Indexing of large scale patient set

• US 9,355,105 B2
• Filed: 12/19/2012
• Issued: 05/31/2016
• Est. Priority Date: 12/19/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Wang, Fei, Wang, Jun
• Primary Examiner(s): Fleurantin, Jean B
• Assistant Examiner(s): Sultana, Nargis
• Application Number:
  US13/719,427
• Publication Number:
  US 20140172869A1
• Time in Patent Office: 1,259 Days
• Field of Search: 707/741
• US Class Current: 1/1
• CPC Class Codes: G06F 16/13 File access structures, e.g. distributed indices arrangements of input from, or output to, record carriers G06F3/06
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees
• CPC Class Codes: G06F 16/278 Data partitioning, e.g. horizontal or vertical partitioning
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/31 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/316 Indexing structures
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Systems and methods for indexing data include formulating an objective function to index a dataset, a portion of the dataset including supervision information. A data property component of the objective function is determined, which utilizes a property of the dataset to group data of the dataset. A supervised component of the objective function is determined, which utilizes the supervision information to group data of the dataset. The objective function is optimized using a processor based upon the data property component and the supervised component to partition a node into a plurality of child nodes.

Claims

1. A method for indexing data, comprising:

formulating an objective function to index a dataset, a portion of the dataset including supervision information;

determining a data property component of the objective function which utilizes a property of the dataset to group data of the dataset;

determining a supervised component of the objective function which utilizes the supervision information to group data of the dataset; and

optimizing the objective function using a processor based upon the data property component and the supervised component to partition a node into a plurality of child nodes,

wherein the objective function includes a tradeoff parameter to balance contributions from the data property component and the supervision component,

wherein determining the data property component includes maximizing a variance of the dataset, and

wherein maximizing the variance includes applying maximum variance unfolding, the maximum variance unfolding comprising forming a projection as an inner product of a data matrix and a learned partition hyperplane and maximizing overall pairwise distances in a projected space.

2. The method as recited in claim 1, wherein optimizing the objective function includes recursively optimizing the objective function at each node to provide a binary space partitioning tree.

3. The method as recited in claim 1, wherein maximizing the variance includes applying principal component analysis, the principal component analysis comprising forming a data covariance matrix having a plurality of eigenvectors and determining a largest eigenvector from among the plurality of eigenvectors.

4. The method as recited in claim 1, wherein determining the data property component includes clustering data around a median of the dataset.

5. The method as recited in claim 1, wherein determining the supervised component includes minimizing pairwise distance of similar data identified by the supervision information and maximizing pairwise distance of dissimilar data identified by the supervision information.

6. The method as recited in claim 1, wherein determining the supervised component includes applying the dataset as a linear prediction function.

7. The method as recited in claim 1, wherein the supervision information includes must-links indicating pairs of data that are similar to each other and cannot-links indicating pairs of data that are dissimilar to each other.

8. The method as recited in claim 1, wherein the dataset includes patient medical information.