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Article

Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida

1
Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA
2
College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
3
Memorial Healthcare System, Hollywood, FL 33021, USA
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(17), 1866; https://doi.org/10.3390/diagnostics14171866
Submission received: 12 July 2024 / Revised: 16 August 2024 / Accepted: 21 August 2024 / Published: 26 August 2024
(This article belongs to the Special Issue Pulmonary Disease: Diagnosis and Management)

Abstract

Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients’ data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years (‘older adults’), males, current smokers, and BMI classified as ‘overweight’ and ‘obese’ were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models’ interpretability were from the ‘sociodemographic characteristics’, ‘pre-hospital comorbidities’, and ‘medications’ categories. However, ‘pre-hospital comorbidities’ played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients’ conditions when urgent treatment plans are needed during the surge of patients during the pandemic.
Keywords: COVID-19 predictive model; random forest classifier; SHAP; Gini index; permutation-based interpretation; caring data science COVID-19 predictive model; random forest classifier; SHAP; Gini index; permutation-based interpretation; caring data science

Share and Cite

MDPI and ACS Style

Datta, D.; Ray, S.; Martinez, L.; Newman, D.; Dalmida, S.G.; Hashemi, J.; Sareli, C.; Eckardt, P. Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics 2024, 14, 1866. https://doi.org/10.3390/diagnostics14171866

AMA Style

Datta D, Ray S, Martinez L, Newman D, Dalmida SG, Hashemi J, Sareli C, Eckardt P. Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics. 2024; 14(17):1866. https://doi.org/10.3390/diagnostics14171866

Chicago/Turabian Style

Datta, Debarshi, Subhosit Ray, Laurie Martinez, David Newman, Safiya George Dalmida, Javad Hashemi, Candice Sareli, and Paula Eckardt. 2024. "Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida" Diagnostics 14, no. 17: 1866. https://doi.org/10.3390/diagnostics14171866

APA Style

Datta, D., Ray, S., Martinez, L., Newman, D., Dalmida, S. G., Hashemi, J., Sareli, C., & Eckardt, P. (2024). Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida. Diagnostics, 14(17), 1866. https://doi.org/10.3390/diagnostics14171866

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