# US 2019/0172564 A1 · Early cost prediction and risk identification

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- Number: US 2019/0172564 A1
- Document ID: US20190172564A1
- Status: Published application
- Kind: A1
- Application number: 15/831,426
- Filed: 2017-12-05
- Published: 2019-06-06
- Earliest claimed: 2017-12-05
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Rachita Chandra; Vijay S. Iyengar; Dmitriy A. Katz; Karthikeyan Natesan Ramamurthy; Emily A. Ray; Moninder Singh; Dennis Wei; Gigi Y. C. Yuen-Reed; Kevin N. Tran
- Web page: https://patentgrep.com/patent/US20190172564A1

## CPC

- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G06F 16/24522: {Translation of natural language queries to structured queries}
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A system may predict costs for a set of members by building and using a predictive pipeline. The pipeline may be built using a set of historical data for training members. A set of member-level features can be identified by performing empirical testing on the set of historical data. The trained configurable predictive pipeline can generate a set of predictive data for each member, using historical test data for a set of testing members. The system can then generate a predictive report for each set of predictive data.

## Claims

### Claim 1

A computer-implemented method for generating predictive data, the method comprising:
building, based on a set of historical training data for a set of training members, a trained configurable predictive pipeline;
identifying, based on empirical testing of the set of historical training data, a set of member-level features;
generating, using the trained configurable predictive pipeline and a set of historical test data for the set of testing members, a set of predictive data for each member in the set of testing members; and
generating, for the set of predictive data for each member, a predictive report, wherein the predictive report comprises the set of predictive data and a set of explanations, wherein the predictive report is customizable for various levels of granularity including cohort levels, and wherein the set of explanations provides details for each prediction in the report.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein the building comprises:
training, using a first subset of the set of historical training data, the configurable predictive pipeline;
predicting, using the configurable predictive pipeline, a predictive second subset of the set of historical training data;
comparing, with a second subset of the set of historical training data, the predictive second subset of the set of historical training data; and
modifying, based on the comparing, weighting in the configurable predictive pipeline.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein the method further comprises:
curating, prior to the building, the set of historical training data for the set of training members and the set of historical test data for the set of testing members.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, wherein the curating comprises data standardization and the use of sparse matrices, and wherein the data curation addresses temporal drifts in population.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein the details comprise data from a treatment course

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein the method further comprises: saving, to memory and responsive to the building the trained configurable predictive pipeline, a set of building configurations; and saving, responsive to the generating the set of predictive data for each member, a set of generating configurations, wherein each set of configurations is accessible in a modularized format.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein the building further comprises:
identifying, prior to the building and for the set of training members, a first subset of members and a second subset of members, wherein the first subset of members is identified based on a first duration of membership and the second subset of members is identified based on a second duration of membership;
training, for the first subset of members, a first subset model; and
training, for the second subset of members, a second subset model;
and wherein the generating the set of predictive data for each member further comprises:
generating, using the first subset model, a first subset of predictive data;
generating, using the second subset model, a second subset of predictive data; and
merging the first subset of predictive data and the second subset of predictive data into the set of predictive data for each member.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1, wherein the set of testing members is a subset of the set of training members.

### Claim 9

A system comprising:
a training module configured to build, based on a set of historical training data for a set of training members, a trained configurable predictive pipeline;
a feature identification module configured to identify, based on empirical testing of the set of historical training data, a set of member-level features;
a prediction module configured to generate, using the trained configurable predictive pipeline and a set of historical test data for the set of testing members, a set of predictive data for each member in the set of testing members, wherein the predictive data comprise predictions for each of the set of member-level features; and
a reporting module configured to:
generate, for the set of predictive data for each member in the set of testing members, a predictive report, wherein the predictive report comprises the set of predictive data and a set of explanations, and wherein the set of explanations provides details for each prediction in the report.

### Claim 10 (depends on 9)

The system of claim 9, wherein the training module is further configured to build the trained configurable predictive pipeline by:
training, using a first subset of the set of historical training data, the configurable predictive pipeline;
predicting, using the configurable predictive pipeline, a predictive second subset of the set of historical training data;
comparing, with a second subset of the set of historical training data, the predictive second subset of the set of historical training data; and
modifying, based on the comparing, weighting in the configurable predictive pipeline.

### Claim 11 (depends on 9)

The system of claim 9, wherein the training module is further configured to:
curate, before the building, the set of historical training data for the set of training members and the set of historical test data for the set of testing members.

### Claim 12 (depends on 11)

The system of claim 11, wherein the curating comprises data standardization and the use of sparse matrices.

