Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study.
Yu-Ting LinYuan-Xiang DengChu-Lin TsaiChien-Hua HuangLi-Chen FuPublished in: JMIR medical informatics (2024)
Our system improved the prediction of 3 different medical outcomes when compared with other machine learning methods. With the pretrained vital sign encoder and repretrained mask language modeling MacBERT encoder, our multimodality model can provide a deeper insight into the characteristics of electronic health records. Additionally, by providing interpretability, we believe that the proposed system can assist nursing staff and physicians in taking appropriate medical decisions.
Keyphrases
- electronic health record
- machine learning
- deep learning
- healthcare
- end stage renal disease
- emergency department
- primary care
- ejection fraction
- mental health
- prognostic factors
- peritoneal dialysis
- autism spectrum disorder
- metabolic syndrome
- type diabetes
- skeletal muscle
- obstructive sleep apnea
- big data
- quality improvement
- adipose tissue