ArticleJournal of Korean medical science2025
Developing a Prediction Model for Suicidality Among COVID-19 Patients in Korea Using Timely Data From the National Center for Disaster and Trauma.
Article in Journal of Korean medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Mental Health Outcomes After the Acute Phase of COVID-19: Factors Identified in a Korean Public Mental Health Service.Journal of Korean medical science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundTo date, no study has developed a predictive model based on the timely data (diagnosed within a month) of patients with coronavirus disease 2019 (COVID-19). This study aimed to develop screening tools and a model for predicting suicidality based on data from patients diagnosed with COVID-19 within the past month.
methodsWe analyzed data from 96,694 COVID-19 patients collected by the Korean National Center for Disaster and Trauma. Using classification and regression tree (CART) and random forest models, we predicted suicidality based on 39 features, including demographic information, COVID-19-related factors, and psychological symptoms such as depression, anxiety, somatic, and post-traumatic stress symptoms. Based on the final model with the highest performance index, we presented the results with a scorecard that includes multiple cut-off scores.
resultsThe area under curve was 0.71 for the CART model and 0.85 for the random forest model, with the sensitivity and specificity of the random forest model being 73.4% and 83.9%, respectively. The suicidality of COVID-19 patients was most strongly associated with suicidal ideation, fatigue, anticipatory anxiety, uncontrollable worrying, depressive mood, generalized worry, restlessness, changes in appetite, concentration problems, and feeling nervous.
conclusionWe developed a well-performing prediction model for suicidality among Korean COVID-19 patients and developed a scorecard to expand the feasibility of the model in public mental health practice.
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.