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Title: Entropy removal of medical diagnostics

Journal Article · · Scientific Reports
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [2];  [8];  [1];  [1];  [1]
  1. Massachusetts General Hospital, Boston, MA (United States); Harvard Medical School, Boston, MA (United States)
  2. Campbell University, Lillington, NC (United States)
  3. Texas A & M Univ., College Station, TX (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States)
  4. Texas A & M Univ., College Station, TX (United States)
  5. Univ. of Toronto, ON (Canada)
  6. Harvard Univ., Cambridge, MA (United States)
  7. Oregon Health & Science Univ., Portland, OR (United States)
  8. Massachusetts General Hospital, Boston, MA (United States). Institute of Health Professions
+ Show Author Affiliations

Shannon entropy is a core concept in machine learning and information theory, particularly in decision tree modeling. To date, no studies have extensively and quantitatively applied Shannon entropy in a systematic way to quantify the entropy of clinical situations using diagnostic variables (true and false positives and negatives, respectively). Decision tree representations of medical decision-making tools can be generated using diagnostic variables found in literature and entropy removal can be calculated for these tools. This concept of clinical entropy removal has significant potential for further use to bring forth healthcare innovation, such as quantifying the impact of clinical guidelines and value of care and applications to Emergency Medicine scenarios where diagnostic accuracy in a limited time window is paramount. This analysis was done for 623 diagnostic tools and provided unique insights into their utility. For studies that provided detailed data on medical decision-making algorithms, bootstrapped datasets were generated from source data to perform comprehensive machine learning analysis on these algorithms and their constituent steps, which revealed a novel and thorough evaluation of medical diagnostic algorithms.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
SC0012704
OSTI ID:
2372679
Report Number(s):
BNL--225648-2024-JAAM
Journal Information:
Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

References (22)

Entropy removal of medical diagnostics dataset January 2023
Evidence-Based Medicine journal November 1992
Diagnostic performance of chest radiography in high COVID-19 prevalence setting: experience from a European reference hospital journal July 2021
Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests journal February 2022
Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study journal October 2009
Decision Model for Acute Appendicitis Treatment With Decision Tree Technology—A Modification of the Alvarado Scoring System journal August 2010
Shannon entropy for time-varying persistence of cell migration journal June 2021
Making machine learning matter to clinicians: model actionability in medical decision-making journal January 2023
Conformational Shannon Entropy of mRNA Structures from Force Spectroscopy Measurements Predicts the Efficiency of − 1 Programmed Ribosomal Frameshift Stimulation journal January 2021
Get the Diagnosis: an evidence-based medicine collaborative Wiki for diagnostic test accuracy journal August 2016
Diagnostic Accuracy Measures journal January 2013
A method for machine learning generation of realistic synthetic datasets for validating healthcare applications journal January 2022
Generation and evaluation of synthetic patient data journal May 2020
Shannon entropy approach reveals relevant genes in Alzheimer’s disease journal December 2019
Validation of the “Step-by-Step” Approach in the Management of Young Febrile Infants journal August 2016
Entropy removal of medical diagnostics dataset January 2023
Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study journal April 2022
Correctly Using Sensitivity, Specificity, and Predictive Values in Clinical Practice: How to Avoid Three Common Pitfalls journal June 2013
Imprecise Shannon’s Entropy and Multi Attribute Decision Making journal January 2010
Real-World Data Difficulty Estimation with the Use of Entropy journal December 2021
Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value journal May 2021
Understanding and using sensitivity, specificity and predictive values journal January 2008

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