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

Autor: Datta D; Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA., Ray S; Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA., Martinez L; Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA., Newman D; Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA., Dalmida SG; Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL 33431, USA., Hashemi J; College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA., Sareli C; Memorial Healthcare System, Hollywood, FL 33021, USA., Eckardt P; Memorial Healthcare System, Hollywood, FL 33021, USA.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 Aug 26; Vol. 14 (17). Date of Electronic Publication: 2024 Aug 26.
DOI: 10.3390/diagnostics14171866
Abstrakt: 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 I ntensive C are U nit (ICU) with M echanical V entilation (MV) requirement, ICU, and I nter M ediate C are U nit (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.
Databáze: MEDLINE
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