Backgrounds: Diagnostic prediction is a central application that spans various medical specialties and scenarios, sequential diagnosis prediction is the process of predicting future diagnoses based on patients' historical visits. Prior research has underexplored the impact of irregular intervals between patient visits on predictive models, despite its significance.
Method: We developed the Multi-task Fusion Visit Interval for Sequential Diagnosis Prediction (MISDP) framework to address this research gap. The MISDP framework integrated sequential diagnosis prediction with visit interval prediction within a multi-task learning paradigm. It uses positional encoding and interval encoding to handle irregular patient visit intervals. Furthermore, it incorporates historical attention residue to enhance the multi-head self-attention mechanism, focusing on extracting long-term dependencies from clinical historical visits.
Results: The MISDP model exhibited superior performance across real-world healthcare dataset, irrespective of the training data scarcity or abundance. With only 20% training data, MISDP achieved a 4. 2% improvement over KAME; when training data ranged from 60 to 80%, MISDP surpassed SETOR, the top baseline, by 0. 8% in accuracy, underscoring its robustness and efficacy in sequential diagnosis prediction task.
Conclusions: The MISDP model significantly improves the accuracy of Sequential Diagnosis Prediction. The result highlights the advantage of multi-task learning in synergistically enhancing the performance of individual sub-task. Notably, irregular visit interval factors and historical attention residue has been particularly instrumental in refining the precision of sequential diagnosis prediction, suggesting a promising avenue for advancing clinical decision-making through data-driven modeling approaches.
Keywords: Historical attention residue; Irregular visit intervals; Multi-task learning; Sequential diagnosis prediction.
© 2024. The Author(s).