with aNSCLC eligible for precision oncology treatments do not benefit from them because of clinical practice gaps. This finding is likely reflective of similar gaps in other cancer types. An increased understanding of the impact of each practice gap can inform strategies to improve the delivery of precision oncology, helping to fully realize the promise of personalized medicine.

The links to the live meeting notes are given below and collection of tweets follow (please note this meeting did not have a Twitter hashtag)

Real Time Coverage Advancing Precision Medicine Annual Conference, Philadelphia PA November 1,2 2024

https://pharmaceuticalintelligence.com/2024/11/01/real-time-coverage-advancing-precision-medicine-annual-conference-philadelphia-pa-november-12-2024/

Real Time Coverage Morning Session on Precision Oncology: Advancing Precision Medicine Annual Conference, Philadelphia PA November 1 2024

https://pharmaceuticalintelligence.com/2024/11/01/real-time-coverage-morning-session-on-precision-oncology-advancing-precision-medicine-annual-conference-philadelphia-pa-november-1-2024/

Real Time Coverage Afternoon Session on Precision Oncology: Advancing Precision Medicine Annual Conference, Philadelphia PA November 1 2024

https://pharmaceuticalintelligence.com/2024/11/01/real-time-coverage-afternoon-session-on-precision-oncology-advancing-precision-medicine-annual-conference-philadelphia-pa-november-1-2024/ 

Real Time Coverage Morning Session on Precision Oncology: Advancing Precision Medicine Annual Conference, Philadelphia PA November 2 2024

https://pharmaceuticalintelligence.com/2024/11/04/real-time-coverage-morning-session-on-precision-oncology-advancing-precision-medicine-annual-conference-philadelphia-pa-november-2-2024/ 

Tweet Collection

Tweet Collection Advancing Precision Medicine Conference November 1,2 2024 Philadelphia PA

 

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Al is on the way to lead critical ED decisions on CT

Posted in AI Models in Healthcare, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Cancer Screening, Crohn's disease, CT, Gastroenterology, Liver & Digestive Diseases Research, Machine Learning, tagged , , , , , , on July 31, 2021| Leave a Comment »

Al is on the way to lead critical ED decisions on CT

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Artificial intelligence (AI) has infiltrated many organizational processes, raising concerns that robotic systems will eventually replace many humans in decision-making. The advent of AI as a tool for improving health care provides new prospects to improve patient and clinical team’s performance, reduce costs, and impact public health. Examples include, but are not limited to, automation; information synthesis for patients, “fRamily” (friends and family unpaid caregivers), and health care professionals; and suggestions and visualization of information for collaborative decision making.

In the emergency department (ED), patients with Crohn’s disease (CD) are routinely subjected to Abdomino-Pelvic Computed Tomography (APCT). It is necessary to diagnose clinically actionable findings (CAF) since they may require immediate intervention, which is typically surgical. Repeated APCTs, on the other hand, results in higher ionizing radiation exposure. The majority of APCT performance guidance is clinical and empiric. Emergency surgeons struggle to identify Crohn’s disease patients who actually require a CT scan to determine the source of acute abdominal distress.

Image Courtesy: Jim Coote via Pixabay https://www.aiin.healthcare/media/49446

Aid seems to be on the way. Researchers employed machine learning to accurately distinguish these sufferers from Crohn’s patients who appear with the same complaint but may safely avoid the recurrent exposure to contrast materials and ionizing radiation that CT would otherwise wreak on them.

The study entitled “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department” was published on July 9 in Digestive and Liver Disease by gastroenterologists and radiologists at Tel Aviv University in Israel.

Retrospectively, Jacob Ollech and his fellow researcher have analyzed 101 emergency treatments of patients with Crohn’s who underwent abdominopelvic CT.

They were looking for examples where a scan revealed clinically actionable results. These were classified as intestinal blockage, perforation, intra-abdominal abscess, or complex fistula by the researchers.