### Claim 13 (depends on 9)

The system of claim 9, wherein the details comprise data from a treatment course.

### Claim 14 (depends on 9)

The system of claim 9, wherein the predictive report is configurable to varying levels of granularity including member level and cohort level reporting.

### Claim 15 (depends on 9)

The system of claim 9, wherein the building module is further configured to:
identify, prior to the building and for the set of training members, a first subset of members and a second subset of members, wherein the first subset of members is identified based on a first duration of membership and the second subset of members is identified based on a second duration of membership;
train, for the first subset of members, a first subset model; and
train, for the second subset of members, a second subset model;
and wherein the prediction module is further configured to generate the set of predictive data for each member in the set of testing members by:
generating, using the first subset model, a first subset of predictive data;
generating, using the second subset model, a second subset of predictive data; and
merging the first subset of predictive data and the second subset of predictive data into the set of predictive data for each member.

### Claim 16 (depends on 9)

The system of claim 9, wherein the set of testing members is a subset of the set of training members.

### Claim 17

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:
building, based on a set of historical training data for a set of training members, a trained configurable predictive pipeline;
identifying, based on empirical testing of the set of historical training data, a set of member-level features;
generating, using the trained configurable predictive pipeline and a set of historical test data for the set of testing members, a set of predictive data for each member in the set of testing members; and
generating, for the set of predictive data for each member, a predictive report, wherein the predictive report comprises the set of predictive data and a set of explanations, and wherein the set of explanations provides details for each prediction in the report.

### Claim 18 (depends on 17)

The computer program product of claim 17, wherein the building further comprises:
training, using a first subset of the set of historical training data, the configurable predictive pipeline;
predicting, using the configurable predictive pipeline, a predictive second subset of the set of historical training data;
comparing, with a second subset of the set of historical training data, the predictive second subset of the set of historical training data; and
modifying, based on the comparing, weighting in the configurable predictive pipeline.

### Claim 19 (depends on 17)

The computer program product of claim 17, wherein the building further comprises:
identifying, prior to the building and for the set of training members, a first subset of members and a second subset of members, wherein the first subset of members is identified based on a first duration of membership and the second subset of members is identified based on a second duration of membership;
training, for the first subset of members, a first subset model; and
training, for the second subset of members, a second subset model;
and wherein the generating the set of predictive data for each member further comprises:
generating, using the first subset model, a first subset of predictive data;
generating, using the second subset model, a second subset of predictive data; and
merging the first subset of predictive data and the second subset of predictive data into the set of predictive data for each member.

### Claim 20 (depends on 17)

The computer program product of claim 17, wherein the details comprise data from a treatment course.

### Claim 21

A computer-implemented method for generating predictive healthcare cost data, the method comprising:
identifying, based on empirical testing of a set of historical patient training data for a set of training members, a set of member-level features;
generating, using a trained configurable predictive healthcare cost pipeline and a set of historical patient test data for a set of testing members in a testing set, a set of predictive data for each member in the set of testing members; and
generating, for the set of predictive data for each member, a predictive report, wherein the predictive report comprises a set of medical risk factors, a predictive risk score, and a set of predictive costs.

### Claim 22 (depends on 21)

The computer-implemented method of claim 21, wherein the method further comprises: building, prior to the identifying and based on the set of historical patient training data for the set of training members, the trained configurable predictive healthcare cost pipeline, wherein the set of training members are a set of members enrolled in a particular health insurance program and wherein the set of historical patient training data and the set of historical patient testing data comprise high-dimensional data.

### Claim 23 (depends on 22)

The computer-implemented method of claim 22, wherein the high-dimensional data comprises patient demographic data, treatment course data, and diagnosis data.

### Claim 24

A system for generating predictive healthcare cost data comprising:
a computer readable storage medium with program instructions stored thereon; and
one or more processors configured to execute the program instructions to perform a method comprising:
building, based on a set of historical patient training data for a set of training members, a trained configurable predictive healthcare cost pipeline, wherein the set of training members are a set of members enrolled in a particular health insurance program;
identifying, based on empirical testing of the set of historical patient training data for the set of training members, a set of member-level features;
generating, using the trained configurable predictive healthcare cost pipeline and a set of historical patient test data for a set of testing members in a testing set, a set of predictive data for each member in the set of testing members; and
generating, for the set of predictive data for each member, a predictive report, wherein the predictive report comprises at set of medical risk factors, a predictive risk score, and a set of predictive costs.

### Claim 25 (depends on 24)

The system of claim 24, wherein the method further comprises curating, prior to the building, the set of historical patient training data for the set of training members and the set of historical patient test data for the set of testing members.