System and method for deriving a hierarchical event based database optimized for clinical applications

• US 7,788,202 B2
• Filed: 02/26/2007
• Issued: 08/31/2010
• Est. Priority Date: 02/26/2007
• Status: Expired due to Fees

• Current Assignee: ServiceNow Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Hennessy, Richard A., Kraemer, James R., Friedlander, Robert R.
• Primary Examiner(s): Jalil; Neveen Abel
• Assistant Examiner(s): Peach; Polina
• Application Number:
  US11/678,997
• Publication Number:
  US 20080208903A1
• Time in Patent Office: 1,282 Days
• Field of Search: 707/100, 707/200, 707/14, 707/15, 707/2
• US Class Current: 706/47
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 2216/03 Data mining

Abstract

A computer implemented method, apparatus, and computer usable program code for inferring a probability of a first inference absent from a database at which a query regarding the inference is received. Each datum of the database is conformed to the dimensions of the database. Each datum of the plurality of data has associated metadata and an associated key. The associated metadata includes data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum. The query is used as a frame of reference for the search. The database returns a probability of the correctness of the first inference based on the query and on the data.

Claims

1. A computer implemented method for inferring a probability of an I^th inference relating to a clinical application, wherein I is an integer reflecting how many times a recursion process has been conducted, the computer implemented method comprising:

receiving a I^th query at a database, on a data processing system, regarding an I^th fact relating to the clinical application, wherein the I^th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process, wherein the I^th inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum;

establishing the I^th fact as a frame of reference for the query, by a processing unit of the data processing system, wherein the frame of reference is used to determine data to be searched and rules to apply to the I^th query;

mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact;

applying a I^th set of rules to the query, by the processing unit, wherein the I^th set of rules are determined for the query according to a J^th set of rules, wherein J is equal to I-1, wherein the I^th set of rules determine how the plurality of data are to be compared to the I^th fact, wherein the I^th set of rules is prioritized, and wherein the I^th set of rules determine a I^th search space for the I^th query including the associated metadata and associated key, wherein the J^th set of rules is a rule set used in a previous iteration of the recursive process;

executing the I^th query, by the processing unit, to create the probability of the I^th inference, wherein the probability of the I^th inference is determined from comparing the first I^th search space according to the I^th set of rules;

automatically generating cohort data for the I^th fact; and

storing the probability of the I^th inference and the cohort data for the I^th fact by the processing unit in a memory element of the data processing system, wherein the I^th inference and the cohort data are stored in the database at an atomic level; and

wherein the clinical application is selected from the group consisting of determining a cause of a disease, determining a proximal cause of a disease, determining a cause of a medical condition, determining a proximal cause of a medical condition, disease state management, medical condition management, determining a pattern of at least one disease in a plurality of patients, determining a pattern of at least one medical condition in a plurality of patients, selecting a treatment plan for a particular patient, determining a genetic factor in relation to a disease, determining a genetic factor in relation to a medical condition, epidemic management, disease containment management, quality control in a medical facility, quality analysis in the medical facility, and monitoring public health.

2. The computer implemented method of claim 1 wherein the method repeats until a threshold is reached, wherein the threshold is one of a number of recursions and a probability of inference.

3. The computer implemented method of claim 1 wherein the second set of rules is selected from the group consisting of a heuristic set of rules, a statistical set of rules, a neural network, rules created by user input, and combinations thereof.

4. The computer implemented method of claim 1 wherein the database accesses a sub-system when the I^th query is executed, and wherein the sub-system includes ontology information and taxonomy information regarding each associated metadata.

5. The computer implemented method of claim 1 further comprising:

entering the first inference into the database as a first additional datum;

entering the probability of the first inference into the database as a second additional datum;

entering a categorization of the first inference into the database as a third additional datum; and

entering a categorization of the probability of the first inference into the database as a fourth additional datum.

6. The computer implemented method of claim 1 wherein the I^th inference comprises at least one of an event, a fact, a pattern of events, a group of events, and a group of facts.

7. The computer implemented method of claim 1 wherein the metadata further comprises user-defined data.

8. The computer implemented method of claim 1 further comprising:

identifying an additional cohort associated with the fact.

9. The computer implemented method of claim 8 wherein the identifying an additional cohort associated with the I^th fact is performed using at least one of a clustering algorithm, Bayesian statistics, user defined rules, and a combination thereof.

10. The computer implemented method of claim 1 wherein the I^th set of rules includes rules for adjusting the probability of the inference based on background data.

11. The computer implemented method of claim 1 wherein the data regarding hierarchies further comprises how the corresponding datum is categorized with other data in the plurality of data.

