Abstract: The present disclosure pertains to a system configured to prepare and use prediction models for socioeconomic data and missing value prediction. Some embodiments may: extract, from received population segment data, a training set of socioeconomic parameter values for each population segment; provide, to a prediction model as input, first parameter values of the respective training set for the prediction of additional parameter values of the training set such that the prediction of the additional parameter values is performed without reliance on the additional parameter values; provide, for each of the training sets, the additional parameter values to the prediction model as reference feedback for the prediction model's prediction of the additional parameter values to train the prediction model; and predict, based on a working set of parameter values for a population segment, additional values for the working set using the prediction model subsequent to its training.

Abstract: According to an aspect, there is provided an apparatus for use in detecting malingering by a first subject in a test of physical and/or mental function of the first subject.

Abstract: The present system is configured to display distributions of predicted health outcome information for patient populations in geographical areas. The system is configured to obtain demographic, social (e.g., including environmental), and prior health outcome information for a patient population in a geographical area. The system is configured to train a prediction model based on the demographic, social, and prior health outcome information, which outputs weighted features of the demographic and social information that are predictive of health outcomes for the patient population. The system is configured to cause display of a distribution of predicted health outcome information for the patient population in the geographical area based on the weighted features.

Abstract: The present disclosure pertains to a method and system configured for facilitating performance irregularity detection. The system comprises one or more physical computer processors configured to obtain a collection of records associated with a user; select one or more criteria to evaluate a performance of the user; determine impact of contributing factors on the one or more criteria; determine a criterion from the one or more criteria that has a value that satisfies an irregularity detection threshold associated with the criterion; determine one or more subsets of contributing factors from the contributing factors; modify one or more weights associated with the determined one or more subsets of contributing factors; and redetermine the criterion using the modified weights associated with the one or more subsets of contributing factors to identify which contributing factors of the one or more subsets of contributing factors cause the criterion to breach the irregularity detection threshold.

Abstract: The present disclosure pertains to a system configured to generate computer simulations of patient loads for units of health care facilities. The system is configured to: obtain past patient census information for an individual unit of a health care facility, the past patient census information comprising a quantity of patient visits to the individual unit during past periods of time; determine intra-period variation and inter-period variation in the quantity of patient visits to the individual unit during the past periods of time; classify the individual unit based on the intra-period and inter-period variations; and generate a computer simulation of patient loads for the individual unit based on the classification. The computer-simulated patient loads comprise a quantity of patient visits to the individual unit during one or more future periods of time. The computer simulation is performed using a non-parametric simulation algorithm.

Abstract: A method for scheduling patients for medical appointments, including: predicting the no-show risk and no-show cost for the patients; forecasting the cost of a transportation assistance service for the patients; optimizing the scheduling of patients based upon cost of the transportation assistance service, the no-show risk, and the no-show cost; selecting a population of patients to receive the transportation assistance service; and scheduling the population of patients for their medical appointment and transportation assistance service.

Abstract: A care plan tool for defining care plans for patients with constrained care plan resources, including: a patient clustering and risk stratification module configured to cluster a group of patients into patient cohorts and configured to produce a machine learning model to predict the risk of a medical condition for each patient cohort based upon medical data for the patients in the cohort; an optimization module configured to determine an optimized care plan for each patient cohort based upon the machine learning models for each patient cohort and the constrained care plan resources; a matching care plan module configured to receive patient data for new patients and configured to match the new patients to a patient cohort and associated care plan; and a new care plan optimization module configured to receive patient specific constraints for new patients from a care manager and configured to determine a new optimized care plan for each new patient based upon the machine learning models for each patient cohort,

Abstract: Methods and systems for selecting a treatment for a patient. The system extracts an incidental finding from a record associated with a patient and an associated follow-up recommendation. The system then determines whether any inconsistencies exist between the follow-up recommendation from the report and a follow-up recommendation prescribed by institutional or administrative guidelines. Any inconsistencies may then be resolved to guarantee an appropriate workflow for patient care.

Abstract: The present disclosure pertains to a system configured to generate computer simulations of patient loads for units of health care facilities. The system is configured to: obtain past patient census information for an individual unit of a health care facility, the past patient census information comprising a quantity of patient visits to the individual unit during past periods of time; determine intra-period variation and inter- period variation in the quantity of patient visits to the individual unit during the past periods of time; classify the individual unit based on the intra-period and inter-period variations; and generate a computer simulation of patient loads for the individual unit based on the classification. The computer-simulated patient loads comprise a quantity of patient visits to the individual unit during one or more future periods of time. The computer simulation is performed using a non-parametric simulation algorithm.

