ArticleComputational and mathematical methods in medicine2021
Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From Sentiment to Signal: Narrative-Physiology Discordance as a Testable Target for Artificial Intelligence in Critical Care.Medical sciences (Basel, Switzerland) · 2026Article
- Article
- Clinical sentiment analysis of ICU notes: a dataset and a comparison of lexicon and clinical language model methods.BMC medical informatics and decision making · 2026Article
- Relationship between the sentiment of nursing notes and one-year mortality of elderly sepsis patients.PloS one · 2025Article
- Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.PloS one · 2025Article
- Clinical Sentiment Analysis by Large Language Models Enhances Prediction of Hepatorenal Syndrome.Gastro hep advances · 2025Article
- Article
- Delivery of Hand Care to Patients With High Anxiety Burden.The Journal of hand surgery · 2024Article
- Caregivers Attitude Detection From Clinical Notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms.Annals of translational medicine · 2022Article
- A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study.Annals of translational medicine · 2022Article
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5 authors.
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Abstract
In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begins to be extracted from large-scale unstructured data like nursing notes. This study extracted sentiment information in nursing notes and explored its association with in-hospital 28-day mortality in sepsis patients. The data of patients and nursing notes were extracted from the MIMIC-III database. A COX proportional hazard model was used to analyze the relationship between sentiment scores in nursing notes and in-hospital 28-day mortality. Based on the COX model, the individual prognostic index (PI) was calculated, and then, survival was analyzed. Among eligible 1851 sepsis patients, 580 cases suffered from in-hospital 28-day mortality (dead group), while 1271 survived (survived group). Significant differences were shown between two groups in sentiment polarity, Simplified Acute Physiology Score II (SAPS-II) score, age, and intensive care unit (ICU) type (all
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