Published on in Vol 20, No 1 (2018): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9268, first published .
Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Chengyin Ye 1, 2*, PhD;  Tianyun Fu 3*, BS;  Shiying Hao 4, 5*, PhD;  Yan Zhang 6, MD;  Oliver Wang 3, BA;  Bo Jin 3, MS;  Minjie Xia 3, BS;  Modi Liu 3, MS;  Xin Zhou 7, MD;  Qian Wu 8, BS;  Yanting Guo 1, 9, BS;  Chunqing Zhu 3, MS;  Yu-Ming Li 7, MD;  Devore S Culver 10, MM;  Shaun T Alfreds 10, MBA;  Frank Stearns 3, MHA;  Karl G Sylvester 1, MD;  Eric Widen 3, MHA;  Doff McElhinney 4, 5*, MD;  Xuefeng Ling 1, 5, 11*, PhD

1 Department of Surgery , Stanford University, Stanford, CA, US

2 Department of Health Management , Hangzhou Normal University, Hangzhou , CN

3 HBI Solutions Inc , Palo Alto, CA, US

4 Department of Cardiothoracic Surgery , Stanford University, Stanford, CA, US

5 Clinical and Translational Research Program, Betty Irene Moore Children's Heart Center, Lucile Packard Children’s Hospital, Stanford, CA, US

6 Department of Oncology , The First Hospital of Shijiazhuang, Shijiazhuang , CN

7 Tianjin Key Laboratory of Cardiovascular Remodeling and Target Organ Injury , Pingjin Hospital Heart Center, Tianjin , CN

8 China Electric Power Research Institute , Beijing , CN

9 School of Management , Zhejiang University, Hangzhou , CN

10 HealthInfoNet , Portland, ME, US

11 Health Care Big Data Center, School of Public Health, Zhejiang University, Hangzhou , CN

*these authors contributed equally

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