12. The computer implemented method of claim 1 further comprising:

importing additional data into the plurality of data, wherein the additional data initially is not associated with metadata and the additional data does not conform to the dimensions of the database;

conforming the additional data to the dimensions of the database; and

associating metadata and a key with each datum of the additional data.

13. The computer implemented method of claim 12 wherein the additional data is imported according to a technique selected from the group consisting of federation and extraction, transformation, and loading.

14. A computer implemented method for building a database capable of inferring a probability of I^th inference relating to a clinical application, wherein I is an integer reflecting how many times a recursion process has been conducted, the computer implemented method comprising:

establishing a database structure in a memory element of a data processing system, wherein the I^th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process, wherein the database structure is adapted to receive a plurality of divergent data, wherein in the database the plurality of divergent data includes a plurality of cohort data, wherein the database is adapted such that each datum of the database is conformed to the dimensions of the database, wherein the database is further adapted such that each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum;

mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact;

establishing a J^th set of rules, in the memory element of the data processing system for the database structure, the J^th set of rules comprising rules for determining a I^th set of rules to be applied to an I^th query submitted to the database, wherein the I^th query is related to the clinical application, wherein the I^th set of rules determines that a fact submitted with the I^th query will serve as a frame of reference when executing the I^th query, wherein the I^th set of rules determines an I^th first search space of the inverted star schema for the I^th query, including the associated metadata and associated keys, wherein the J^th set of rules is a rule set used in a previous iteration of a recursive process, wherein the I^th set of rules is prioritized, and wherein the I^th set of rules are adapted to create the probability of the I^th inference, wherein the probability of the I^th inference is determined from comparing the I^th search space according to the I^th set of rules using the I^th fact as the frame of reference by a processing unit of the data processing system, wherein the frame of reference is used to determine data to be searched and rules to apply to the I^th query;

receiving a plurality of divergent data in the database by a processing unit on the data processing system;

conforming the plurality of divergent data to the dimensions of the database, by the processing unit, to form a plurality of conformed data;

associating the metadata and the key with each datum in the plurality of conformed data by the processing unit; and

storing the database structure in the memory element of the data processing system;

wherein the clinical application is selected from the group consisting of determining a cause of a disease, determining a proximal cause of a disease, determining a cause of a medical condition, determining a proximal cause of a medical condition, disease state management, medical condition management, determining a pattern of at least one disease in a plurality of patients, determining a pattern of at least one medical condition in a plurality of patients, selecting a treatment plan for a particular patient, determining a genetic factor in relation to a disease, determining a genetic factor in relation to a medical condition, epidemic management, disease containment management, quality control in a medical facility, quality analysis in the medical facility, and monitoring public health.

15. A database stored in a computer-readable storage medium, the database comprising:

a plurality of divergent data stored in a data structure on the computer-readable medium, wherein the computer-readable storage medium comprises memory elements, wherein the I^th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum;

computer usable program code stored in the computer-readable storage medium for establishing an I^th fact relating to a clinical application, received in an I^th query relating to the clinical application, as a frame of reference for the I^th query;

mathematically refocusing the database such that the fact is modeled as a first center of an inverted star schema, and modeling each datum of the plurality of data in the inverted star schema around the fact;

computer usable program code stored in the computer-readable storage medium for applying an I^th set of rules to the I^th query, wherein the I^th set of rules are determined for the I^th query according to a J^th set of rules, wherein J is equal to I-1, wherein the I^th set of rules determine how the plurality of data are to be compared to the I^th fact, and wherein the I^th set of rules determine a I^th search space of the inverted star schema for the I^th query, wherein the J^th set of rules is a rule set used in a previous iteration of a recursive process;

computer usable program code stored in the computer-readable storage medium for executing the I^th query to create a probability of an I^th inference, wherein the probability of the I^th inference is determined from comparing the I^th search space according to the I^th set of rules;

computer usable program code stored in the computer-readable storage medium for storing the probability of I^th first inference in the database;

wherein the clinical application is selected from the group consisting of determining a cause of a disease, determining a proximal cause of a disease, determining a cause of a medical condition, determining a proximal cause of a medical condition, disease state management, medical condition management, determining a pattern of at least one disease in a plurality of patients, determining a pattern of at least one medical condition in a plurality of patients, selecting a treatment plan for a particular patient, determining a genetic factor in relation to a disease, determining a genetic factor in relation to a medical condition, epidemic management, disease containment management, quality control in a medical facility, quality analysis in the medical facility, and monitoring public health.