ADVANCING BLOOD SAMPLE ANALYSIS: INCORPORATING EXPERT OPINIONS AND EXPLAINABLE AI IN MULTI-LABEL DISEASE PREDICTION - Akanova, Inabat; Turmakhan, Diana; Beken, Ulpan; Serikkazhy, Islam; Orynbay, Sultan (Nazarbayev University School Engineering and Digital Sciences, 2024)
Blood sample analysis plays a crucial role in modern medical practice, aiding in the detection of a wide array of diseases. Despite its significance, the potential of blood samples for predicting various diseases has remained largely unexplored. Our project aimed to dive into evaluate the efficacy of blood samples in predicting a broad spectrum of disease using large-scale MIMIC III medical dataset. Given the sparse nature of the data, we combine imputation with multi-task models for which we identify and utilize meaningful auxiliary tasks and are thus able to reach an average state-of-the-art ROC-AUC score of 81% across the 50 most prevalent diseases within the dataset. To further validate our findings, we sought the expertise of five medical doctors, who independently rated the predictability of these diseases from blood samples. Spearman’s rho analysis revealed a substantial agreement ( = 0.61) between the doctors’ ratings and the actual ROCAUC values of our machine learning models. In order to add transparency and reliability, we employed the Local Interpretable Modelagnostic Explanations (LIME) method to identify the most predictive blood sample features. These findings were rigorously cross-checked with medical experts, affirming the robustness and credibility of our predictive models. Our study represents a significant advancement in the field of medical diagnostics, showcasing the untapped potential of blood sample analysis in disease prediction. By integrating cuttingedge machine learning techniques with expert validation, we pave the way for enhanced patient care and improved healthcare outcomes.
Keywords: blood samples; multi-label classification; multi-task learning; explainable AI; imputation; expert opinion; Type of access: Restricted