Self-improving classification system

Abstract

A self-improving classification system classifies specimens based on class identifiers. The system stores specimen profiles in a database that is updated with additional specimen profiles and with follow-up data that corrects classification of specimens that were initially incorrectly classified. Algorithms use the updated database to discover new class identifiers, modify thresholds of known class identifiers, and drop unnecessary class identifiers to improve classification of specimens.

1 . A self-improving method of classifying a specimen, the method comprising:
 extracting one or more selected from the group consisting of proteomic information and genomic information from the specimen to determine at least one proteomic parameter or genomic parameter;   using a computer to develop an algorithm for classifying specimens based on class identifiers, wherein the class identifiers include at least one physiological or demographic parameter and one or more selected from the group consisting of proteomic parameters and genomic parameters;   using a computer to generate a specimen profile from the specimen by analyzing the class identifiers of the specimen and storing the specimen profile in a database;   classifying the specimen into a class based on the specimen profile and the class identifiers using the algorithm;   determining if reclassification of the specimen is necessary;   refining the algorithm based on a refinement of the class identifiers; and   reclassifying the specimen if necessary.   
     
     
         2 . (canceled) 
     
     
         3 . The self-improving method of classifying a specimen of  claim 1 , wherein the specimen profile further includes data selected from a group consisting of demographic, historical, psychiatric, pathological, metabolic, and lipidomic information and the data includes electrical information. 
     
     
         4 . The self-improving method of classifying a specimen of  claim 3 , wherein the data includes electrical information collected from an implantable medical device. 
     
     
         5 . The self-improving method of classifying a specimen of  claim 1 , wherein at least one of the class identifiers is based on a class distinction of previously classified specimens. 
     
     
         6 . The self-improving method of classifying a specimen of  claim 1 , wherein the specimen is an animal. 
     
     
         7 . The self-improving method of classifying a specimen of  claim 1 , wherein the specimen is a person. 
     
     
         8 . The self-improving method of classifying a specimen of  claim 1 , wherein the specimen is selected from a group consisting of a plant, tissue sample, bodily fluid, cellular sample, and subcellular sample. 
     
     
         9 . (canceled) 
     
     
         10 . The self-improving method of classifying a specimen of  claim 1 , wherein refining the algorithm comprises: modifying threshold levels to increase sensitivity and specificity of classification. 
     
     
         11 . The self-improving method of classifying a specimen of  claim 1 , wherein refining the algorithm comprises: identifying a new class identifier. 
     
     
         12 . The self-improving method of classifying a specimen of  claim 1 , wherein refining the algorithm comprises: dropping a class identifier of the class identifiers. 
     
     
         13 . The self-improving method of classifying a specimen of  claim 1 , wherein the algorithm is based on a plurality of the class identifiers. 
     
     
         14 - 33 . (canceled) 
     
     
         34 . A self-improving system of classifying specimens, the system comprising:
 a database containing specimen profiles that are clustered into classes based on class identifiers, wherein class identifiers include at least one physiological or demographic parameter and one or more selected from the group consisting of proteomic parameters and genomic parameters;   a means for classifying specimens using a computer and a first algorithm based on the class identifiers, and placing the specimens in classes based on the class identifiers; and   a means for improving the first algorithm based on follow-up data regarding accuracy of classification of the specimens.   
     
     
         35 . The self-improving system of  claim 34 , wherein the means for improving the first algorithm includes:
 a means for deriving a class table using a second algorithm; and   a means for modifying thresholds of the class identifiers, identifying new class identifiers, and dropping class identifiers based upon analysis of the class table.   
     
     
         36 - 38 . (canceled) 
     
     
         39 . A self-improving method of identifying patients who are candidates for treatment with a medical device, the method comprising:
 creating a database;   gathering data from patients, the data including class identifiers wherein the class identifiers include at least one physiological or demographic parameter and one or more selected from the group consisting of proteomic parameters and genomic parameters;   analyzing the data using a computer and an algorithm, the algorithm identifying patients who are candidates for treatment with the medical device;   adding the data to the database;   correcting identification of the patients based upon follow-up data indicating a response of the patients to the medical device; and   updating the database and the algorithm using corrected identification of the patients.   
     
     
         40 . The self-improving method of  39 , wherein the follow-up data further comprises: an operational history of the medical device. 
     
     
         41 . The self-improving method of  claim 39 , wherein the data and follow-up data include one or more of a group consisting of intracardiac electrogram data, pseudo-ECG data, heart rate data, physical activity data, minute volume data, pacing activity data, intracardiac blood pressure data, transthoracic impedance data, patient activation of the medical device data, stimulation threshold and trends data, lead tip accelerometer data, and venous blood oxygen saturation data. 
     
     
         42 . The self-improving method of  claim 39 , further comprising: processing the data and the follow-up data to extract additional information. 
     
     
         43 . The self-improving method of  claim 42 , wherein the additional information includes heart rate variability, QRS width, QT interval measurements, T-wave alternans, frequency of ventricular defibrillation shocks, duration and frequency of atrial fibrillation, atrial fibrillation burden, and correlation between syncopic episodes and heart rate drop data. 
     
     
         44 . The self-improving method of  claim 39 , wherein the medical device is an implantable medical device. 
     
     
         45 . The self-improving method of  claim 39 , wherein the data and follow-up data are gathered from the medical device via telemetry. 
     
     
         46 - 54 . (canceled)