公开(公告)号：US20210358579A1

公开(公告)日：2021-11-18

申请号：US16618656

申请日：2017-09-29

申请人： Google LLC

发明人： Kai Chen ,  Eyal Oren ,  Hector Yee ,  James Wilson ,  Alvin Rajkomar ,  Michaela Hardt

IPC分类号： G16H10/60 ,  G06N20/00

摘要： A method is described for training a predictive model which increases the interpretability and trustworthiness of the model for end-users. The model is trained from data having multitude of features. Each feature is associated with a real value and a time component. Many predicates (atomic elements for training the model) are defined as binary functions operating on the features, and typically time sequences of the features or logical combinations thereof. The predicates can be limited to those functions which have human understandability or encode expert knowledge relative to a predication task of the model. We iteratively train a boosting model with input from an operator or human-in-the-loop. The human-in-the-loop is provided with tools to inspect the model as it is iteratively built and remove one or more of the predicates in the model, e.g. if it does not have indicia of trustworthiness, is not causally related to a prediction of the model, or is not understandable. We repeat the iterative process several times ultimately generate a final boosting model. The final model is then evaluated, e.g., for accuracy, complexity, trustworthiness and post-hoc explainability.

公开(公告)号：US11935634B2

公开(公告)日：2024-03-19

申请号：US15690721

申请日：2017-08-30

申请人： Google LLC

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 Junteng Liu ,  Quoc Le ,  Xiaobing Liu ,  Yi Zhang

IPC分类号： G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G16H10/60 ,  G16H15/00 ,  G16H50/30 ,  G16H50/20

CPC分类号： G16H10/60 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G16H15/00 ,  G16H50/30 ,  G16H50/20

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.