ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Patient Representation Transfer Learning from Clinical Notes based on Hierarchical Attention Network.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
- Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.Bioengineering (Basel, Switzerland) · 2024Article
- Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST).Journal of machine learning research : JMLR · 2024Article
- Transfer learning for non-image data in clinical research: A scoping review.PLOS digital health · 2022Article
- CATAN: Chart-aware temporal attention network for adverse outcome prediction.Proceedings. IEEE International Conference on Healthcare Informatics · 2021Article
- Generalized and transferable patient language representation for phenotyping with limited data.Journal of biomedical informatics · 2021Article
- Pre-training phenotyping classifiers.Journal of biomedical informatics · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
To explicitly learn patient representations from longitudinal clinical notes, we propose a hierarchical attention-based recurrent neural network (RNN) with greedy segmentation to distinguish between shorter and longer, more meaningful gaps between notes. The proposed model is evaluated for both a direct clinical prediction task (mortality) and as a transfer learning pre-training model to downstream evaluation (phenotype prediction of obesity and its comorbidities). Experimental results first show the proposed model with appropriate segmentation achieved the best performance on mortality prediction, indicating the effectiveness of hierarchical RNNs in dealing with longitudinal clinical text. Attention weights from the models highlight those parts of notes with the largest impact on mortality prediction and hopefully provide a degree of interpretability. Following the transfer learning approach, we also demonstrate the effectiveness and generalizability of pre-trained patient representations on target tasks of phenotyping.
Identifiers
32477682PMC7233035What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.