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A Novel Explainable AI Method to Assess Associations between Temporal Patterns in Patient Trajectories and Adverse
Yijun Shao1,2, Edward Y Zamrini1,2,3,4, Ali Ahmed1,2,5
1George Washington University, Washington, DC, USA.
We developed a new explainable AI method to link patient health data patterns to disease risks. Higher cardiorespiratory fitness (CRF) levels and improving CRF trends significantly reduce Alzheimer's disease and related dementias risk.
Area of Science:
- Artificial Intelligence
- Medical Informatics
- Biostatistics
Background:
- Longitudinal clinical data offers insights into patient health trajectories.
- Predicting adverse outcomes from complex temporal patterns remains challenging.
- Existing models often lack transparency in their predictions.
Conclusions:
- The novel XAI method effectively links temporal health data patterns to disease risks.
- Cardiorespiratory fitness is a significant protective factor against Alzheimer's disease and related dementias.
- The interpretability of HVAT models is enhanced, aiding clinical data analysis and risk prediction.
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