Sentiment in Clinical Notes: A Predictor for Length of Stay?

Sentiment in Clinical Notes: A Predictor for Length of Stay? | medRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Sentiment in Clinical Notes: A Predictor for Length of Stay? View ORCID Profile Aidan Boyne , View ORCID Profile Maximillian Feygin , View ORCID Profile John Sholeen , View ORCID Profile Andrew Zimolzak doi: https://doi.org/10.64898/2026.03.16.26348553 Aidan Boyne 1 Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aidan Boyne For correspondence: aidan.boyne{at}bcm.edu Maximillian Feygin 1 Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maximillian Feygin John Sholeen 1 Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John Sholeen Andrew Zimolzak 1 Baylor College of Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew Zimolzak Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Length of stay (LOS) is a critical metric for hospital operational efficiency. While structured clinical data is widely used to predict LOS, unstructured admission notes contain latent prognostic information regarding diagnostic uncertainty and disease complexity. This study evaluates the efficacy of extracting sentiment and direct LOS estimates from admission notes to predict patient hospitalization duration. Methods We conducted a retrospective study of 4,503 adult patients admitted with community-acquired pneumonia between 2013 and 2023. Admission history and physical notes were preprocessed and filtered to extract physician-generated narratives. We evaluated four natural language processing models, VADER, TextBlob, Longformer, and an open-source large language model (GPT-oss-20B), to generate zero-shot sentiment scores. Additionally, GPT-oss-20B was prompted to directly estimate LOS. Model outputs were correlated with actual LOS using linear regression and Pearson correlation coefficients. Results Sentiment models demonstrated statistically significant, albeit weak, correlations with actual LOS. Longformer achieved the highest variance explained among sentiment classifiers (R 2 = 0.019). Direct LOS estimation by the LLM outperformed sentiment-based approaches, demonstrating the strongest correlation with actual hospital duration (r = -0.218, p < 0.001). Model agreement was generally poor (ICC = 0.059), and computational time varied drastically, from 2.6 seconds per 100 notes (TextBlob) to over 370 seconds (GPT-oss-20B). Conclusion Zero-shot sentiment analysis of clinical notes yields a small but measurable correlation with LOS, limited primarily by the objective, non-evaluative nature of clinical documentation. Direct LLM estimation of clinical outcomes outperforms emotional sentiment extraction. Future predictive systems should integrate computationally efficient NLP models capable of capturing latent clinical complexity alongside established structured data variables. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The IRB of Baylor College of Medicine waived ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability Aggregate data produced in the study are available upon reasonable request to the authors. Patient level data is not available due to privacy concerns. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. 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Aidan Boyne , Maximillian Feygin , John Sholeen , Andrew Zimolzak medRxiv 2026.03.16.26348553; doi: https://doi.org/10.64898/2026.03.16.26348553 Share This Article: Copy Citation Tools Sentiment in Clinical Notes: A Predictor for Length of Stay? Aidan Boyne , Maximillian Feygin , John Sholeen , Andrew Zimolzak medRxiv 2026.03.16.26348553; doi: https://doi.org/10.64898/2026.03.16.26348553 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Health Informatics Subject Areas All Articles Addiction Medicine (637) Allergy and Immunology (917) Anesthesia (348) Cardiovascular Medicine (4974) Dentistry and Oral Medicine (493) Dermatology (426) Emergency Medicine (674) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1682) Epidemiology (16207) Forensic Medicine (36) Gastroenterology (1238) Genetic and Genomic Medicine (7250) Geriatric Medicine (751) Health Economics (1092) Health Informatics (5262) Health Policy (1463) Health Systems and Quality Improvement (1825) Hematology (599) HIV/AIDS (1375) Infectious Diseases (except HIV/AIDS) (16466) Intensive Care and Critical Care Medicine (1199) Medical Education (696) Medical Ethics (156) Nephrology (747) Neurology (7552) Nursing (377) Nutrition (1120) Obstetrics and Gynecology (1293) Occupational and Environmental Health (1038) Oncology (3730) Ophthalmology (1087) Orthopedics (406) Otolaryngology (463) Pain Medicine (499) Palliative Medicine (143) Pathology (720) Pediatrics (1852) Pharmacology and Therapeutics (752) Primary Care Research (784) Psychiatry and Clinical Psychology (6099) Public and Global Health (9957) Radiology and Imaging (2515) Rehabilitation Medicine and Physical Therapy (1506) Respiratory Medicine (1264) Rheumatology (657) Sexual and Reproductive Health (799) Sports Medicine (595) Surgery (799) Toxicology (109) Transplantation (318) Urology (307)

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