A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are often not integrated into predictive models. In this paper, we provide a novel multimodal transformer to fuse clinical notes and structured EHR data for better prediction of in-hospital mortality. To improve interpretability, we propose an integrated gradients (IG) method to select important words in clinical notes and discover the critical structured EHR features with Shapley values. These important words and clinical features are visualized to assist with interpretation of the prediction outcomes. We also investigate the significance of domain adaptive pretraining and task adaptive fine-tuning on the Clinical BERT, which is used to learn the representations of clinical notes. Experiments demonstrated that our model outperforms other methods (AUCPR: 0.538, AUCROC: 0.877, F1:0.490).
Code (0)
등록된 구현이 없습니다.
Tasks
Mortality PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
On Preserving the Knowledge of Long Clinical Texts
Clinical texts, such as admission notes, discharge summaries, and progress notes, contain rich and valuable information that can be used for clinical decision making. However, a severe bottleneck in using transformer enc…
Decision MakingEnsemble LearningLength-of-Stay predictionMortality Prediction+1MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use complementary multimodal EH…
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPatient Trajectory Prediction: Integrating Clinical Notes with Transformers
Predicting disease trajectories from electronic health records (EHRs) is a complex task due to major challenges such as data non-stationarity, high granularity of medical codes, and integration of multimodal data. EHRs c…
ClassificationDiagnosticDisease PredictionNatural Language Inference+2Multimodal Deep Learning for Early Prediction of Patient Deterioration in the ICU: Integrating Time-Series EHR Data with Clinical Notes
Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU) remains a critical challenge. Delayed recognition of impending adverse events, including mortality, vasopressor initiat…
Multimodal Deep LearningClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensio…
Readmission Prediction