Evidence map›Paper›PMID 33127965›Full record

ArticleScientific reports2020

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

Chansik An, Hyunsun Lim, Dong-Wook Kim, Jung Hyun Chang, Yoon Jung Choi, Seong Woo Kim

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
91citing papers in PubMed, 17 pooled it
18.6field-weighted citation impact, top 1% of its field
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

91 citing papers in PubMed, 17 syntheses or guidelines pooled it, 210 citations in OpenAlex.

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  15. Statin and outcomes of coronavirus disease 2019 (COVID-19): A systematic review, meta-analysis, and meta-regression.Nutrition, metabolism, and cardiovascular diseases : NMCD · 2021 · on this map
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31 more citing papers are in PubMed but not listed here.

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

6 authors at 2 institutions in 1 country.

Chansik AnResearch Institute, National Health Insurance Service Ilsan Hospital, Goyang, Korea.
Hyunsun LimResearch Institute, National Health Insurance Service Ilsan Hospital, Goyang, Korea.
Dong-Wook KimDepartment of Big Data, National Health Insurance Service, Wonju, Korea.
Jung Hyun ChangResearch Institute, National Health Insurance Service Ilsan Hospital, Goyang, Korea.
Yoon Jung ChoiResearch Institute, National Health Insurance Service Ilsan Hospital, Goyang, Korea. chris316@yuhs.ac.
Seong Woo KimDepartment of Physical Medicine and Rehabilitation, National Health Insurance Service Ilsan Hospital, Goyang, Korea.
National Health Insurance Service · KRNational Health Insurance Service Ilsan Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

Indexed as

Machine LearningAdultAgedAged, 80 and overCoronavirus InfectionsCOVID-19FemaleHumansMaleMiddle AgedModels, StatisticalMortalityPandemicsPneumonia, ViralRepublic of Korea

Identifiers

PMID33127965
PMCPMC7599238
OpenAlexW3097787292

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.