175 related articles for article (PubMed ID: 29857458)
1. Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch.
Choi J; Choi J; Choi WJ
Stud Health Technol Inform; 2018; 250():265. PubMed ID: 29857458
[TBL] [Abstract][Full Text] [Related]
2. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone.
Kim H; Lee S; Lee S; Hong S; Kang H; Kim N
JMIR Mhealth Uhealth; 2019 Oct; 7(10):e14149. PubMed ID: 31621642
[TBL] [Abstract][Full Text] [Related]
3. Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.
Hatton CM; Paton LW; McMillan D; Cussens J; Gilbody S; Tiffin PA
J Affect Disord; 2019 Mar; 246():857-860. PubMed ID: 30795491
[TBL] [Abstract][Full Text] [Related]
4. Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.
Tighe PJ; Harle CA; Hurley RW; Aytug H; Boezaart AP; Fillingim RB
Pain Med; 2015 Jul; 16(7):1386-401. PubMed ID: 26031220
[TBL] [Abstract][Full Text] [Related]
5. Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm.
Na KS; Cho SE; Geem ZW; Kim YK
Neurosci Lett; 2020 Mar; 721():134804. PubMed ID: 32014516
[TBL] [Abstract][Full Text] [Related]
6. Machine learning models for predicting depression in Korean young employees.
Kim SS; Gil M; Min EJ
Front Public Health; 2023; 11():1201054. PubMed ID: 37501944
[TBL] [Abstract][Full Text] [Related]
7. Predictive modeling in pediatric traumatic brain injury using machine learning.
Chong SL; Liu N; Barbier S; Ong ME
BMC Med Res Methodol; 2015 Mar; 15():22. PubMed ID: 25886156
[TBL] [Abstract][Full Text] [Related]
8. Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).
Dipnall JF; Pasco JA; Berk M; Williams LJ; Dodd S; Jacka FN; Meyer D
Eur Psychiatry; 2017 Jan; 39():40-50. PubMed ID: 27810617
[TBL] [Abstract][Full Text] [Related]
9. Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.
Dipnall JF; Pasco JA; Berk M; Williams LJ; Dodd S; Jacka FN; Meyer D
PLoS One; 2016; 11(12):e0167055. PubMed ID: 27935995
[TBL] [Abstract][Full Text] [Related]
10. Applying Machine-Learning Techniques to Build Self-reported Depression Prediction Models.
Choi J; Choi J; Jung HT
Comput Inform Nurs; 2018 Jul; 36(7):317-321. PubMed ID: 29985815
[No Abstract] [Full Text] [Related]
11. Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.
Dipnall JF; Pasco JA; Berk M; Williams LJ; Dodd S; Jacka FN; Meyer D
PLoS One; 2016; 11(2):e0148195. PubMed ID: 26848571
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning to Identify Behavioral Determinants of Oral Health in Inner City Older Hispanic Adults.
Yoon S; Choi T; Odlum M; Mitchell DA; Kronish IM; Davidson KW; Finkelstein J
Stud Health Technol Inform; 2018; 251():253-256. PubMed ID: 29968651
[TBL] [Abstract][Full Text] [Related]
13. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.
Zhang B; Liang XL; Gao HY; Ye LS; Wang YG
Genet Mol Res; 2016 May; 15(2):. PubMed ID: 27323037
[TBL] [Abstract][Full Text] [Related]
14. Breast Cancer Patients' Depression Prediction by Machine Learning Approach.
Cvetković J
Cancer Invest; 2017 Sep; 35(8):569-572. PubMed ID: 28872366
[TBL] [Abstract][Full Text] [Related]
15. Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.
Churpek MM; Yuen TC; Winslow C; Meltzer DO; Kattan MW; Edelson DP
Crit Care Med; 2016 Feb; 44(2):368-74. PubMed ID: 26771782
[TBL] [Abstract][Full Text] [Related]
16. Developing a dengue forecast model using machine learning: A case study in China.
Guo P; Liu T; Zhang Q; Wang L; Xiao J; Zhang Q; Luo G; Li Z; He J; Zhang Y; Ma W
PLoS Negl Trop Dis; 2017 Oct; 11(10):e0005973. PubMed ID: 29036169
[TBL] [Abstract][Full Text] [Related]
17. Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma.
Książek W; Gandor M; Pławiak P
Comput Biol Med; 2021 Jul; 134():104431. PubMed ID: 34015670
[TBL] [Abstract][Full Text] [Related]
18. Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning.
Yang R; Plasek JM; Cummins MR; Sward KA
Comput Inform Nurs; 2021 May; 39(5):273-280. PubMed ID: 33208628
[TBL] [Abstract][Full Text] [Related]
19. Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.
Shimoda A; Ichikawa D; Oyama H
Int J Med Inform; 2018 Mar; 111():90-99. PubMed ID: 29425640
[TBL] [Abstract][Full Text] [Related]
20. Prediction and detection models for acute kidney injury in hospitalized older adults.
Kate RJ; Perez RM; Mazumdar D; Pasupathy KS; Nilakantan V
BMC Med Inform Decis Mak; 2016 Mar; 16():39. PubMed ID: 27025458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]