ArticlePeerJ2020
Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data.Clinics (Sao Paulo, Brazil) · 2023Trial
- Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA.BMJ open · 2021Trial
- A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes.International journal of clinical pharmacy · 2025Article
- Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort.Genomics & informatics · 2025Article
- Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients.Scientific reports · 2025Article
- Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil.PeerJ. Computer science · 2025Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.Scientific reports · 2023Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.Scientific reports · 2023Article
- Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice.Frontiers in digital health · 2023Review
- Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning.Global health, epidemiology and genomics · 2023Article
- Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases.Frontiers in medicine · 2023Article
- Health Promotion and Racial Disparity in COVID-19 Mortality Among African American Populations.Reports on global health research · 2023Article
- Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19.Diagnostics (Basel, Switzerland) · 2022Article
- Spatial-temporal generalized additive model for modeling COVID-19 mortality risk in Toronto, Canada.Spatial statistics · 2022Article
- A COVID-19 mortality prediction model for Korean patients using nationwide Korean disease control and prevention agency database.Scientific reports · 2022Article
- Prognosis of COVID-19 pneumonia can be early predicted combining Age-adjusted Charlson Comorbidity Index, CRB score and baseline oxygen saturation.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe recent pandemic of CoVID-19 has emerged as a threat to global health security. There are very few prognostic models on CoVID-19 using machine learning.
objectivesTo predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. MATERIALS AND
methodsMortality for confirmed CoVID-19 patients (
resultsThe logistic regression algorithm was the best performer in terms of discrimination (area under ROC curve = 0.830), calibration (Matthews Correlation Coefficient = 0.433; Brier Score = 0.036) and. The best performing algorithm (logistic regression) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/).
conclusionsWe describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policymakers to triage patients at the community level in addition to other approaches.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.