Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients

• Published in: International journal of environmental research and public health 2021-06, Vol.18 (12), p.6228
• Main Authors: Vepa, Abhinav, Saleem, Amer, Rakhshan, Kambiz, Daneshkhah, Alireza, Sedighi, Tabassom, Shohaimi, Shamarina, Omar, Amr, Salari, Nader, Chatrabgoun, Omid, Dharmaraj, Diana, Sami, Junaid, Parekh, Shital, Ibrahim, Mohamed, Raza, Mohammed, Kapila, Poonam, Chakrabarti, Prithwiraj
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Chronic obstructive pulmonary disease; Clinical decision making; Coronaviruses; COVID-19; Data collection; Decision making; Embolism; Independent variables; Learning algorithms; Machine learning; Mathematical models; Medical imaging; Medical prognosis; Mortality; Patients; Physicians; Prediction models; Random variables; Risk analysis; Risk factors; Statistical analysis
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph18126228

Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.