tetano
Editor, Senior Moderator
PLoS One
. 2024 Jan 25;19(1):e0294362.
doi: 10.1371/journal.pone.0294362. eCollection 2024. In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records
Hyungjun Park[SUP] 1 [/SUP], Chang-Min Choi[SUP] 2 3 [/SUP], Sung-Hoon Kim[SUP] 4 [/SUP], Su Hwan Kim[SUP] 5 6 [/SUP], Deog Kyoem Kim[SUP] 5 7 [/SUP], Ji Bong Jeong[SUP] 5 6 [/SUP]
Affiliations
Coronavirus disease 2019 (COVID-19) has strained healthcare systems worldwide. Predicting COVID-19 severity could optimize resource allocation, like oxygen devices and intensive care. If machine learning model could forecast the severity of COVID-19 patients, hospital resource allocation would be more comfortable. This study evaluated machine learning models using electronic records from 3,996 COVID-19 patients to forecast mild, moderate, or severe disease up to 2 days in advance. A deep neural network (DNN) model achieved 91.8% accuracy, 0.96 AUROC, and 0.90 AUPRC for 2-day predictions, regardless of disease phase. Tree-based models like random forest achieved slightly better metrics (random forest: 94.1% of accuracy, 0.98 AUROC, 0.95 AUPRC; Gradient boost: 94.1% of accuracy, 0.98 AUROC, 0.94 AUPRC), prioritizing treatment factors like steroid use. However, the DNN relied more on fixed patient factors like demographics and symptoms in aspect to SHAP value importance. Since treatment patterns vary between hospitals, the DNN may be more generalizable than tree-based models (random forest, gradient boost model). The results demonstrate accurate short-term forecasting of COVID-19 severity using routine clinical data. DNN models may balance predictive performance and generalizability better than other methods. Severity predictions by machine learning model could facilitate resource planning, like ICU arrangement and oxygen devices.
. 2024 Jan 25;19(1):e0294362.
doi: 10.1371/journal.pone.0294362. eCollection 2024. In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records
Hyungjun Park[SUP] 1 [/SUP], Chang-Min Choi[SUP] 2 3 [/SUP], Sung-Hoon Kim[SUP] 4 [/SUP], Su Hwan Kim[SUP] 5 6 [/SUP], Deog Kyoem Kim[SUP] 5 7 [/SUP], Ji Bong Jeong[SUP] 5 6 [/SUP]
Affiliations
- PMID: 38271404
- DOI: 10.1371/journal.pone.0294362
Coronavirus disease 2019 (COVID-19) has strained healthcare systems worldwide. Predicting COVID-19 severity could optimize resource allocation, like oxygen devices and intensive care. If machine learning model could forecast the severity of COVID-19 patients, hospital resource allocation would be more comfortable. This study evaluated machine learning models using electronic records from 3,996 COVID-19 patients to forecast mild, moderate, or severe disease up to 2 days in advance. A deep neural network (DNN) model achieved 91.8% accuracy, 0.96 AUROC, and 0.90 AUPRC for 2-day predictions, regardless of disease phase. Tree-based models like random forest achieved slightly better metrics (random forest: 94.1% of accuracy, 0.98 AUROC, 0.95 AUPRC; Gradient boost: 94.1% of accuracy, 0.98 AUROC, 0.94 AUPRC), prioritizing treatment factors like steroid use. However, the DNN relied more on fixed patient factors like demographics and symptoms in aspect to SHAP value importance. Since treatment patterns vary between hospitals, the DNN may be more generalizable than tree-based models (random forest, gradient boost model). The results demonstrate accurate short-term forecasting of COVID-19 severity using routine clinical data. DNN models may balance predictive performance and generalizability better than other methods. Severity predictions by machine learning model could facilitate resource planning, like ICU arrangement and oxygen devices.