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MISDP: multi-task fusion visit interval for sequential diagnosis prediction.

Shengrong Zhu¹, Ruijia Yang², Zifeng Pan²

¹Department of Information Management and Big Data Center, Peking University Third Hospital, Beijing, 100191, China.

BMC Bioinformatics

|December 20, 2024

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The Multi-task Fusion Visit Interval for Sequential Diagnosis Prediction (MISDP) model enhances diagnostic accuracy by accounting for irregular patient visit intervals. This approach improves predictions, especially with limited data, advancing clinical decision-making.

Area of Science:

Computational medicine
Health informatics
Machine learning in healthcare

Background:

Sequential diagnosis prediction is crucial across medical specialties.
Prior research has overlooked the impact of irregular patient visit intervals.
Addressing this gap is vital for improving predictive model accuracy.

Purpose of the Study:

To develop a novel framework for sequential diagnosis prediction that accounts for irregular visit intervals.
To enhance the accuracy and robustness of diagnostic prediction models.

Main Methods:

Developed the Multi-task Fusion Visit Interval for Sequential Diagnosis Prediction (MISDP) framework.
Integrated sequential diagnosis and visit interval prediction using multi-task learning.
Employed positional encoding, interval encoding, and historical attention residue to handle irregular intervals and capture long-term dependencies.

Keywords:

Historical attention residue Irregular visit intervals Multi-task learning Sequential diagnosis prediction

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Main Results:

MISDP demonstrated superior performance on real-world healthcare datasets.
Achieved a 4.2% improvement over KAME with only 20% training data.
Outperformed the top baseline SETOR by 0.8% accuracy with 60-80% training data, showing robustness.

Conclusions:

MISDP significantly improves sequential diagnosis prediction accuracy.
Multi-task learning synergistically enhances individual sub-task performance.
Irregular visit intervals and historical attention residue are key factors in refining prediction precision.