Evidence map›Paper›PMID 38216607›Full record

ArticleScientific reports2024

Entropy removal of medical diagnostics.

Shuhan He, Paul Chong, Byung-Jun Yoon, Pei-Hung Chung, David Chen, Sammer Marzouk, Kameron C Black, Wilson Sharp, Pedram Safari, Joshua N Goldstein and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Entropy removal of clinical features.Scientific reports · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Shuhan He *Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. she@mgh.harvard.edu.
Paul Chong *Campbell University School of Osteopathic Medicine, Lillington, NC, USA.
Byung-Jun YoonDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
Pei-Hung ChungDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
David ChenTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Sammer MarzoukHarvard University Department of Chemistry and Chemical Biology, Cambridge, MA, USA.
Kameron C BlackOregon Health and Science University, Portland, OR, USA.
Wilson SharpCampbell University School of Osteopathic Medicine, Lillington, NC, USA.
Pedram SafariMassachusetts General Hospital Institute of Health Professions, Boston, MA, USA.
Joshua N GoldsteinMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Ali S RajaMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Jarone LeeMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AlgorithmsClinical Decision-MakingEntropyInformation TheoryMachine Learning

Identifiers

PMID38216607
PMCPMC10786933

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.