An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients

Sinha, Abhishar and Joshi, Swati Purohit and Das, Purnendu Sekhar and Jana, Soumya and et al, . (2022) An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients. Scientific Reports, 12 (1). pp. 1-9. ISSN 2045-2322

[img] Text
Scientific_Reports.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB)


Outcome prediction for individual patient groups is of paramount importance in terms of selection of appropriate therapeutic options, risk communication to patients and families, and allocating resource through optimum triage. This has become even more necessary in the context of the current COVID-19 pandemic. Widening the spectrum of predictor variables by including radiological parameters alongside the usually utilized demographic, clinical and biochemical ones can facilitate building a comprehensive prediction model. Automation has the potential to build such models with applications to time-critical environments so that a clinician will be able to utilize the model outcomes in real-time decision making at bedside. We show that amalgamation of computed tomogram (CT) data with clinical parameters (CP) in generating a Machine Learning model from 302 COVID-19 patients presenting to an acute care hospital in India could prognosticate the need for invasive mechanical ventilation. Models developed from CP alone, CP and radiologist derived CT severity score and CP with automated lesion-to-lung ratio had AUC of 0.87 (95% CI 0.85–0.88), 0.89 (95% CI 0.87–0.91), and 0.91 (95% CI 0.89–0.93), respectively. We show that an operating point on the ROC can be chosen to aid clinicians in risk characterization according to the resource availability and ethical considerations. This approach can be deployed in more general settings, with appropriate calibrations, to predict outcomes of severe COVID-19 patients effectively. © 2022, The Author(s).

[error in script]
IITH Creators:
IITH CreatorsORCiD
Jana, Soumya
Item Type: Article
Uncontrolled Keywords: appropriate therapeutic options, risk communication, radiological parameters, utilized demographic, clinical and biochemical
Subjects: Others > Medicine
Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 18 Jul 2022 07:22
Last Modified: 18 Jul 2022 07:22
Publisher URL:
OA policy:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 9754 Statistics for this ePrint Item