ArticleInternational journal of medical informatics2020
Mining clinical phrases from nursing notes to discover risk factors of patient deterioration.
Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 39 citations in OpenAlex.
- Conceptual framework for prediction models of patient deterioration based on nursing documentation patterns: reproducibility and generalizability with a large number of hospitals across the United States.Journal of biomedical informatics · 2025Article
- Indicators of clinical deterioration in adult general ward patients from nurses' perspectives: a mixed-methods systematic review.BMC nursing · 2024Article
- Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.Applied clinical informatics · 2024Article
- Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace.Journal of multidisciplinary healthcare · 2024Review
- Translating Nursing Data into Computational Metrics: An Evaluation Guideline for Inpatient Intravenous and Subcutaneous Insulin Management.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Natural Language Processing of Nursing Notes: An Integrative Review.Computers, informatics, nursing : CIN · 2023Review
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- Extraction and Quantification of Words Representing Degrees of Diseases: Combining the Fuzzy C-Means Method and Gaussian Membership.JMIR formative research · 2022Article
- Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department.BMC medical research methodology · 2022Article
- Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods.Frontiers in psychiatry · 2022Article
- Application of a Nursing Data-Driven Model for Continuous Improvement of PICC Care Quality.Journal of healthcare engineering · 2022Article
- Capturing Concerns about Patient Deterioration in Narrative Documentation in Home Healthcare.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Real-World Data and Machine Learning to Predict Cardiac Amyloidosis.International journal of environmental research and public health · 2021Article
- Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- The nurse COVID and historical epidemics literature repository: Development, description, and summary.Nursing outlookArticle
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveEarly identification and treatment of patient deterioration is crucial to improving clinical outcomes. To act, hospital rapid response (RR) teams often rely on nurses' clinical judgement typically documented narratively in the electronic health record (EHR). We developed a data-driven, unsupervised method to discover potential risk factors of RR events from nursing notes.
methodsWe applied multiple natural language processing methods, including language modelling, word embeddings, and two phrase mining methods (TextRank and NC-Value), to identify quality phrases that represent clinical entities from unannotated nursing notes. TextRank was used to determine the important word-sequences in each note. NC-Value was then used to globally rank the locally-important sequences across the whole corpus. We evaluated our method both on its accuracy compared to human judgement and on the ability of the mined phrases to predict a clinical outcome, RR event hazard.
resultsWhen applied to 61,740 hospital encounters with 1,067 RR events and 778,955 notes, our method achieved an average precision of 0.590 to 0.764 (when excluding numeric tokens). Time-dependent covariates Cox model using the phrases achieved a concordance index of 0.739. Clustering the phrases revealed clinical concepts significantly associated with RR event hazard. DISCUSSION: Our findings demonstrate that our minimal-annotation, unsurprised method can rapidly mine quality phrases from a large amount of nursing notes, and these identified phrases are useful for downstream tasks, such as clinical outcome predication and risk factor identification.
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Registered trials
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