Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

International Journal of Environmental Research and Public Health

Sarah AlGhamdi¹  Jhan Alarifi¹  Ahmad AbuSalah¹  Sarab AlMuhaideb²  Hisham ElMoaqet³  Ghassan Wali⁴  Abdulrahman A. Alrajhi⁵  Osama A. Alswailem⁶  Khalid Alnafee⁷ 

[1] Center of Healthcare Intelligence, Health Information & Technology Affairs, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia;Computer Science Department, College of Computer & Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan;Department of Medicine, King Faisal Specialist Hospital & Research Centre, Jeddah 21561, Saudi Arabia;Department of Medicine, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia;Healthcare Information & Technology Affairs, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia;Infection Control & Hospital Epidemiology Department, King Faisal Specialist Hospital & Research Centre, Riyadh 11211, Saudi Arabia;

关键词: COVID-19;    severity prediction;    decision support systems;    applied artificial intelligence;    hospital operations;   

DOI  :  10.3390/ijerph19052958

来源: DOAJ

【 摘 要 】

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients’ COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation ($n=462$ patients, March 2020–April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts ($n=185$ patients, April–May 2021) showed a promising overall prediction performance with a recall of 78.4–90.0% and a precision of 75.0–97.8% for different severity classes.

【 授权许可】

Unknown