On CT, 44 (43.5 %) of the 101 cases reviewed had such findings.

Ollech and colleagues utilized a machine-learning technique to design a decision-support tool that required only four basic clinical factors to test an AI approach for making the call.

The approach was successful in categorizing patients into low- and high-risk groupings. The researchers were able to risk-stratify patients based on the likelihood of clinically actionable findings on abdominopelvic CT as a result of their success.

Ollech and co-authors admit that their limited sample size, retrospective strategy, and lack of external validation are shortcomings.

Moreover, several patients fell into an intermediate risk category, implying that a standard workup would have been required to guide CT decision-making in a real-world situation anyhow.

Consequently, they generate the following conclusion:

We believe this study shows that a machine learning-based tool is a sound approach for better-selecting patients with Crohn’s disease admitted to the ED with acute gastrointestinal complaints about abdominopelvic CT: reducing the number of CTs performed while ensuring that patients with high risk for clinically actionable findings undergo abdominopelvic CT appropriately.

Main Source:

Konikoff, Tom, Idan Goren, Marianna Yalon, Shlomit Tamir, Irit Avni-Biron, Henit Yanai, Iris Dotan, and Jacob E. Ollech. “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department.” Digestive and Liver Disease (2021). https://www.sciencedirect.com/science/article/abs/pii/S1590865821003340

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Al App for People with Digestive Disorders

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/07/06/yet-another-success-story-machine-learning-to-predict-immunotherapy-response/

Systemic Inflammatory Diseases as Crohn’s disease, Rheumatoid Arthritis and Longer Psoriasis Duration May Mean Higher CVD Risk

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/09/systemic-inflammatory-diseases-as-crohns-disease-rheumatoid-arthritis-and-longer-psoriasis-duration-may-mean-higher-cvd-risk/

Autoimmune Inflammatory Bowel Diseases: Crohn’s Disease & Ulcerative Colitis: Potential Roles for Modulation of Interleukins 17 and 23 Signaling for Therapeutics

Curators: Larry H Bernstein, MD FCAP and Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/

Inflammatory Disorders: Inflammatory Bowel Diseases (IBD) – Crohn’s and Ulcerative Colitis (UC) and Others

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/gama-delta-epsilon-gde-is-a-global-holding-company-absorbing-lpbi/subsidiary-5-joint-ventures-for-ip-development-jvip/drug-discovery-with-3d-bioprinting/ibd-inflammatory-bowl-diseases-crohns-and-ulcerative-colitis/

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WORKFLOW for a Eight-Steps Medical Text Analysis Operation using NLP on LPBI Medical and Life Sciences Content

Posted in LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, tagged , , on July 15, 2021| Leave a Comment »

WORKFLOW for a Eight-Steps Medical Text Analysis Operation using NLP on LPBI Medical and Life Sciences Content

Author: Aviva Lev-Ari, PhD, RN with inputs related to Wolfram NLP Code by Madison Davis and Yash Choudhary

  • All INTERNS will work 50% on NLP and 50% on Synthetic Biology
  • Training will be offered
  • Protocol will be developed 
  • Software applications will be selected.
  • Mid September we will have an Internal Meeting on Mission #2, LPBI India before the Meeting with Dr. Nir
  • All Interns need to complete at least ½ an e-Book Eight-Step Workflow Protocol for NLP before starting the Synthetic Biology SW Training
  • This is the Eight-Step WORKFLOW Protocol for Medical Text Analysis using NLP at LPBI:
  • https://pharmaceuticalintelligence.com/2021/07/15/workflow-for-a-ten-steps-medical-text-analysis-operation-using-nlp-on-lpbi-medical-and-life-sciences-content/
  • Each INTERN Completing the 50% assignment on NLP will need to submit this table for his/hers NLP Book Assignment with a Check off mark for each article in each Chapter in the Book the intern was assigned for
  • This Table filled in serves as INPUT for QA of the work of the INTERN. Verification is needed for Internship completion for Certification purposes
NLPStep 1Step 2Step 3Step 4Step 5Step 6Step 7Step 8
Chapter 1, Article 1          
Chapter 1, Article n          
Chapter 2 Article 1          
Chapter 2, Article n          
Chapter 3, Article 1          
Chapter 3, Article n          
Chapter 4 Article 1          
Chapter 4, Article n          
Chapter 5, Article 1          
Chapter 5, Article n          
Chapter n Article 1          
Chapter n Article n          

