Kavli Affiliate: Ting Xu
| First 5 Authors: Ting Xu, Ting Xu, , ,
| Summary:
This paper addresses the challenges posed by the unstructured nature and
high-dimensional semantic complexity of electronic health record texts. A deep
learning method based on attention mechanisms is proposed to achieve unified
modeling for information extraction and multi-label disease prediction. The
study is conducted on the MIMIC-IV dataset. A Transformer-based architecture is
used to perform representation learning over clinical text. Multi-layer
self-attention mechanisms are employed to capture key medical entities and
their contextual relationships. A Sigmoid-based multi-label classifier is then
applied to predict multiple disease labels. The model incorporates a
context-aware semantic alignment mechanism, enhancing its representational
capacity in typical medical scenarios such as label co-occurrence and sparse
information. To comprehensively evaluate model performance, a series of
experiments were conducted, including baseline comparisons, hyperparameter
sensitivity analysis, data perturbation studies, and noise injection tests.
Results demonstrate that the proposed method consistently outperforms
representative existing approaches across multiple performance metrics. The
model maintains strong generalization under varying data scales, interference
levels, and model depth configurations. The framework developed in this study
offers an efficient algorithmic foundation for processing real-world clinical
texts and presents practical significance for multi-label medical text modeling
tasks.
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