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Items: 1 to 20 of 101


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 20;15(11):e1002695. doi: 10.1371/journal.pmed.1002695. eCollection 2018 Nov.


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 6;15(11):e1002674. doi: 10.1371/journal.pmed.1002674. eCollection 2018 Nov.


Detection of mental fatigue state with wearable ECG devices.

Huang S, Li J, Zhang P, Zhang W.

Int J Med Inform. 2018 Nov;119:39-46. doi: 10.1016/j.ijmedinf.2018.08.010. Epub 2018 Sep 5.


Health-related quality of life in early breast cancer.

Groenvold M.

Dan Med Bull. 2010 Sep;57(9):B4184.


Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer.

Montoye AHK, Westgate BS, Fonley MR, Pfeiffer KA.

J Appl Physiol (1985). 2018 May 1;124(5):1284-1293. doi: 10.1152/japplphysiol.00760.2017. Epub 2018 Jan 25.


Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.

Afzali MH, Sunderland M, Stewart S, Masse B, Seguin J, Newton N, Teesson M, Conrod P.

Addiction. 2019 Apr;114(4):662-671. doi: 10.1111/add.14504. Epub 2018 Dec 21.


Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P.

Med Phys. 2018 Jul;45(7):3449-3459. doi: 10.1002/mp.12967. Epub 2018 Jun 13. Erratum in: Med Phys. 2019 Feb;46(2):1080-1087.


Exercise interventions on health-related quality of life for people with cancer during active treatment.

Mishra SI, Scherer RW, Snyder C, Geigle PM, Berlanstein DR, Topaloglu O.

Cochrane Database Syst Rev. 2012 Aug 15;(8):CD008465. doi: 10.1002/14651858.CD008465.pub2. Review.


Predicting depression in rheumatoid arthritis: the signal importance of pain extent and fatigue, and comorbidity.

Wolfe F, Michaud K.

Arthritis Rheum. 2009 May 15;61(5):667-73. doi: 10.1002/art.24428.


Physiological and psychological effects of forest therapy on middle-aged males with high-normal blood pressure.

Ochiai H, Ikei H, Song C, Kobayashi M, Takamatsu A, Miura T, Kagawa T, Li Q, Kumeda S, Imai M, Miyazaki Y.

Int J Environ Res Public Health. 2015 Feb 25;12(3):2532-42. doi: 10.3390/ijerph120302532.


Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study.

Guthrie NL, Berman MA, Edwards KL, Appelbaum KJ, Dey S, Carpenter J, Eisenberg DM, Katz DL.

JMIR Cardio. 2019 Mar 12;3(1):e13030. doi: 10.2196/13030.


Heart Rate Variability in Healthy Non-Concussed Youth Athletes: Exploring the Effect of Age, Sex, and Concussion-Like Symptoms.

Paniccia M, Verweel L, Thomas S, Taha T, Keightley M, Wilson KE, Reed N.

Front Neurol. 2018 Jan 18;8:753. doi: 10.3389/fneur.2017.00753. eCollection 2017.


Prediction of 90Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests.

Ingrisch M, Schöppe F, Paprottka K, Fabritius M, Strobl FF, De Toni EN, Ilhan H, Todica A, Michl M, Paprottka PM.

J Nucl Med. 2018 May;59(5):769-773. doi: 10.2967/jnumed.117.200758. Epub 2017 Nov 16.


Predicting factors for survival of breast cancer patients using machine learning techniques.

Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK.

BMC Med Inform Decis Mak. 2019 Mar 22;19(1):48. doi: 10.1186/s12911-019-0801-4.


Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach.

Mansoor H, Elgendy IY, Segal R, Bavry AA, Bian J.

Heart Lung. 2017 Nov - Dec;46(6):405-411. doi: 10.1016/j.hrtlng.2017.09.003. Epub 2017 Oct 6.


A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.

Ellis K, Kerr J, Godbole S, Lanckriet G, Wing D, Marshall S.

Physiol Meas. 2014 Nov;35(11):2191-203. doi: 10.1088/0967-3334/35/11/2191. Epub 2014 Oct 23.


Effects of Walking in a Forest on Young Women.

Song C, Ikei H, Kagawa T, Miyazaki Y.

Int J Environ Res Public Health. 2019 Jan 15;16(2). pii: E229. doi: 10.3390/ijerph16020229.

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