-
1.
公开(公告)号:US20190034589A1
公开(公告)日:2019-01-31
申请号:US15690703
申请日:2017-08-30
申请人: Google Inc.
发明人: Kai Chen , Patrik Sundberg , Alexander Mossin , Nissan Hajaj , Kurt Litsch , James Wexler , Yi Zhang , Kun Zhang , Jacob Marcus , Eyal Oren , Hector Yee , Jeffrey Dean , Michaela Hardt , Benjamin Irvine , James Wilson , Andrew Dai , Peter Liu , Xiaomi Sun , Quoc Le , Xiaobing Liu , Alvin Rajkomar , Gregory Corrado , Gerardo Flores , Yingwei Cui , Gavin Duggan
摘要: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.
-
2.
公开(公告)号:US20190034591A1
公开(公告)日:2019-01-31
申请号:US15690721
申请日:2017-08-30
申请人: Google Inc.
发明人: Alexander Mossin , Alvin Rajkomar , Eyal Oren , James Wilson , James Wexler , Patrik Sundberg , Andrew Dai , Yingwei Cui , Gregory Corrado , Hector Yee , Jacob Marcus , Jeffrey Dean , Benjamin Irvine , Kai Chen , Kun Zhang , Michaela Hardt , Xiaomi Sun , Nissan Hajaj , Peter Liu , Quoc Le , Xiaobing Liu , Yi Zhang
摘要: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order. An electronic device configured with a healthcare provider-facing interface displays the predicted one or more future clinical events and the pertinent past medical events of the patient.
-