Machine learning approaches to identify rare fungal and tuberculous infections in hospitalized patients

Diagnosing atypical pathogen-induced infectious diseases is a complex and challenging problem, given their unique characteristics. A-priori identification of features using modern AI approaches can facilitate the diagnostic process, potentially leading to improved outcomes, early diagnosis, and effective resource utilization. We aimed to develop an AI-driven clinical decision support system for identifying rare and atypical infections - a composite of six microbiologically and clinically distinct diseases (blastomycosis, cryptococcosis, histoplasmosis, mucormycosis, pneumocystosis, and tuberculosis) unified by their shared pattern of diagnostic delay - in the early stages of hospital admission, which are often missed due to nonspecific symptoms, anchoring bias, and low clinical suspicion, leading to delays of 20-30 days.

We analyzed EHR data from 11,494 patients (1,494 with rare infections, 10,000 controls) admitted to Mayo Clinic hospitals (2010-2023). Machine learning models, including HistGradientBoosting, were trained on demographics, medical history, and laboratory results within three days of admission. SMOTE addressed class imbalance, and SHAP values provided interpretability. Performance was evaluated using accuracy, sensitivity, specificity, and AUC.

The HistGradientBoosting model achieved an accuracy of 93.2 %, AUC of 0.947 (IQR 0.942-0.952), s pecificity of 97.3 %, and sensitivity of 61.9 % at the model's default classification threshold; threshold optimization for the intended flagging use case is planned prior to implementation. Key predictors included elevated phosphate, decreased lymphocyte counts, male sex, immunodeficiency, age, and calcium levels, aligning with clinical markers.

The AI-driven clinical decision support system developed in this study shows strong potential for aiding the early diagnosis of rare infections. By leveraging EHR data and advanced machine learning models, this system could improve resource utilization, enable timely clinical interventions, and ultimately enhance patient outcomes. Further validation in external datasets and prospective trials is needed to confirm these findings and facilitate real-world implementation.