ArticlePloS one2014
Redundancy-aware topic modeling for patient record notes.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
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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
21 citing papers in PubMed.
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- Estimating the Severity of Oral Lesions Via Analysis of Cone Beam Computed Tomography Reports: A Proposed Deep Learning Model.International dental journal · 2025Article
- Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs.NPJ digital medicine · 2024Article
- Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs.medRxiv : the preprint server for health sciences · 2024Article
- Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study.Intelligence-based medicine · 2023Article
- A novel multiple kernel fuzzy topic modeling technique for biomedical data.BMC bioinformatics · 2022Article
- Article
- Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method.JMIRx med · 2021Article
- Identification of social determinants of health using multi-label classification of electronic health record clinical notes.JAMIA open · 2021Article
- Topic modeling to characterize the natural history of ANCA-Associated vasculitis from clinical notes: A proof of concept study.Seminars in arthritis and rheumatism · 2021Article
- A Multiview Model for Detecting the Inappropriate Use of Prescription Medication: Machine Learning Approach.JMIR medical informatics · 2020Article
- Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study.JMIR medical informatics · 2020Article
- Article
- Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment.Journal of acquired immune deficiency syndromes (1999) · 2018Article
- What's in a Note? Unpacking Predictive Value in Clinical Note Representations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.BMC medical informatics and decision making · 2017Article
- Using phrases and document metadata to improve topic modeling of clinical reports.Journal of biomedical informatics · 2016Article
- Evaluating topic model interpretability from a primary care physician perspective.Computer methods and programs in biomedicine · 2016Article
- Learning probabilistic phenotypes from heterogeneous EHR data.Journal of biomedical informatics · 2015Article
- Automated methods for the summarization of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2015Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
The clinical notes in a given patient record contain much redundancy, in large part due to clinicians' documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocation (LDA) topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes. To assess the value of Red-LDA, we experiment with three baselines and our novel redundancy-aware topic modeling method: given a large collection of patient records, (i) apply vanilla LDA to all documents in all input records; (ii) identify and remove all redundancy by chosing a single representative document for each record as input to LDA; (iii) identify and remove all redundant paragraphs in each record, leaving partial, non-redundant documents as input to LDA; and (iv) apply Red-LDA to all documents in all input records. Both quantitative evaluation carried out through log-likelihood on held-out data and topic coherence of produced topics and qualitative assessment of topics carried out by physicians show that Red-LDA produces superior models to all three baseline strategies. This research contributes to the emerging field of understanding the characteristics of the electronic health record and how to account for them in the framework of data mining. The code for the two redundancy-elimination baselines and Red-LDA is made publicly available to the community.
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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.