ArticleIJCAI : proceedings of the conference2019
DDL: Deep Dictionary Learning for Predictive Phenotyping.
Article in IJCAI : proceedings of the conference, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- MediSim: Multi-granular simulation for enriching longitudinal, multi-modal electronic health records.Patterns (New York, N.Y.) · 2025Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction.Health data science · 2024Article
- Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.Nature communications · 2023Article
- Synthesize Extremely High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model.Research square · 2023Article
- Marker-free characterization of full-length transcriptomes of single live circulating tumor cells.Genome research · 2023Article
- Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.Proceedings of machine learning research · 2022Article
- Hierarchical Information Criterion for Variable Abstraction.Proceedings of machine learning research · 2021Article
- TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Predictive phenotyping is about accurately predicting what phenotypes will occur in the next clinical visit based on longitudinal Electronic Health Record (EHR) data. While deep learning (DL) models have recently demonstrated strong performance in predictive phenotyping, they require access to a large amount of labeled data, which are expensive to acquire. To address this label-insufficient challenge, we propose a deep dictionary learning framework (DDL) for phenotyping, which utilizes unlabeled data as a complementary source of information to generate a better, more succinct data representation. Our empirical evaluations on multiple EHR datasets demonstrated that DDL outperforms the existing predictive phenotyping methods on a wide variety of clinical tasks that require patient phenotyping. The results also show that unlabeled data can be used to generate better data representation that helps improve DDL's phenotyping performance over existing methods that only uses labeled data.
Identifiers
What 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.