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   ^{2, 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   ² , MD ;   Eric Widen   ³ , MHA ;   Doff McElhinney   ^{4, 5 *} , MD ;   Xuefeng Ling   ^{2, 5, 11 *} , PhD

¹ Department of Health Management, Hangzhou Normal University, Hangzhou, China

² Department of Surgery, Stanford University, Stanford, CA, United States

³ HBI Solutions Inc, Palo Alto, CA, United States

⁴ Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, United States

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

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

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

⁸ China Electric Power Research Institute, Beijing, China

⁹ School of Management, Zhejiang University, Hangzhou, China

¹⁰ HealthInfoNet, Portland, ME, United States

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

*these authors contributed equally