IEEE transactions on bio-medical engineering · 2024
DuKA: A Dual-Keyless-Attention Model for Multi-Modality EHR Data Fusion and Organ Failure Prediction.
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Liu Zhangdaihong, Wu Xuan, Yang Yang, Clifton David A
Аннотация
OBJECTIVE: Organ failure is a leading cause of mortality in hospitals, particularly in intensive care units. Predicting organ failure is crucial for clinical and social reasons. This study proposes a dual-keyless-attention (DuKA) model that enables interpretable predictions of organ failure using electronic health record (EHR) data.
METHODS: Three modalities of medical data from EHR, namely diagnosis, procedure, and medications, are selected to predict three types of vital organ failures: heart failure, respiratory failure, and kidney failure. DuKA utilizes pre-trained embeddings of medical codes and combines them using a modality-wise attention module and a medical concept-wise attention module to enhance interpretation. Three organ failure tasks are addressed using two datasets to verify the effectiveness of DuKA.
RESULTS: The proposed multi-modality DuKA model outperforms all reference and baseline models. The diagnosis history, particularly the presence of cachexia and previous organ failure, emerges as the most influential feature in organ failure prediction.
CONCLUSIONS: DuKA offers competitive performance, straightforward model interpretations and flexibility in terms of input sources, as the input embeddings can be trained using different datasets and methods.
SIGNIFICANCE: DuKA is a lightweight model that innovatively uses dual attention in a hierarchical way to fuse diagnosis, procedure and medication information for organ failure predictions. It also enhances disease comprehension and supports personalized treatment.
METHODS: Three modalities of medical data from EHR, namely diagnosis, procedure, and medications, are selected to predict three types of vital organ failures: heart failure, respiratory failure, and kidney failure. DuKA utilizes pre-trained embeddings of medical codes and combines them using a modality-wise attention module and a medical concept-wise attention module to enhance interpretation. Three organ failure tasks are addressed using two datasets to verify the effectiveness of DuKA.
RESULTS: The proposed multi-modality DuKA model outperforms all reference and baseline models. The diagnosis history, particularly the presence of cachexia and previous organ failure, emerges as the most influential feature in organ failure prediction.
CONCLUSIONS: DuKA offers competitive performance, straightforward model interpretations and flexibility in terms of input sources, as the input embeddings can be trained using different datasets and methods.
SIGNIFICANCE: DuKA is a lightweight model that innovatively uses dual attention in a hierarchical way to fuse diagnosis, procedure and medication information for organ failure predictions. It also enhances disease comprehension and supports personalized treatment.