Generation of human readable explanations of data driven analytics

Patent metadata

Field	Value
Publication number	US-2020104733-A1
Application number	US-201816143802-A
Country	US
Kind code	A1
Filing date	Sep 27, 2018
Priority date	Sep 27, 2018
Publication date	Apr 2, 2020
Grant date	—

Abstract

Systems and method describe inputting a set of characteristic data to a machine learning model that was trained at least in part on a knowledge based data set. A predicted outcome is determined based on the output of the machine learning model and a subset of the knowledge based data set that includes terms corresponding to the set of characteristic data is identified. The predicted outcome and subset of the knowledge based data set is used to generate display information for an interface.

First claim

What is claimed is:

1 . A method comprising:
inputting a set of characteristic data to a machine learning model; determining a predicted outcome based on an output of the machine learning model; identifying a subset of a knowledge based data set that explains the predicted outcome; and generating display information for an interface, the display information comprising the predicted outcome and the subset of the knowledge based data set.

2 . The method of claim 1 , wherein identifying the subset of the knowledge based data comprises:
determining a first term in the characteristic data that is associated with the predicted outcome; and identifying a passage in the knowledge based data set that includes the first term and the predicted outcome.

3 . The method of claim 1 , wherein identifying the subset of the knowledge based data comprises:
determining a first term and a second term contributing to the determination of the predicted outcome; and identifying a passage in the knowledge based data set that includes the first term and the second term.

4 . The method of claim 1 , wherein identifying the subset of the knowledge based data set comprises determining a term in the set of characteristic data that is grouped with the predicted outcome in a topic model.

5 . The method of claim 1 , wherein the machine learning model is one of a topic model or a neural network.

6 . The method of claim 1 , wherein identifying the subset of the knowledge based data set comprises:
identifying a first passage of the knowledge based data set that includes the terms corresponding to the set of characteristic data; identifying a second passage of the knowledge based data set that includes the terms corresponding to the set of characteristic data; and selecting the first passage over the second passage based on a relationship of the terms within the first passage.

7 . The method of claim 1 , wherein the characteristic data is generated from medical admissions and the knowledge based data comprises medical texts.

8 . The method of claim 1 , wherein identifying a subset of the knowledge based data comprises:
identifying a first passage describing a relationship between a first term of the set of characteristic data and the predicted outcome; and identifying a second passage describing a relationship between a second term of the set of characteristic data and the predicted outcome.

9 . The method of claim 1 , further comprising:
inputting a second set of characteristic data to the machine learning model; determining a predicted outcome based on a second output of the machine learning model; and in response to determining that no subset of the knowledge based data includes terms corresponding to the set of characteristic data and the predicted outcome, rejecting the predicted outcome.

10 . A non-transitory computer-readable medium having instructions stored thereon that, when executed by a processing device, cause the processing device to:
input a medical admission record of a patient to a machine learning model to generate an output; determine a predicted outcome based on the output of the machine learning model; identify a subset of a set of medical texts that includes terms corresponding to at least some of the terms of the medical admission record; and generating an interface comprising the predicted outcome and the subset of the set of medical texts.

11 . The non-transitory computer-readable medium of claim 10 , wherein to identify the subset of the set of medical texts, the processing device is further to:
determine a first term in the medical admission record that is associated with the predicted outcome; and identify a passage in the set of medical texts that includes the first term and the predicted outcome.

12 . The non-transitory computer-readable medium of claim 10 , wherein to identify the subset of the set of medical texts, the processing device is further to:
determine a first term and a second term contributing to the determination of the predicted outcome; and identifying a passage in the set of medical texts that includes the first term and the second term.

13 . The non-transitory computer-readable medium of claim 10 , wherein to identify the subset of the set of medical texts, the processing device is further to determine a term in the medical admission record that is grouped with the predicted outcome in a topic model.

14 . The non-transitory computer-readable medium of claim 10 , wherein the machine learning model is one of a topic model or a neural network.

15 . The non-transitory computer-readable medium of claim 10 , wherein to identify the subset of the set of medical texts, the processing device is further to:
identify a first passage of the set of medical texts that includes the terms corresponding to the medical admission record; identify a second passage of the set of medical texts that includes the terms corresponding to the medical admission record; and selecting the first passage over the second passage based on a relationship of the terms within the first passage.

16 . The non-transitory computer-readable medium of claim 10 , wherein to identify the subset of the set of medical texts, the processing device is further to:
identify a first passage describing a relationship between a first term of the medical admission record and the predicted outcome; and identifying a second passage describing a relationship between a second term of medical admission record and the predicted outcome.

17 . A system comprising:
a memory device; and a processing device operatively coupled to the memory device, wherein the processing device is to:
receive a predicted outcome generated by a machine learning system based on a set of characteristic data;
identify, from a set of knowledge based data, a plurality of documents explaining the predicted outcome;
select a passage from one of the plurality of documents; and
generate an explanatory interface for the prediction based on the selected passage.

18 . The system of claim 17 , wherein to select a passage from one of the plurality of documents the processing device is further to:
determine a first term in the characteristic data that is associated with the predicted outcome; and identify a passage in the plurality of documents that includes the first term and the predicted outcome.

19 . The system of claim 17 , wherein to identify the subset of the knowledge based data the processing device is further to:
determine a first term and a second term contributing to the determination of the predicted outcome; and identify a passage in the knowledge based data set that includes the first term and the second term.

20 . The system of claim 17 , wherein the machine learning model is one of a topic model or a neural network.

Assignees

• Palo Alto Res Ct Inc

Inventors

• Bart Evgeniy

Classifications

• G16H50/20 Primary

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G06N3/02

  Neural networks · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N5/045 Primary

  Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence · CPC title

• G06N99/005

  Physics · mapped topic