272 related articles for article (PubMed ID: 33614539)
1. Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2).
Reed RA; Morgan AS; Zeitlin J; Jarreau PH; Torchin H; Pierrat V; Ancel PY; Khoshnood B
Front Pediatr; 2020; 8():585868. PubMed ID: 33614539
[No Abstract] [Full Text] [Related]
2. Assessing the risk of early unplanned rehospitalisation in preterm babies: EPIPAGE 2 study.
Reed RA; Morgan AS; Zeitlin J; Jarreau PH; Torchin H; Pierrat V; Ancel PY; Khoshnood B
BMC Pediatr; 2019 Nov; 19(1):451. PubMed ID: 31752782
[TBL] [Abstract][Full Text] [Related]
3. Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation.
Zhou H; Albrecht MA; Roberts PA; Porter P; Della PR
Aust Health Rev; 2021 Jun; 45(3):328-337. PubMed ID: 33840419
[TBL] [Abstract][Full Text] [Related]
4. Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study.
Zhang J; Ma G; Peng S; Hou J; Xu R; Luo L; Hu J; Yao N; Wang J; Huang X
J Med Internet Res; 2023 Nov; 25():e49016. PubMed ID: 37971792
[TBL] [Abstract][Full Text] [Related]
5. Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions.
Massaad E; Williams N; Hadzipasic M; Patel SS; Fourman MS; Kiapour A; Schoenfeld AJ; Shankar GM; Shin JH
Neurosurg Focus; 2021 May; 50(5):E5. PubMed ID: 33932935
[TBL] [Abstract][Full Text] [Related]
6. Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.
Lo YT; Liao JC; Chen MH; Chang CM; Li CT
BMC Med Inform Decis Mak; 2021 Oct; 21(1):288. PubMed ID: 34670553
[TBL] [Abstract][Full Text] [Related]
7. Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.
Noel CW; Sutradhar R; Gotlib Conn L; Forner D; Chan WC; Fu R; Hallet J; Coburn NG; Eskander A
JAMA Otolaryngol Head Neck Surg; 2022 Aug; 148(8):764-772. PubMed ID: 35771564
[TBL] [Abstract][Full Text] [Related]
8. Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.
Lei M; Han Z; Wang S; Guo C; Zhang X; Song Y; Lin F; Huang T
Front Immunol; 2022; 13():979877. PubMed ID: 36325351
[TBL] [Abstract][Full Text] [Related]
9. Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study.
Huang G; Jin Q; Mao Y
J Med Internet Res; 2023 Sep; 25():e46891. PubMed ID: 37698911
[TBL] [Abstract][Full Text] [Related]
10. Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.
Desai RJ; Wang SV; Vaduganathan M; Evers T; Schneeweiss S
JAMA Netw Open; 2020 Jan; 3(1):e1918962. PubMed ID: 31922560
[TBL] [Abstract][Full Text] [Related]
11. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis.
Sufriyana H; Husnayain A; Chen YL; Kuo CY; Singh O; Yeh TY; Wu YW; Su EC
JMIR Med Inform; 2020 Nov; 8(11):e16503. PubMed ID: 33200995
[TBL] [Abstract][Full Text] [Related]
12. Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.
Alaka SA; Menon BK; Brobbey A; Williamson T; Goyal M; Demchuk AM; Hill MD; Sajobi TT
Front Neurol; 2020; 11():889. PubMed ID: 32982920
[No Abstract] [Full Text] [Related]
13. Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.
Lo-Ciganic WH; Huang JL; Zhang HH; Weiss JC; Wu Y; Kwoh CK; Donohue JM; Cochran G; Gordon AJ; Malone DC; Kuza CC; Gellad WF
JAMA Netw Open; 2019 Mar; 2(3):e190968. PubMed ID: 30901048
[TBL] [Abstract][Full Text] [Related]
14. Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden.
Lindblad Wollmann C; Hart KD; Liu C; Caughey AB; Stephansson O; Snowden JM
Acta Obstet Gynecol Scand; 2021 Mar; 100(3):513-520. PubMed ID: 33031579
[TBL] [Abstract][Full Text] [Related]
15. Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.
Park JY; Hsu TC; Hu JR; Chen CY; Hsu WT; Lee M; Ho J; Lee CC
J Med Internet Res; 2022 Apr; 24(4):e29982. PubMed ID: 35416785
[TBL] [Abstract][Full Text] [Related]
16. Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches.
Weng SF; Vaz L; Qureshi N; Kai J
PLoS One; 2019; 14(3):e0214365. PubMed ID: 30917171
[TBL] [Abstract][Full Text] [Related]
17. Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
Zack CJ; Senecal C; Kinar Y; Metzger Y; Bar-Sinai Y; Widmer RJ; Lennon R; Singh M; Bell MR; Lerman A; Gulati R
JACC Cardiovasc Interv; 2019 Jul; 12(14):1304-1311. PubMed ID: 31255564
[TBL] [Abstract][Full Text] [Related]
18. Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.
Jones S; Hargrave C; Deegan T; Holt T; Mengersen K
Med Phys; 2020 Apr; 47(4):1452-1459. PubMed ID: 31981427
[TBL] [Abstract][Full Text] [Related]
19. A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong.
Guan J; Leung E; Kwok KO; Chen FY
BMC Med Res Methodol; 2023 Jan; 23(1):14. PubMed ID: 36639745
[TBL] [Abstract][Full Text] [Related]
20. Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study.
Matsumoto K; Nohara Y; Sakaguchi M; Takayama Y; Fukushige S; Soejima H; Nakashima N; Kamouchi M
JMIR Perioper Med; 2023 Oct; 6():e50895. PubMed ID: 37883164
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]