252 related articles for article (PubMed ID: 31791325)
1. Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.
Ford E; Rooney P; Oliver S; Hoile R; Hurley P; Banerjee S; van Marwijk H; Cassell J
BMC Med Inform Decis Mak; 2019 Dec; 19(1):248. PubMed ID: 31791325
[TBL] [Abstract][Full Text] [Related]
2. Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case-control study using electronic primary care records.
Ford E; Sheppard J; Oliver S; Rooney P; Banerjee S; Cassell JA
BMJ Open; 2021 Jan; 11(1):e039248. PubMed ID: 33483436
[TBL] [Abstract][Full Text] [Related]
3. Could dementia be detected from UK primary care patients' records by simple automated methods earlier than by the treating physician? A retrospective case-control study.
Ford E; Starlinger J; Rooney P; Oliver S; Banerjee S; van Marwijk H; Cassell J
Wellcome Open Res; 2020; 5():120. PubMed ID: 32766457
[No Abstract] [Full Text] [Related]
4. A machine learning-based framework to identify type 2 diabetes through electronic health records.
Zheng T; Xie W; Xu L; He X; Zhang Y; You M; Yang G; Chen Y
Int J Med Inform; 2017 Jan; 97():120-127. PubMed ID: 27919371
[TBL] [Abstract][Full Text] [Related]
5. Primary care electronic medical records can be used to predict risk and identify potentially modifiable factors for early and late death in adult onset epilepsy.
Hrabok M; Engbers JDT; Wiebe S; Sajobi TT; Subota A; Almohawes A; Federico P; Hanson A; Klein KM; Peedicail J; Pillay N; Singh S; Josephson CB
Epilepsia; 2021 Jan; 62(1):51-60. PubMed ID: 33316095
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.
Saccà V; Sarica A; Novellino F; Barone S; Tallarico T; Filippelli E; Granata A; Chiriaco C; Bruno Bossio R; Valentino P; Quattrone A
Brain Imaging Behav; 2019 Aug; 13(4):1103-1114. PubMed ID: 29992392
[TBL] [Abstract][Full Text] [Related]
8. Fetal health status prediction based on maternal clinical history using machine learning techniques.
Akbulut A; Ertugrul E; Topcu V
Comput Methods Programs Biomed; 2018 Sep; 163():87-100. PubMed ID: 30119860
[TBL] [Abstract][Full Text] [Related]
9. Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.
Russo DP; Zorn KM; Clark AM; Zhu H; Ekins S
Mol Pharm; 2018 Oct; 15(10):4361-4370. PubMed ID: 30114914
[TBL] [Abstract][Full Text] [Related]
10. Machine learning models to detect and predict patient safety events using electronic health records: A systematic review.
Deimazar G; Sheikhtaheri A
Int J Med Inform; 2023 Dec; 180():105246. PubMed ID: 37837710
[TBL] [Abstract][Full Text] [Related]
11. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.
Cepeda S; Arrese I; García-García S; Velasco-Casares M; Escudero-Caro T; Zamora T; Sarabia R
World Neurosurg; 2021 Feb; 146():e1147-e1159. PubMed ID: 33259973
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database.
Miller PE; Pawar S; Vaccaro B; McCullough M; Rao P; Ghosh R; Warier P; Desai NR; Ahmad T
J Card Fail; 2019 Jun; 25(6):479-483. PubMed ID: 30738152
[TBL] [Abstract][Full Text] [Related]
14. Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms.
Senan EM; Abunadi I; Jadhav ME; Fati SM
Comput Math Methods Med; 2021; 2021():8500314. PubMed ID: 34966445
[TBL] [Abstract][Full Text] [Related]
15. Development of a prediction model for hypotension after induction of anesthesia using machine learning.
Kang AR; Lee J; Jung W; Lee M; Park SY; Woo J; Kim SH
PLoS One; 2020; 15(4):e0231172. PubMed ID: 32298292
[TBL] [Abstract][Full Text] [Related]
16. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.
Nhu VH; Shirzadi A; Shahabi H; Singh SK; Al-Ansari N; Clague JJ; Jaafari A; Chen W; Miraki S; Dou J; Luu C; Górski K; Thai Pham B; Nguyen HD; Ahmad BB
Int J Environ Res Public Health; 2020 Apr; 17(8):. PubMed ID: 32316191
[TBL] [Abstract][Full Text] [Related]
17. Trends in diagnosis and treatment for people with dementia in the UK from 2005 to 2015: a longitudinal retrospective cohort study.
Donegan K; Fox N; Black N; Livingston G; Banerjee S; Burns A
Lancet Public Health; 2017 Mar; 2(3):e149-e156. PubMed ID: 29253388
[TBL] [Abstract][Full Text] [Related]
18. Prediction model development of late-onset preeclampsia using machine learning-based methods.
Jhee JH; Lee S; Park Y; Lee SE; Kim YA; Kang SW; Kwon JY; Park JT
PLoS One; 2019; 14(8):e0221202. PubMed ID: 31442238
[TBL] [Abstract][Full Text] [Related]
19. Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.
Tibble H; Tsanas A; Horne E; Horne R; Mizani M; Simpson CR; Sheikh A
BMJ Open; 2019 Jul; 9(7):e028375. PubMed ID: 31292179
[TBL] [Abstract][Full Text] [Related]
20. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.
Rahimian F; Salimi-Khorshidi G; Payberah AH; Tran J; Ayala Solares R; Raimondi F; Nazarzadeh M; Canoy D; Rahimi K
PLoS Med; 2018 Nov; 15(11):e1002695. PubMed ID: 30458006
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]