Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

Abstract

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

XAI-based Clinical Decision Support System: A Systematic Review

“…In the second layer, the binary classification score is were used to determine the transition from cognitive impairment to AD [80]. Similarly, SHAP has been widely used in various diseases and clinical domains such as predicting readmission [81,82], COVID-19 [83][84][85][86], liver cancer [87], influenza [88], and malignant cerebral edema [89].…”

A Bridge to Machine Learning for Zero Computer Science Community: A Machine Learning Path with Linear Regression

“…Among many ML budget applications available in the literature, the latest involved the works of researchers who used ML for budget-related applications in the COVID-19 pandemic. For instance, (Nguyen et al, 2021) leveraged electronic health record (HER) data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. A large and growing body of literature has investigated the use of ML in price predictions.…”

Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip

“…AI technologies that emphasize development of learning models while taking cost constraints into account are loosely called “budget-sensitive” learning models. 8,9 While all fields of medicine can benefit from such approaches, it can be argued that point-of-care diagnostics (POC), which highly values cost-effectiveness, tends to benefit the most. Furthermore, a budget sensitive model synergizes well with the more recent wave of POC diagnostics that have incorporated more “smart” elements in the assay workflow.…”