Evidence map›Paper›PMID 36755135›Full record

ArticleScientific reports2023

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.

Hannah Paris Cowley, Michael S Robinette, Jordan K Matelsky, Daniel Xenes, Aparajita Kashyap, Nabeela F Ibrahim, Matthew L Robinson, Scott Zeger, Brian T Garibaldi, William Gray-Roncal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. 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. 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

10 authors.

Hannah Paris CowleyResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. Hannah.Cowley@jhuapl.edu.
Michael S RobinetteResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Jordan K MatelskyResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Daniel XenesResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Aparajita KashyapResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Nabeela F IbrahimResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Matthew L RobinsonThe Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Scott ZegerThe Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
Brian T GaribaldiThe Johns Hopkins University School of Medicine, Baltimore, MD, USA.
William Gray-RoncalResearch and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA. William.Gray.Roncal@jhuapl.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.

Indexed as

COVID-19Big DataHumansMachine LearningPandemicsPatients

Identifiers

PMID36755135
PMCPMC9906583

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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.