System and Method For Healthcare Outcome Predictions Using Medical History Categorical Data
A system and method for healthcare outcome predictions using medical history categorical data is provided. The system for healthcare outcome predictions using medical history categorical data comprising a computer system for receiving medical history categorical data, a healthcare outcome prediction engine stored on the computer system which, when executed by the computer system, causes the computer system to process the medical history categorical data to define a set of high-level constructs, calculate smoothed and thresholded Weight of Evidence tables for each high-level construct using training data, calculate an Evidence Ranked Sum value for each instance of each high-level construct based on the Weight of Evidence tables, and build predictive models based on the calculated Evidence Ranked Sum values.
1 . A system for healthcare outcome predictions using medical history categorical data comprising:
a computer system for receiving medical history categorical data;
a healthcare outcome prediction engine stored on the computer system which, when executed by the computer system, causes the computer system to:
process the medical history categorical data to define a set of high-level constructs;
calculate smoothed and thresholded Weight of Evidence tables for each high-level construct using training data;
calculate an Evidence Ranked Sum value for each instance of each high-level construct based on the Weight of Evidence tables; and
build predictive models based on the calculated Evidence Ranked Sum values.
2 . The system of claim 1 , wherein the medical history categorical data comprises ICD9 diagnostic and procedure codes.
3 . The system of claim 1 , wherein one or more of the high-level constructs are time-dependent.
4 . The system of claim 1 , wherein for each instance of a type of medical event in the training data, all categorical data within a time window are included in the Weight of Evidence tables.
5 . The system of claim 1 , wherein any values in the training data with counts below a threshold are dropped from the Weight of Evidence tables.
6 . The system of claim 1 , wherein the Evidence Ranked Sum value is a single scalar value summed from a list of Weight of Evidence values.
7 . A method for healthcare outcome predictions using medical history categorical data comprising:
receiving at a computer system medical history categorical data;
processing the medical history categorical data using a healthcare outcome prediction engine executed by the computer system to define a set of high-level constructs built from medical history categorical data;
calculating using the healthcare outcome prediction engine smoothed and thresholded Weight of Evidence tables for each high-level construct using training data;
calculating using the healthcare outcome prediction engine an Evidence Ranked Sum value for each instance of each high-level construct based on the Weight of Evidence tables; and
building predictive models using the healthcare outcome prediction engine based on the calculated Evidence Ranked Sum values.
8 . The method of claim 7 , wherein the medical history categorical data comprises ICD9 diagnostic and procedure codes.
9 . The method of claim 7 , wherein one or more of the high-level constructs are time-dependent.
10 . The method of claim 7 , wherein for each instance of a type of medical event in the training data, all categorical data within a time window are included in the Weight of Evidence tables.
11 . The method of claim 7 , wherein any values in the training data with counts below a threshold are dropped from the Weight of Evidence tables.
12 . The method of claim 7 , wherein the Evidence Ranked Sum value is a single scalar value summed from a list of Weight of Evidence values.
13 . A non-transitory computer-readable medium having computer-readable instructions stored thereon which, when executed by a computer system, cause the computer system to perform the steps of:
receiving at the computer system medical history categorical data;
processing the medical history categorical data using a healthcare outcome prediction engine executed by the computer system to define a set of high-level constructs built from medical history categorical data;
calculating using the healthcare outcome prediction engine smoothed and thresholded Weight of Evidence tables for each high-level construct using training data;
calculating using the healthcare outcome prediction engine an Evidence Ranked Sum value for each instance of each high-level construct based on the Weight of Evidence tables; and
building predictive models using the healthcare outcome prediction engine based on the calculated Evidence Ranked Sum values.
14 . The computer-readable medium of claim 13 , wherein the medical history categorical data comprises ICD9 diagnostic and procedure codes.
15 . The computer-readable medium of claim 13 , wherein one or more of the high-level constructs are time-dependent.
16 . The computer-readable medium of claim 13 , wherein for each instance of a type of medical event in the training data, all categorical data within a time window are included in the Weight of Evidence tables.
17 . The computer-readable medium of claim 13 , wherein any values in the training data with counts below a threshold are dropped from the Weight of Evidence tables.
18 . The computer-readable medium of claim 13 , wherein the Evidence Ranked Sum value is a single scalar value summed from a list of Weight of Evidence values.