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Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Definitive diagnosis of dementia in the ADAMS sample was assigned by a consensus of clinical experts [11], using international diagnostic criteria for dementia (cognitive or behavioral disorders associated with significant decline in social or occupational

Laurent Cleret de Langavant, Eleonore Bayen, Kristine Yaffe

J Med Internet Res 2018;20(7):e10493

Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment

Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment

Public health surveillance uses ANN to forecast diseases distributions, whereas Guan et al (2004) used ANN to forecast incidents of hepatitis.

Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi

JMIR Public Health Surveill 2019;5(1):e11357

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Another GA was used in the work of Catalogna et al to support an ANN controller [29].

Ivan Contreras, Josep Vehi

J Med Internet Res 2018;20(5):e10775

Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation

Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation

/498 (97.39)–0.06279NontoxicB44:02Consensus (ann/smm)0.06N protein9KPRQKRTAT487/498 (97.79)–0.20542NontoxicB07:02Consensus(ann/smm/comblib_sidney2008)0.1orf109MGYINVFAF477/480 (99.38)–0.09452NontoxicB35:01Consensus(ann/smm/comblib_sidney2008)0.1orf1010GYINVFAFPFe232

Sukrit Srivastava, Sonia Verma, Mohit Kamthania, Rupinder Kaur, Ruchi Kiran Badyal, Ajay Kumar Saxena, Ho-Joon Shin, Michael Kolbe, Kailash C Pandey

JMIR Bioinformatics Biotechnol 2020;1(1):e19371

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

The study used 6 indicators, that is ozone, carbon monoxide, PM10 (particulate matter of 10 μm in diameter or smaller), PM25 (particulate matter less than 2.5 μm in diameter), and sulfur dioxide, from Athens, Greece to train the ANN model.

Junfeng Peng, Chuan Chen, Mi Zhou, Xiaohua Xie, Yuqi Zhou, Ching-Hsing Luo

JMIR Med Inform 2020;8(3):e13075