ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2019
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 5 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- PHO-Agents: A Large Language Model-Powered Multi-Agent System for Predicting Health Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphoma.HemaSphere · 2026Article
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- How machine learning on real world clinical data improves adverse event recording for endoscopy.NPJ digital medicine · 2025Article
- The Danish Lymphoid Cancer Research (DALY-CARE) Data Resource: The Basis for Developing Data-Driven Hematology.Clinical epidemiology · 2025Article
- PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking.International ACM SIGIR Conference on Research and Development in Information Retrieval. Annual International ACMSIGIR Conference on Research & Development in Information Retrieval · 2024Article
- Deployment and validation of the CLL treatment infection model adjoined to an EHR system.NPJ digital medicine · 2024Article
- Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Article
- Stable clinical risk prediction against distribution shift in electronic health records.Patterns (New York, N.Y.) · 2023Article
- Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer's Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator.The journal of prevention of Alzheimer's disease · 2023Article
- MR-KPA: medication recommendation by combining knowledge-enhanced pre-training with a deep adversarial network.BMC bioinformatics · 2022Article
- Uncovering interpretable potential confounders in electronic medical records.Nature communications · 2022Article
- Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In recent years, large amounts of health data, such as patient Electronic Health Records (EHR), are becoming readily available. This provides an unprecedented opportunity for knowledge discovery and data mining algorithms to dig insights from them, which can, later on, be helpful to the improvement of the quality of care delivery. Predictive modeling of clinical risks, including in-hospital mortality, hospital readmission, chronic disease onset, condition exacerbation, etc., from patient EHR, is one of the health data analytic problems that attract lots of the interests. The reason is not only because the problem is important in clinical settings, but also is challenging when working with EHR such as sparsity, irregularity, temporality, etc. Different from applications in other domains such as computer vision and natural language processing, the data samples in medicine (patients) are relatively limited, which creates lots of troubles for building effective predictive models, especially for complicated ones such as deep learning. In this paper, we propose MetaPred, a meta-learning framework for clinical risk prediction from longitudinal patient EHR. In particular, in order to predict the target risk with limited data samples, we train a meta-learner from a set of related risk prediction tasks which learns how a good predictor is trained. The meta-learned can then be directly used in target risk prediction, and the limited available samples in the target domain can be used for further fine-tuning the model performance. The effectiveness of MetaPred is tested on a real patient EHR repository from Oregon Health & Science University. We are able to demonstrate that with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) as base predictors, MetaPred can achieve much better performance for predicting target risk with low resources comparing with the predictor trained on the limited samples available for this risk alone.
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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.