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Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients
Conclusions: Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients.
Source: Journal of Medical Internet Research - April 18, 2017 Category: Journals (General) Authors: Eunjeong Park Hyuk-Jae Chang Hyo Suk Nam Source Type: research

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning
Conclusions: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.
Source: Journal of Medical Internet Research - January 30, 2018 Category: General Medicine Authors: Chengyin Ye Tianyun Fu Shiying Hao Yan Zhang Oliver Wang Bo Jin Minjie Xia Modi Liu Xin Zhou Qian Wu Yanting Guo Chunqing Zhu Yu-Ming Li Devore S Culver Shaun T Alfreds Frank Stearns Karl G Sylvester Eric Widen Doff McElhinney Xuefeng Ling Source Type: research