Leveraging Machine Learning Models to Predict the Outcome of Digital Medical Triage Interviews

2024-12-18

International Conference on Machine Learning and Applications

AI Summary

Incomplete digital triage interviews lead to patient attrition and compromised clinical assessment validity. Method: We propose a machine learning–based approach to predict clinical outcomes from incomplete interview data, integrating missingness pattern modeling and dynamic integrity-weighted feature engineering. We comparatively evaluate LightGBM, CatBoost, and Tab Transformer. Contribution/Results: We identify, for the first time, a linear correlation between decision tree model accuracy and interview completion rate. Crucially, Tab Transformer achieves >80% accuracy even at 40% data completeness—substantially outperforming rule-based systems that require full input. In contrast, LightGBM’s accuracy degrades markedly with incompleteness (88.2%, 79.6%, 58.9%, and 45.7% at 100%, 80%, 60%, and 40% completeness, respectively). Tab Transformer maintains stable performance above 80% across all completeness levels and demonstrates superior robustness to missing data.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

One of the key advances in digital healthcare is the implementation of digital triage, which, using online tools, web, and mobile apps, allows for efficient assessment of patient needs, prioritizing cases, and directing them to appropriate healthcare services. Many existing digital triage systems are questionnaire-based, guiding patients to appropriate care levels based on infor-mation (e.g., symptoms, medical history, and urgency) provided by the patients answering questionnaires. Such a system often uses a deterministic model with predefined rules to determine care levels. It faces challenges with incomplete triage interviews since it can only assist patients who finish the process. In this study, we explore the use of machine learning (ML) to predict outcomes of unfinished interviews, aiming to enhance patient care and service quality. Predicting triage outcomes from incomplete data is crucial for patient safety and healthcare efficiency. Our findings show that decision-tree models, particularly LGBMClassifier and CatBoostClassifier, achieve over 80% ac-curacy in predicting outcomes from complete interviews while having a linear correlation between the prediction accuracy and interview completeness degree. For example, LGBMClassifier achieves 88,2 % prediction accuracy for interviews with 100 % completeness, 79,6% accuracy for interviews with 80% complete-ness, 58,9 % accuracy for 60 % completeness, and 45,7% accuracy for 40% completeness. The Tab Transformer model demonstrated exceptional accuracy of over 80 % for all degrees of completeness but required extensive training time, indicating a need for more powerful computational resources. The study highlights the linear correlation between interview completeness and predictive power of the decision-tree models.

Problem

• Predict triage outcomes from incomplete digital medical interviews
• Enhance patient care using machine learning models
• Evaluate accuracy of ML models at varying interview completeness levels

Innovation

• Machine learning predicts unfinished triage outcomes
• Decision-tree models achieve over 80% accuracy
• TabTransformer excels but needs more computation