ArticlePloS one2018
Readmission prediction via deep contextual embedding of clinical concepts.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 127 citations in OpenAlex.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review.BMJ (Clinical research ed.) · 2020Pooled it
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
- Survival models and longitudinal medical events for hospital readmission forecasting.BMC health services research · 2024Article
- A machine learning model to predict heart failure readmission: toward optimal feature set.Frontiers in artificial intelligence · 2024Article
- Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach.BMC medical informatics and decision making · 2023Article
- [Advances in heart failure clinical research based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping reviewEuropean heart journal. Digital health · 2022Article
- Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.JMIR medical informatics · 2022Article
- Decision Support Systems in HF based on Deep Learning Technologies.Current heart failure reports · 2022Review
- Digital Biomarkers for Supporting Transitional Care Decisions: Protocol for a Transnational Feasibility Study.JMIR research protocols · 2022Article
- Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond.An international journal on information fusion · 2022Article
- Article
- Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study.JMIR medical informatics · 2021Article
- Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.NPJ digital medicine · 2021Article
- Application of machine learning in predicting hospital readmissions: a scoping review of the literature.BMC medical research methodology · 2021Article
- Comparative effectiveness of medical concept embedding for feature engineering in phenotyping.JAMIA open · 2021Article
- Article
- Recurrent disease progression networks for modelling risk trajectory of heart failure.PloS one · 2021Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveHospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. MATERIALS AND
methodsWe propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients.
resultsThe proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. DISCUSSION: Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions.
conclusionThis is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.
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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.