Table Source :

Author: Aviva Lev-Ari, PhD, RN, 7/23/2021

This Table is the supporting evidence for:

LPBI’s WORKFLOW for Medical Text Analysis Operation using NLP on

LPBI Medical and Life Sciences Content

THESE ARE THE INSTRUCTIONS FOR PERFORMANCE OF ALGORITHMS FOR NLP – INSTRUCTIONS ARE AUTHORED BY MADISON DAVIS. CODE FOR BAR DIAGRAM PLOTS, HYPERGRAPH VERSION 1 AND TREE DIAGRAM PLOTS REPRESENT CODE WRITTEN BY YASH CHOUDHARY.

STEP 1: Create Merged .TXT File of Data for Semantic Analysis

  1. Go to Wolfram Cloud and login (use old or make free account)
  2. Make a new folder: click “New” then “Folder” in the corner of your dashboard
  3. Name your folder. This is where you’ll put your text files
  4. Make a new text file: click “New” then “Text” in the corner of your dashboard
  5. Name your text file.
  6. Put this text file in the folder you made.
  7. Copy and paste the text you want to analyze in the text file. The text can be from one article or multiple articles.
  8. Verify that the text has been saved by reopening the file. Sometimes you may need to repeat the process of copying and pasting text so it works.

STEP 2: Create Presentation Slides to Copy and Paste Data, Image Graphs Into

  1. Google Slides Example: Link
  2. Google Slides Template: Link

STEP 3: View this Document for a Guide on How to Make Semantics Graphs:

  1. Link
  2. Add graphs to presentation slides
  3. The link covers the following algorithms:
    1. 25 KeyWord Extraction
    2. Hypergraphs (make one hypergraph per article collection)
    3. Tree Diagrams (make one tree diagram per article collection)
    4. Bar Diagrams

STEP 4:  Use WordItOut.com and .TXT file per article to generated One WordCloud per article

  1. Go to WorditOut.com, as seen here: https://worditout.com/
  2. Click “Create Your Own”
  3. Copy and Paste the text of the article you want into the text box
  4. Click “Generate”
  5. Change the font to “serif”, disable “random on regenerate”
  6. Change the colors based on the following guidelines, although this is just a suggestion.
  7. Change the number of colors to 2, color blending method “Direct”
  8. Save the image
  9. Upload WordClouds to the Media Gallery and record Article title as Legend and Source for the graph, add your name as image producer and date
  10. Insert World Cloud in the Article following the Author/Curator’s name
  11. Place WordCloud in a one PowerPoint Presentation for the entire Article Collection

STEP 5: Transfer Powerpoint to DropBox

STEP 6: Create a .DOCX Interpretation File of Results

  1. Domain Knowledge Expert generates a .DOCX file with his expert interpretation of all the Insights drawn from the visualization artifacts generated by NLP, ML, AI when all the insights are put together for analysis and synthesis. Store the Expert interpretation into the Interpretation Folder.
  2. What are the clinical implications for patient treatment
  3. What are the clinical insights for drug discovery for Big Pharma?
  4. Are there clues for risk adjustment and policy writing tips for health care insurers?

STEP 7:  Transfer copy of Interpretations files for Translation into Foreign Languages: Spanish, Japanese, Russian into Folders with Language Name

STEP 8: Under Construction: Enrichment of the original content with External Repositories

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