System and method for using classification trees to predict rare events

• US 20100076799A1
• Filed: 09/25/2008
• Published: 03/25/2010
• Est. Priority Date: 09/25/2008
• Status: Abandoned Application

• Current Assignee: Air Products and Chemicals Incorporated (Linde Plc)
• Original Assignee: Air Products and Chemicals Incorporated (Linde Plc)
• Inventors: Mehta, Sanjay, Barbieri, Robert S., Neogi, Debashis, Petit, Cecilia Anna Paulette, Lewis, Charles Roland, Waring, Malcolm Merritt, Magent, Michael Andrew, Jenkins, Jean, Glick, Gregory Robert, Toth, Michael S.
• Application Number:
  US12/284,943
• Publication Number:
  US 20100076799A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Systems and methods are provided for predicting rare events, such as hospitalization events. A set of data records, each containing multiple attributes with one or more values (which may include an “unknown” value), may represent a root node of a decision tree. This root node may be partitioned based on one of the attributes, such that the concentration (e.g., “purity”) of a relevant outcome (e.g., the rare event) is increased in one node and decreased in another. This process may be repeated until a decision tree with sufficiently pure leaf nodes is created. This “purified” decision tree may then be used to predict one or more rare events.

Claims

1. A method, comprising:

loading a plurality of data records, wherein each data record has one or more attributes, wherein the plurality of data records include a first group;

assigning a relevant event to be predicted;

selecting at least one of the one or more attributes;

creating a plurality of subgroups associated with the first group, wherein each data record associated with the first group is associated with at least one subgroup, wherein the associating for each record is based at least in part on a respective value associated with the selected attribute; and

repeating the selecting and creating until a concentration of positive outcomes for the relevant event is sufficient.

2. The method of claim 1, wherein sufficient includes a user defined threshold.

3. The method of claim 1, wherein the repeating includes measuring a difference between a concentration attained before the repeating and a concentration attained after the repeating, and wherein sufficient includes the difference being below a threshold.

4. The method of claim 1, wherein the first group is a root node of a decision tree and the plurality of subgroups are child nodes of the decision tree.

5. The method of claim 4, wherein the decision tree is a binary tree.

6. The method of claim 1, wherein the relevant event is a hospitalization event within a timeframe.

7. The method of claim 1, wherein the plurality of data records includes health related records.

8. The method of claim 1, further comprising:

using at least the first group and the associated plurality of subgroups to predict a probability of the relevant event occurring within a timeframe.

9. The method of claim 8, wherein the relevant event is associated with an entity, and wherein the using includes applying the first group and the associated plurality of subgroups to a dataset, wherein the dataset is associated with the entity.

10. A system, comprising:

a memory configured to load a plurality of data records, wherein each data record has one or more attributes, wherein the plurality of data records include a first group;

a processor configured to assign a relevant event to be predicted;

the processor configured to select at least one of the one or more attributes;

the processor configured to create a plurality of subgroups associated with the first group, wherein each data record associated with the first group is associated with at least one subgroup, wherein the associating for each record is based at least in part on a respective value associated with the selected attribute;

the processor further configured to repeat the selecting and creating until a concentration of positive outcomes for the relevant event is sufficient.

11. The system of claim 10, wherein sufficient includes a user defined threshold.

12. The system of claim 10, wherein the repeating includes measuring a difference between a concentration attained before the repeating and a concentration attained after the repeating, and wherein sufficient includes the difference being below a threshold.

13. The system of claim 10, wherein the first group is a root node of a decision tree and the plurality of subgroups are child nodes of the decision tree.

14. The system of claim 13, wherein the decision tree is a binary tree.

15. The system of claim 10, wherein the relevant event is a hospitalization event within a timeframe.

16. The system of claim 10, wherein the plurality of data records includes health related records.

17. The system of claim 10, further comprising:

the processor configured to predict a probability of the relevant event occurring within a timeframe using at least the first group and the associated plurality of subgroups.

18. The system of claim 17, wherein the relevant event is associated with an entity, and wherein the using includes applying the first group and the associated plurality of subgroups to a dataset, wherein the dataset is associated with the entity.

19. A computer-readable storage medium encoded with instructions configured to be executed by a processor, the instructions which, when executed by the processor, cause the performance of a method, comprising:

loading a plurality of data records, wherein each data record has one or more attributes, wherein the plurality of data records include a first group;

assigning a relevant event to be predicted;

selecting at least one of the one or more attributes;

creating a plurality of subgroups associated with the first group, wherein each data record associated with the first group is associated with at least one subgroup, wherein the associating for each record is based at least in part on a respective value associated with the selected attribute; and

repeating the selecting and creating until a concentration of positive outcomes for the relevant event is sufficient.