ArticleJournal of machine learning research : JMLR2023
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.
Article in Journal of machine learning research : JMLR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Phenotypic prediction of missense variants via deep contrastive learning.Nature biomedical engineering · 2026Article
- Inference of dependency knowledge graph for Electronic Health Records.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- DOME: Directional medical embedding vectors from Electronic Health Records.Journal of biomedical informatics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Electronic healthcare records (EHR) provide a rich resource for healthcare research. An important problem for the efficient utilization of the EHR data is the representation of the EHR features, which include the unstructured clinical narratives and the structured codified data. Matrix factorization-based embeddings trained using the summary-level co-occurrence statistics of EHR data have provided a promising solution for feature representation while preserving patients' privacy. However, such methods do not work well with multi-source data when these sources have overlapping but non-identical features. To accommodate multi-sources learning, we propose a novel word embedding generative model. To obtain multi-source embeddings, we design an efficient
Indexed as
Identifiers
41909164PMC13021262What 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.