Machine-learning based query construction and pattern identification

A computer program product comprising:

• a non-transitory computer readable storage medium readable by one or more processors in a distributed computing environment, and storing instructions for execution by the one or more processors for performing a method comprising:
• obtaining, by the one or more processors in a distributed computing environment, one or more machine-readable data sets related to a patient population diagnosed with a medical condition from one or more databases, wherein the medical condition is an orphan disease;
• based on a frequency of features in the one or more data sets, identifying, by the one or more processors, common features in the one or more data sets utilizing an identification method-selected from the group consisting of:
• weighting the common features based on frequency of occurrence in the one or more data sets, performing diffusion mapping, performing principal component analysis, performing recursive feature elimination, and utilizing a Random Forest to select the features, wherein if the selected identification method is weighting the common features, the common features comprise mutual information, and wherein identifying the common features in the one or more data sets utilizing the identification method comprises weighting the common features based on frequency of occurrence in the one or more data sets, wherein the identified common features are selected from a group of features consisting of:
• diagnoses, medications, provider visits, treatment locations, medical procedures, ICD-9 codes, ICD-10 codes, diagnosis codes, procedure codes, adjudicated claims, care providers, disorders, diseases, provider types, care facility types, patient demographics, and co-morbidities;
• generating, by the one or more processors, one or more patterns comprising a portion of the common features identified in the one or more machine-readable data sets related to the patient population diagnosed with the medical condition from the one or more databases, wherein the portion of the features comprises a smallest number of the common features that is a largest number of differentiating characteristics of the patient population diagnosed with the medical condition, wherein the portion of the common features is identified based on comparing members of a general population comprising the patient population diagnosed with the medical condition and additional members not in the patient population diagnosed with the medical condition, wherein a portion of the patient population diagnosed with the medical condition has the differentiating characteristics, and wherein the portion comprises fewer patients than the patient population diagnosed with the medical condition;
• generating, by the one or more processors, one or more machine learning algorithms based on the one or more patterns, the one or more machine learning algorithms to identify presence or absence of the given medical condition in an undiagnosed patient, wherein the generating the one or more machine learning algorithms is based on absence or presence of features comprising the one or more patterns in data related to the undiagnosed patient;
• utilizing, by the one or more processors, statistical sampling to compile a training set of data, wherein the training set comprises data from the one or more data sets and at least one additional data set comprising data related to a population without the medical condition, and wherein utilizing the statistical sampling comprises formulating and obtaining queries based on the data set and processing and responding to the queries, the processing comprising, for each query:
• evaluating, by the one or more processors, the query to determine if a prospective response to the query is a single value pulled from a single data set;
• based on determining that the prospective response to the query is the single value pulled from the single data set, assigning, by the one or more processors, the query to a given computing resource in the distributed computing environment;
• and
• based on determining that the prospective response to the query is not the single value pulled from the single data set, distributing, by the one or more processors, the query over a group of computing resources of the distributed computing environment to maximize efficiency, wherein the distributing comprises assigning each computing resource of the group of computing resources a portion of the query to execute in parallel with at least one other computing resource of the group of computing resources executing another portion of the query;
• tuning, by the one or more processors, the one or more machine learning algorithms by applying the one or more machine learning algorithms to the training set of data;
• and
• determining, by the one or more processors, based on applying the one or more machine learning algorithms to data related to the undiagnosed patient, a probability, wherein the probability is a numerical value indicating a percentage of commonality between the data related to the undiagnosed patient and the one or more patterns, wherein the probability indicates a probability that the undiagnosed patient will be diagnosed with the medical condition in the future.

A system comprising:

• one or more memory;
• one or more processors in communication with the memory;
• and
• program instructions executable by the one or more processors in a distributed computed environment via the one or more memory to perform a method, the method comprising:
• obtaining, by the one or more processors in a distributed computing environment, one or more machine-readable data sets related to a patient population diagnosed with a medical condition from one or more databases, wherein the medical condition is an orphan disease;
• based on a frequency of features in the one or more data sets, identifying, by the one or more processors, common features in the one or more data sets utilizing an identification method-selected from the group consisting of:
• weighting the common features based on frequency of occurrence in the one or more data sets, performing diffusion mapping, performing principal component analysis, performing recursive feature elimination, and utilizing a Random Forest to select the features, wherein if the selected identification method is weighting the common features, the common features comprise mutual information, and wherein identifying the common features in the one or more data sets utilizing the identification method comprises weighting the common features based on frequency of occurrence in the one or more data sets, wherein the identified common features are selected from a group of features consisting of:
• diagnoses, medications, provider visits, treatment locations, medical procedures, ICD-9 codes, ICD-10 codes, diagnosis codes, procedure codes, adjudicated claims, care providers, disorders, diseases, provider types, care facility types, patient demographics, and co-morbidities;
• generating, by the one or more processors, one or more patterns comprising a portion of the common features identified in the one or more machine-readable data sets related to the patient population diagnosed with the medical condition from the one or more databases, wherein the portion of the features comprises a smallest number of the common features that is a largest number of differentiating characteristics of the patient population diagnosed with the medical condition, wherein the portion of the common features is identified based on comparing members of a general population comprising the patient population diagnosed with the medical condition and additional members not in the patient population diagnosed with the medical condition, wherein a portion of the patient population diagnosed with the medical condition has the differentiating characteristics, and wherein the portion comprises fewer patients than the patient population diagnosed with the medical condition;
• generating, by the one or more processors, one or more machine learning algorithms based on the one or more patterns, the one or more machine learning algorithms to identify presence or absence of the given medical condition in an undiagnosed patient, wherein the generating the one or more machine learning algorithms is based on absence or presence of features comprising the one or more patterns in data related to the undiagnosed patient;
• utilizing, by the one or more processors, statistical sampling to compile a training set of data, wherein the training set comprises data from the one or more data sets and at least one additional data set comprising data related to a population without the medical condition, and wherein utilizing the statistical sampling comprises formulating and obtaining queries based on the data set and processing and responding to the queries, the processing comprising, for each query:
• evaluating, by the one or more processors, the query to determine if a prospective response to the query is a single value pulled from a single data set;
• based on determining that the prospective response to the query is the single value pulled from the single data set, assigning, by the one or more processors, the query to a given computing resource in the distributed computing environment;
• and
• based on determining that the prospective response to the query is not the single value pulled from the single data set, distributing, by the one or more processors, the query over a group of computing resources of the distributed computing environment to maximize efficiency, wherein the distributing comprises assigning each computing resource of the group of computing resources a portion of the query to execute in parallel with at least one other computing resource of the group of computing resources executing another portion of the query;
• tuning, by the one or more processors, the one or more machine learning algorithms by applying the one or more machine learning algorithms to the training set of data;
• and
• determining, by the one or more processors, based on applying the one or more machine learning algorithms to data related to the undiagnosed patient, a probability, wherein the probability is a numerical value indicating a percentage of commonality between the data related to the undiagnosed patient and the one or more patterns, wherein the probability indicates a probability that the undiagnosed patient will be diagnosed with the medical condition in the future.