Feeds:
Posts
Comments

Posts Tagged ‘Machine Learning (ML)’


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

Author: Aviva Lev-Ari, PhD, RN

  • 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 Ten-Step Workflow Protocol for NLP before starting the Synthetic Biology SW Training
  • This is the Ten-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 8Step 9Step 10
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 a Ten-Steps Medical Text Analysis Operation using NLP on

LPBI Medical and Life Sciences Content

STEP 1: Domain Knowledge Expert Specifies the selection criteria for a Collection of Articles:

  1. Curated & authored articles vs scientific reports
  2. All articles in a Chapter in a book, [N = 1,2,3,  ..,18]
  3. Selection of articles within a Research Category [N = 1,2,3,  ..,730]
  4. Selection of articles within several Research Categories

STEP 2:    Create .TXT file for each article in the collection

STEP 3:    Create one MERGED .TXT File for all the articles in the collection

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

4.1    Edit Graph – remove connective words

4.2    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

4.3    Insert World Cloud in the Article following the Author/Curator’s name

4.4    Place WordCloud in a one PowerPoint Presentation for the entire Article Collection

STEP 5:    Use .TXT file per article to create a Bar Diagram for the word frequencies in the article

5.1    Edit Bar Diagram and remove connective words

5.2    Place each Bar Diagram in the PowerPoint Presentation for the article collection

STEP 6:    Use the one MERGED .TXT file to create ONE Hyper-graph for the entire article collection

6.1    Edit Hyper-graph

6.2    Place Hyper-graph in the PowerPoint presentation

STEP 7:    Use the one MERGED .TXT file to create ONE Tree Diagram for the entire article collection

6.1    Edit Tree Diagram

6.2    Place Tree Diagram in the PowerPoint presentation

STEP 8:    Transfer all visualization in PowerPoint into a Domain Knowledge Expert Interpretation Folder

STEP 9:    The highest value added step:

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.

9.1    What are the clinical implications for patient treatment

9.2    What are the clinical insights for drug discovery for Big Pharma?

9.4    Are there clues for risk adjustment and policy writing tips for health care insurers?

9.5    Store the Expert interpretation into the Interpretation Folder

STEP 10:  Transfer copy of Interpretations files for Translation into Foreign Languages: Spanish, Japanese, Russian into Folders with Language Name

STEP 11: Under Construction: Enrichment of the original content with External Repositories

Read Full Post »