Analyzing Impact of Socio-Economic Factors on COVID-19 Mortality Prediction Using SHAP Value.

Redoan Rahman, Jooyeong Kang, Justin F Rousseau, Ying Ding

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.BMC infectious diseases · 2025
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

4 authors.

Redoan RahmanSchool of Information, The University of Texas at Austin, Austin, Texas, United States.

Jooyeong KangSchool of Information, The University of Texas at Austin, Austin, Texas, United States.

Justin F RousseauDell Medical School, The University of Texas at Austin, Austin, Texas, United States.

Ying DingSchool of Information, The University of Texas at Austin, Austin, Texas, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database. The dataset consists of 20,878 COVID-positive patients, among which 9,177 patients died in the year 2020. This paper aims to understand and interpret the association of socio-economic characteristics of patients with their mortality instead of maximizing prediction accuracy. According to our analysis, a patient's household's annual and disposable income, age, education, and employment status significantly impacts a machine learning model's prediction. We also observe several individual patient data, which gives us insight into how the feature values impact the prediction for that data point. This paper analyzes the global and local interpretation of machine learning models on socio-economic data of COVID patients.

Indexed as

COVID-19AlgorithmsDatabases, FactualEconomic FactorsEducational StatusHumans

Identifiers

PMID37128465

PMCPMC10148315

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.