242 related articles for article (PubMed ID: 31220274)
1. Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort.
Fang G; Annis IE; Elston-Lafata J; Cykert S
J Am Med Inform Assoc; 2019 Oct; 26(10):977-988. PubMed ID: 31220274
[TBL] [Abstract][Full Text] [Related]
2. A machine learning approach to predict early outcomes after pituitary adenoma surgery.
Hollon TC; Parikh A; Pandian B; Tarpeh J; Orringer DA; Barkan AL; McKean EL; Sullivan SE
Neurosurg Focus; 2018 Nov; 45(5):E8. PubMed ID: 30453460
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.
Cheng HY; Wu YC; Lin MH; Liu YL; Tsai YY; Wu JH; Pan KH; Ke CJ; Chen CM; Liu DP; Lin IF; Chuang JH
J Med Internet Res; 2020 Aug; 22(8):e15394. PubMed ID: 32755888
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features.
Sun Y; Rashedi N; Vaze V; Shah P; Halter R; Elliott JT; Paradis NA
Mil Med; 2021 Jan; 186(Suppl 1):445-451. PubMed ID: 33499528
[TBL] [Abstract][Full Text] [Related]
7. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.
Lin H; Long E; Ding X; Diao H; Chen Z; Liu R; Huang J; Cai J; Xu S; Zhang X; Wang D; Chen K; Yu T; Wu D; Zhao X; Liu Z; Wu X; Jiang Y; Yang X; Cui D; Liu W; Zheng Y; Luo L; Wang H; Chan CC; Morgan IG; He M; Liu Y
PLoS Med; 2018 Nov; 15(11):e1002674. PubMed ID: 30399150
[TBL] [Abstract][Full Text] [Related]
8. Predicting personalized process-outcome associations in psychotherapy using machine learning approaches-A demonstration.
Rubel JA; Zilcha-Mano S; Giesemann J; Prinz J; Lutz W
Psychother Res; 2020 Mar; 30(3):300-309. PubMed ID: 30913982
[No Abstract] [Full Text] [Related]
9. On learning disentangled representations for individual treatment effect estimation.
Chu J; Sun Z; Dong W; Shi J; Huang Z
J Biomed Inform; 2021 Dec; 124():103940. PubMed ID: 34728379
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli.
Njage PMK; Leekitcharoenphon P; Hald T
Int J Food Microbiol; 2019 Mar; 292():72-82. PubMed ID: 30579059
[TBL] [Abstract][Full Text] [Related]
12. Using machine learning to predict opioid misuse among U.S. adolescents.
Han DH; Lee S; Seo DC
Prev Med; 2020 Jan; 130():105886. PubMed ID: 31705938
[TBL] [Abstract][Full Text] [Related]
13. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.
Hae H; Kang SJ; Kim WJ; Choi SY; Lee JG; Bae Y; Cho H; Yang DH; Kang JW; Lim TH; Lee CH; Kang DY; Lee PH; Ahn JM; Park DW; Lee SW; Kim YH; Lee CW; Park SW; Park SJ
PLoS Med; 2018 Nov; 15(11):e1002693. PubMed ID: 30422987
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Estimating individual treatment effects by gradient boosting trees.
Sugasawa S; Noma H
Stat Med; 2019 Nov; 38(26):5146-5159. PubMed ID: 31460679
[TBL] [Abstract][Full Text] [Related]
16. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.
Huang Y; Li J; Li M; Aparasu RR
BMC Med Res Methodol; 2023 Nov; 23(1):268. PubMed ID: 37957593
[TBL] [Abstract][Full Text] [Related]
17. Estimating real-world performance of a predictive model: a case-study in predicting mortality.
Major VJ; Jethani N; Aphinyanaphongs Y
JAMIA Open; 2020 Jul; 3(2):243-251. PubMed ID: 32734165
[TBL] [Abstract][Full Text] [Related]
18. Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department.
Ni Y; Beck AF; Taylor R; Dyas J; Solti I; Grupp-Phelan J; Dexheimer JW
J Am Med Inform Assoc; 2016 Jul; 23(4):671-80. PubMed ID: 27121609
[TBL] [Abstract][Full Text] [Related]
19. Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990-2022).
Sheehy J; Rutledge H; Acharya UR; Loh HW; Gururajan R; Tao X; Zhou X; Li Y; Gurney T; Kondalsamy-Chennakesavan S
Artif Intell Med; 2023 May; 139():102536. PubMed ID: 37100507
[TBL] [Abstract][Full Text] [Related]
20. Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng SF; Reps J; Kai J; Garibaldi JM; Qureshi N
PLoS One; 2017; 12(4):e0174944. PubMed ID: 28376093
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]