System and Method for Predicting and Summarizing Medical Events from Electronic Health Records

Patent metadata

Field	Value
Publication number	US-2019034591-A1
Application number	US-201715690721-A
Country	US
Kind code	A1
Filing date	Aug 30, 2017
Priority date	Jul 28, 2017
Publication date	Jan 31, 2019
Grant date	—

Abstract

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.

First claim

We claim:

1 . A system comprising, in combination,
a) a computer executing one or more deep learning models trained on aggregated health records converted into the single standardized data structure format and in an ordered arrangement per patient to predict one or more future clinical events and summarize pertinent past medical events related to the predicted one or more future clinical events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order; and b) a healthcare provider-facing interface of an electronic device for use by a healthcare provider treating the patient configured to display the predicted one or more future clinical events and the pertinent past medical events of the patient.

2 . The system of claim 1 , wherein the interface of the electronic device includes a display of:
(1) an alert to the one or more future clinical events, (2) key medical problems or conditions related to the alert, and (3) notes or excerpts thereof related to the alert.

3 . The system of claim 2 , wherein at least one of the one or more deep learning models each contain an attention mechanism indicating how much attention the at least one of the one or more models gave to elements in the electronic health record to predict the one or more future clinical events and summarize pertinent past medical events related to the predicted one or more future clinical events, and wherein the display of the notes or excerpts thereof are displayed in a manner indicating results from the application of the attention mechanism.

4 . The system of claim 2 , wherein the display further comprises a display of at least one of inferred information from the patient electronic health record and a timeline of a probability or risk of certain events occurring in the future.

5 . The system of claim 1 , wherein the display permits a user of the electronic device to select one of the key problems or conditions and the selection triggers further display of information pertinent to the selected key problem or condition.

6 . The system of claim 4 , wherein the further display comprises display of medications prescribed to the patient and notes or excerpts thereof related to the selected key problem or condition.

7 . The system of claim 3 , wherein the display of the notes or excerpts thereof indicating results from the application of the attention mechanism comprises display of the notes or excerpts thereof using at least one of the following to provide highlighting or gradations of emphasis on particular words, phrases or other text in the notes: font size, font color, shading, bold, italics, underline, strikethough, blinking, highlighting with color, and font selection.

8 . An electronic device having a healthcare provider facing interface displaying in substantial real time a display of a prediction of one or more future clinical events for at least one patient;
wherein the display further is configured to display elements comprising past medical events from an electronic health record which correspond to application of an attention mechanism on a predictive model operating on the electronic health record which are related to the prediction.

9 . The electronic device of claim 8 , wherein the elements of the electronic health record comprise notes or extracts thereof with highlighting or gradations of emphasis on particular words, phrases or other text in the notes.

10 . The electronic device of claim 8 , wherein the highlighting or gradations of emphasis comprise use of at least one of font size, font color, shading, bold, italics, underline, strikethough, blinking, highlighting with color, and font selection.

11 . The electronic device of claim 8 , wherein the electronic device comprises a workstation, a tablet computer, or a smartphone.

12 . The electronic device of claim 8 , wherein the predicted one or more future clinical events include at least one of unplanned transfer to intensive care unit, length of stay in a hospital greater than 7 days, unplanned readmission within 30 days after discharge of the patient, inpatient mortality, primary diagnosis, a complete set of primary and secondary billing diagnoses, or atypical laboratory values, such as acute kidney injury, hypokalemia, hypoglycemia, and hyponeutrimia.

13 . The electronic device of claim 8 , wherein the interface is further configured to display a time line plotting at least one patient risk or probability of an event over time.

14 . The electronic device of claim 8 , wherein the interface is further configured to display a time line plotting at least one patient risk or probability of an event over time for a plurality of patients simultaneously.

15 . The electronic device of claim 8 , wherein the display of a prediction of one or more future clinical events is in the form of a display of an alert.

16 . A method of assisting a health care provider in providing care for a patient, comprising the steps of:
a) using a predictive model trained from aggregated electronic health records to generate (1) a prediction of a future clinical event for the patient and (2) identify pertinent past medical events from an input electronic health record for the patient; b) generate data related to both the prediction and the identified pertinent past medical events; and c) transmit the generated data to an electronic device used by the health care provider for display on the electronic device; wherein: the predictive model uses an attention mechanism to indicate how much attention the predictive model gave to elements in the input electronic health record to predict the future clinical event and identify pertinent past medical events and wherein the generated data includes the results of the attention mechanism.

17 . The method of claim 16 , wherein the pertinent past medical events include notes or excerpts thereof.

18 . The method of claim 16 , wherein the prediction is selected from the group consisting of: unplanned transfer to intensive care unit, length of stay in a hospital greater than 7 days, unplanned readmission within 30 days after discharge of the patient, inpatient mortality, primary diagnosis, a complete set of primary and secondary billing diagnoses, and atypical laboratory values.

19 . The method of claim 16 , wherein the generated data further comprises a time line of probability or risk of an event occurring over time.

20 . The method of claim 16 , wherein steps a), b) c) and d) are performed in real time for a multitude of patients simultaneously from a multitude of input electronic health records; and wherein a health care provider caring for at least two of the multitude of patients receives the generated data in real time for the at least two patients, thereby assisting the health care provider in providing care for the at least two patients simultaneously and permitting prioritization in patient care for the at least two patients based on the respective predictions.

21 . The method of claim 16 , wherein the predictive model comprises an ensemble of deep learning models individually trained on aggregated electronic health records, at least one of which incorporates the attention mechanism.

22 . The method of claim 21 , wherein the ensemble comprises (1) a Long-Short-Term Memory (LSTM) model, (2) a time aware Feed-Forward Model (FFM), and (3) an embedded boosted time-series model.

Assignees

• Google Inc

Inventors

• Mossin Alexander
• Rajkomar Alvin
• Oren Eyal
• Wilson James
• Wexler James
• Sundberg Patrik
• Dai Andrew
• Cui Yingwei
• Corrado Gregory
• Yee Hector
• Marcus Jacob
• Dean Jeffrey
• Irvine Benjamin
• Chen Kai
• Zhang Kun
• Hardt Michaela
• Sun Xiaomi
• Hajaj Nissan
• Liu Peter
• Le Quoc
• Liu Xiaobing
• Zhang Yi

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G16H10/60 Primary

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G06N3/08

  Learning methods · CPC title