These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
113 related articles for article (PubMed ID: 37753238)
1. Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record. Kennedy CJ; Chiu C; Chapman AC; Gologorskaya O; Farhan H; Han M; Hodgson M; Lazzareschi D; Ashana D; Lee S; Smith AK; Espejo E; Boscardin J; Pirracchio R; Cobert J Crit Care Explor; 2023 Oct; 5(10):e0960. PubMed ID: 37753238 [TBL] [Abstract][Full Text] [Related]
2. Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data. Marafino BJ; Park M; Davies JM; Thombley R; Luft HS; Sing DC; Kazi DS; DeJong C; Boscardin WJ; Dean ML; Dudley RA JAMA Netw Open; 2018 Dec; 1(8):e185097. PubMed ID: 30646310 [TBL] [Abstract][Full Text] [Related]
3. Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients. Waudby-Smith IER; Tran N; Dubin JA; Lee J PLoS One; 2018; 13(6):e0198687. PubMed ID: 29879201 [TBL] [Abstract][Full Text] [Related]
4. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing. Han S; Zhang RF; Shi L; Richie R; Liu H; Tseng A; Quan W; Ryan N; Brent D; Tsui FR J Biomed Inform; 2022 Mar; 127():103984. PubMed ID: 35007754 [TBL] [Abstract][Full Text] [Related]
6. Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database. Gao Q; Wang D; Sun P; Luan X; Wang W Comput Math Methods Med; 2021; 2021():3440778. PubMed ID: 34691236 [TBL] [Abstract][Full Text] [Related]
7. A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study. Liu Z; Yang Y; Song H; Luo J Ann Transl Med; 2022 Jun; 10(12):676. PubMed ID: 35845515 [TBL] [Abstract][Full Text] [Related]
8. Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study. McCoy TH; Castro VM; Cagan A; Roberson AM; Kohane IS; Perlis RH PLoS One; 2015; 10(8):e0136341. PubMed ID: 26302085 [TBL] [Abstract][Full Text] [Related]
9. Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients. Taggart M; Chapman WW; Steinberg BA; Ruckel S; Pregenzer-Wenzler A; Du Y; Ferraro J; Bucher BT; Lloyd-Jones DM; Rondina MT; Shah RU JAMA Netw Open; 2018 Oct; 1(6):e183451. PubMed ID: 30646240 [TBL] [Abstract][Full Text] [Related]
10. Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease. Silverman AL; Sushil M; Bhasuran B; Ludwig D; Buchanan J; Racz R; Parakala M; El-Kamary S; Ahima O; Belov A; Choi L; Billings M; Li Y; Habal N; Liu Q; Tiwari J; Butte AJ; Rudrapatna VA Clin Pharmacol Ther; 2024 Jun; 115(6):1391-1399. PubMed ID: 38459719 [TBL] [Abstract][Full Text] [Related]
11. How is the Doctor Feeling? ICU Provider Sentiment is Associated with Diagnostic Imaging Utilization. Ghassemi MM; Al-Hanai T; Raffa JD; Mark RG; Nemati S; Chokshi FH Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():4058-4064. PubMed ID: 30441248 [TBL] [Abstract][Full Text] [Related]
12. Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care. Mahendra M; Luo Y; Mills H; Schenk G; Butte AJ; Dudley RA Crit Care Explor; 2021 Jun; 3(6):e0450. PubMed ID: 34136824 [TBL] [Abstract][Full Text] [Related]
13. Identification of asthma control factor in clinical notes using a hybrid deep learning model. Agnikula Kshatriya BS; Sagheb E; Wi CI; Yoon J; Seol HY; Juhn Y; Sohn S BMC Med Inform Decis Mak; 2021 Nov; 21(Suppl 7):272. PubMed ID: 34753481 [TBL] [Abstract][Full Text] [Related]
14. Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database. Saeed M; Villarroel M; Reisner AT; Clifford G; Lehman LW; Moody G; Heldt T; Kyaw TH; Moody B; Mark RG Crit Care Med; 2011 May; 39(5):952-60. PubMed ID: 21283005 [TBL] [Abstract][Full Text] [Related]
15. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Thorsen-Meyer HC; Nielsen AB; Nielsen AP; Kaas-Hansen BS; Toft P; Schierbeck J; Strøm T; Chmura PJ; Heimann M; Dybdahl L; Spangsege L; Hulsen P; Belling K; Brunak S; Perner A Lancet Digit Health; 2020 Apr; 2(4):e179-e191. PubMed ID: 33328078 [TBL] [Abstract][Full Text] [Related]
16. Sentiment analysis of clinical narratives: A scoping review. Denecke K; Reichenpfader D J Biomed Inform; 2023 Apr; 140():104336. PubMed ID: 36958461 [TBL] [Abstract][Full Text] [Related]
17. Prevalence and Nature of Financial Considerations Documented in Narrative Clinical Records in Intensive Care Units. Gordon DD; Patel I; Pellegrini AM; Perlis RH JAMA Netw Open; 2018 Nov; 1(7):e184178. PubMed ID: 30646344 [TBL] [Abstract][Full Text] [Related]
18. Comparing deep learning architectures for sentiment analysis on drug reviews. Colón-Ruiz C; Segura-Bedmar I J Biomed Inform; 2020 Oct; 110():103539. PubMed ID: 32818665 [TBL] [Abstract][Full Text] [Related]
19. Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting. Le DV; Montgomery J; Kirkby KC; Scanlan J J Biomed Inform; 2018 Oct; 86():49-58. PubMed ID: 30118855 [TBL] [Abstract][Full Text] [Related]
20. Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes. Ye J; Yao L; Shen J; Janarthanam R; Luo Y BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 11):295. PubMed ID: 33380338 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]