Diagnosing critical conditions like Acute Liver Failure (ALF), Methanol Toxicity (MT), Alcohol Poisoning (AP), and Diabetic Ketoacidosis (DKA) is difficult due to similar symptoms and complex interdependent metabolism, often resulting in delayed and incorrect diagnoses in historic clinical practice. We present a hybrid machine learning framework integrating multilabel classification and association rule learning that provides better precision in diagnostics and uncovers complex interrelated conditions. Our methodology uses a Random Forest-based Multi-Output Classifier for multilabel classification, which demonstrates an 18% improvement on the accuracy of traditional single-label-based diagnoses and employs the Apriori Algorithm to find significant co-occurrence, finding that Alcohol Poisoning is linked to Acute Liver Failure with 82% confidence. We assessed our models on a heterogeneous dataset of 10,487 patient cases from Electronic Health Records (EHRs) from 2018-2023. The models developed perform well with LightGBM and XGBoost, providing accuracies of 85.2% and 84.7%, respectively, and validated on a subsequent dataset from EHRs from 2023-2024. As part of a Clinical Decision Support System (CDSS) prototype, the framework provides real-time and interpretable diagnostic support by using SHAP explanations and complies with HIPAA and FDA standards while providing a scalable risk assessment tool to improve patient safety and outcomes in critical care.
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