ArticleJournal of the American Medical Informatics Association : JAMIA2015
Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 58 citations in OpenAlex.
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.JMIR medical informatics · 2022Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.Journal of pathology informatics · 2020Article
- Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.BioMed research international · 2020Article
- Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
- Rich Text Formatted EHR Narratives: A Hidden and Ignored Trove.Studies in health technology and informatics · 2019Article
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.Journal of healthcare informatics research · 2019Article
- Using natural language processing and machine learning to identify breast cancer local recurrence.BMC bioinformatics · 2018Article
- Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.BMC bioinformatics · 2017Article
- Contribution of Natural Language Processing in Predicting Rehospitalization Risk.Medical care · 2017Article
- Recurrent neural networks for classifying relations in clinical notes.Journal of biomedical informatics · 2017Article
- Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction.Journal of cardiovascular translational research · 2017Review
- Tensor factorization toward precision medicine.Briefings in bioinformatics · 2017Article
- Discriminative and Distinct Phenotyping by Constrained Tensor Factorization.Scientific reports · 2017Article
- Contralateral Breast Cancer Event Detection Using Nature Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveExtracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability.
methodsThe authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. RESULTS AND
conclusionSANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features.
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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.