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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 37639620)

  • 21. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.
    Ren Y; Loftus TJ; Datta S; Ruppert MM; Guan Z; Miao S; Shickel B; Feng Z; Giordano C; Upchurch GR; Rashidi P; Ozrazgat-Baslanti T; Bihorac A
    JAMA Netw Open; 2022 May; 5(5):e2211973. PubMed ID: 35576007
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Machine learning functional impairment classification with electronic health record data.
    Pavon JM; Previll L; Woo M; Henao R; Solomon M; Rogers U; Olson A; Fischer J; Leo C; Fillenbaum G; Hoenig H; Casarett D
    J Am Geriatr Soc; 2023 Sep; 71(9):2822-2833. PubMed ID: 37195174
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.
    Paliwal N; Jaiswal P; Tutino VM; Shallwani H; Davies JM; Siddiqui AH; Rai R; Meng H
    Neurosurg Focus; 2018 Nov; 45(5):E7. PubMed ID: 30453461
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Weakly Semi-supervised phenotyping using Electronic Health records.
    Nogues IE; Wen J; Lin Y; Liu M; Tedeschi SK; Geva A; Cai T; Hong C
    J Biomed Inform; 2022 Oct; 134():104175. PubMed ID: 36064111
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.
    Shi L; Muthu N; Shaeffer GP; Sun Y; Ruiz Herrera VM; Tsui FR
    Stud Health Technol Inform; 2022 Jun; 290():660-664. PubMed ID: 35673099
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records.
    Choi MH; Kim D; Choi EJ; Jung YJ; Choi YJ; Cho JH; Jeong SH
    Sci Rep; 2022 May; 12(1):7180. PubMed ID: 35505048
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.
    Zapata RD; Huang S; Morris E; Wang C; Harle C; Magoc T; Mardini M; Loftus T; Modave F
    PLoS One; 2023; 18(10):e0292888. PubMed ID: 37862334
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Development and Temporal Validation of a Machine Learning Model to Predict Clinical Deterioration.
    Foote HP; Shaikh Z; Witt D; Shen T; Ratliff W; Shi H; Gao M; Nichols M; Sendak M; Balu S; Osborne K; Kumar KR; Jackson K; McCrary AW; Li JS
    Hosp Pediatr; 2024 Jan; 14(1):11-20. PubMed ID: 38053467
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records.
    Masukawa K; Aoyama M; Yokota S; Nakamura J; Ishida R; Nakayama M; Miyashita M
    Palliat Med; 2022 Sep; 36(8):1207-1216. PubMed ID: 35773973
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.
    Taylor RA; Pare JR; Venkatesh AK; Mowafi H; Melnick ER; Fleischman W; Hall MK
    Acad Emerg Med; 2016 Mar; 23(3):269-78. PubMed ID: 26679719
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.
    Corey KM; Kashyap S; Lorenzi E; Lagoo-Deenadayalan SA; Heller K; Whalen K; Balu S; Heflin MT; McDonald SR; Swaminathan M; Sendak M
    PLoS Med; 2018 Nov; 15(11):e1002701. PubMed ID: 30481172
    [TBL] [Abstract][Full Text] [Related]  

  • 32. An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.
    Wang JK; Hom J; Balasubramanian S; Schuler A; Shah NH; Goldstein MK; Baiocchi MTM; Chen JH
    J Biomed Inform; 2018 Oct; 86():109-119. PubMed ID: 30195660
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Impact of Intraoperative Data on Risk Prediction for Mortality After Intra-Abdominal Surgery.
    Yan X; Goldsmith J; Mohan S; Turnbull ZA; Freundlich RE; Billings FT; Kiran RP; Li G; Kim M
    Anesth Analg; 2022 Jan; 134(1):102-113. PubMed ID: 34908548
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Using machine learning to predict outcomes following suprainguinal bypass.
    Li B; Eisenberg N; Beaton D; Lee DS; Aljabri B; Wijeysundera DN; Rotstein OD; de Mestral C; Mamdani M; Roche-Nagle G; Al-Omran M
    J Vasc Surg; 2024 Mar; 79(3):593-608.e8. PubMed ID: 37804954
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Predicting adverse drug events in older inpatients: a machine learning study.
    Hu Q; Wu B; Wu J; Xu T
    Int J Clin Pharm; 2022 Dec; 44(6):1304-1311. PubMed ID: 36115909
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
    Vaid A; Somani S; Russak AJ; De Freitas JK; Chaudhry FF; Paranjpe I; Johnson KW; Lee SJ; Miotto R; Richter F; Zhao S; Beckmann ND; Naik N; Kia A; Timsina P; Lala A; Paranjpe M; Golden E; Danieletto M; Singh M; Meyer D; O'Reilly PF; Huckins L; Kovatch P; Finkelstein J; Freeman RM; Argulian E; Kasarskis A; Percha B; Aberg JA; Bagiella E; Horowitz CR; Murphy B; Nestler EJ; Schadt EE; Cho JH; Cordon-Cardo C; Fuster V; Charney DS; Reich DL; Bottinger EP; Levin MA; Narula J; Fayad ZA; Just AC; Charney AW; Nadkarni GN; Glicksberg BS
    J Med Internet Res; 2020 Nov; 22(11):e24018. PubMed ID: 33027032
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.
    Mayampurath A; Sanchez-Pinto LN; Hegermiller E; Erondu A; Carey K; Jani P; Gibbons R; Edelson D; Churpek MM
    Pediatr Crit Care Med; 2022 Jul; 23(7):514-523. PubMed ID: 35446816
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study.
    Hur S; Min JY; Yoo J; Kim K; Chung CR; Dykes PC; Cha WC
    J Med Internet Res; 2021 Aug; 23(8):e23508. PubMed ID: 34382940
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Predictors of in-hospital length of stay among cardiac patients: A machine learning approach.
    Daghistani TA; Elshawi R; Sakr S; Ahmed AM; Al-Thwayee A; Al-Mallah MH
    Int J Cardiol; 2019 Aug; 288():140-147. PubMed ID: 30685103
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer.
    Coquet J; Bievre N; Billaut V; Seneviratne M; Magnani CJ; Bozkurt S; Brooks JD; Hernandez-Boussard T
    JAMA Netw Open; 2021 Jan; 4(1):e2031730. PubMed ID: 33481032
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.