摘要:
A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations are solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.
摘要:
Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.
摘要:
A plurality of actual outcome data points, including actual outcomes for a plurality of episodes of a process, are obtained for the process. A practitioner-independent baseline outcome is also obtained for the process. For each given one of the actual outcome data points, the given one of the actual outcome data points is equated to the practitioner entity-independent baseline outcome multiplied by a plurality of unknown participating practitioner entity outcome indices for each of a plurality of participating practitioner entities. Each of the participating practitioner entity outcome indices is raised to an exponent including a corresponding one of a plurality of unknown participating practitioner entity type indices, to obtain a plurality of equations. The plurality of equations arc solved to obtain estimated values of the unknown participating practitioner entity outcome indices and estimated values of the unknown participating practitioner entity type indices.
摘要:
A system and method for identifying unexpected utilization profiles at a patient level includes determining one or more clusters that have a profile based on patient profiles and building a representative model for each cluster including demographic and clinical information. Using the model, demographic and clinical characteristics are determined which form expected utilization cluster. An expected utilization cluster for each patient, which is derived from the demographic features and the clinical characteristics, is compared against an actual utilization profile for that patient to determine whether the actual utilization profile is unexpected.
摘要:
A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.
摘要:
A system and method for predicting patient prognosis includes a similarity module configured in program storage media to provide a similarity function for a data source and compute similarity scores for pairs of patients. An alignment module is configured to align a query patient to a best anchor timestamp of a similar patient or patients so that a comparison between the query patient and at least one similar patient is provided. A prediction module is configured to predict a long-term outcome measure of the query patient based on data from the at least one similar patient.
摘要:
A recommendation system and method includes extracting patient features for a current patient to generate representation of the current patient. The patient features for the current patient are compared to physician features of one or more physicians and patient-to-physician features of a group of patients from medically related records. Outcome measures associated with physicians are compared related to a current query. A future outcome for patient, physician pairs are predicted for the current patient based upon at least one predictive model constructed from the features and outcome measures to output.
摘要:
A system and method for predicting near term measurements of a patient includes a stream processor configured to summarize raw measurements from patients into signatures and construct optimal prediction models based on previously obtained signatures. A similar patient tracker is configured to monitor similar patient information for a query patient. The similar patient information is determined based on a similarity between the query patient and signatures of other patients. A model analyzer is configured to employ retrofitted optimal prediction models from similar patients to predict near term measurements of the query patient.
摘要:
Systems and methods for risk factor identification include identifying a first set of risk factors from personal data. A second set of risk factors is identified from at least one of a user input and a knowledge source. The first set is combined with the second set, using a processor, by selecting a number of risk factors from the first set that augment the second set of risk factors to determine a combined list of risk factors that predict a condition of interest.
摘要:
Systems and methods for risk factor identification include identifying a first set of risk factors from personal data. A second set of risk factors is identified from at least one of a user input and a knowledge source. The first set is combined with the second set, using a processor, by selecting a number of risk factors from the first set that augment the second set of risk factors to determine a combined list of risk factors that predict a condition of interest.