Abstract: A care plan system, including: a key performance indicator (KPI) model configured to predict the value of a KPI for a specific patient based upon patient data and care plan elements; cost data indicating the cost of the care plan elements; a graphical user interface (GUI) configured to receive first suggested care plan elements, to provide a first predicted value of the KPI and a first care plan cost associated with the first suggested care plan elements using the KPI model, and to present the first predicted KPI value and first care plan cost.

Abstract: The present disclosure pertains to a system configured to prepare and use prediction models for socioeconomic data and missing value prediction. Some embodiments may: extract, from received population segment data, a training set of socioeconomic parameter values for each population segment; provide, to a prediction model as input, first parameter values of the respective training set for the prediction of additional parameter values of the training set such that the prediction of the additional parameter values is performed without reliance on the additional parameter values; provide, for each of the training sets, the additional parameter values to the prediction model as reference feedback for the prediction model's prediction of the additional parameter values to train the prediction model; and predict, based on a working set of parameter values for a population segment, additional values for the working set using the prediction model subsequent to its training.

Abstract: A method for sequence typing using whole-genome sequence data, comprising: receiving a plurality of gene marker sets, each gene marker set comprises sequence data for a plurality of gene markers from an organism, and comprising a plurality of alleles for each gene marker; generating a set of machine learning models for each gene marker in the gene marker set configured to predict an allele value for a gene marker when sequence data for that gene marker is missing or unusable; receiving whole-genome sequence data for the organism, comprising missing or unusable sequence data for a gene marker in the plurality of gene markers; analyzing, using the set of machine learning models, the received whole-genome sequence data to determine one or more probable allele values for that gene maker; and displaying the one or more probable allele values.

Abstract: A database merger method (20) merges two or more anonymized healthcare databases (X, Y). Each anonymized healthcare database has personally identifying information anonymized including having medical care units replaced by medical care unit placeholders. In the database merger method, statistical patient feature distributions are computed for medical care unit placeholders in the anonymized healthcare databases. Medical care unit placeholders in different anonymized healthcare databases are matched by matching corresponding statistical patient feature distributions for the respective medical care unit placeholders. Patients in different anonymized healthcare databases are matched. The patient matching is performed within matched pairs of medical care unit placeholders to improve computational efficiency.

Abstract: According to an aspect, there is provided an apparatus for use in detecting malingering by a first subject in a test of physical and/or mental function of the first subject.

Abstract: A device, system, and method optimizes a healthcare network. The method is performed at a device of a healthcare organization, the healthcare organization having a healthcare network including a plurality of healthcare providers. The method includes selecting a healthcare provider to be evaluated. The method includes determining a score for the healthcare provider, the score being based upon at least one of first information relative to the healthcare provider and second information relative to the healthcare network. The method includes generating a recommendation for the healthcare provider based upon the score.

Abstract: Systems and methods are provided for healthcare predictive analysis based on dynamic monitoring of patient conditions. Dynamic monitoring is used by healthcare provider entities to collect historical claim feed data regarding its patients. The historical claim feed data is used to monitor patients' progress and conditions. Moreover, this data is used to train and update a predictive model used to predict the occurrence of events. The model predicts the occurrence of events using a sliding window-based algorithm, in which subsets (e.g., windows) of the historical claim feed data are sequentially used to train the model. For each window of data, the model extracts features and outcomes, and trains the model based thereon. The model then extracts features and outcomes of the next window of data and updates the existing model based thereon. The resulting model is run against a set of a data to predict the occurrence of events.

Abstract: A patient selection tool for selecting patients for a social-behavioral determinants of health (SBDoH) program, including: a graphical user interface (GUI) module configured to present a GUI to a user, receive inputs from the user including a SBDoH factor, and to select patient cohort data based upon the inputs received from the user, a machine-learning model configured to predict a key performance indicator (KPI) for each patient based upon the patient cohort data and the SBDoH factor, a success rate module configured to predict the probability of success of the SBDoH program for each patient in the patient cohort; a return on investment (ROI) module configured to determine the cost savings associated with the SBDoH program for each patient in the patient cohort based upon the cost associated with the KPI, the probability of success of the SBDoH program, and a change in the KPI associated with the SBDoH factor, and a patient selection module configured to select patients for the SBDoH program based upon the

Abstract: The invention discloses an apparatus for determining features to be included in a risk assessment instrument. The apparatus comprises a processor configured to: receive an indication of a plurality of features to be analyzed, each feature of the plurality of features being potentially relevant to a likelihood of the presence of a defined target condition; apply, using the plurality of features, a predictive model to a dataset representing the ground truth in relation to the defined target condition; determine, based on an output of the predictive model, one or more features of the plurality of features that are most relevant to the likelihood of the presence of the defined target condition; and determine, based on an output of the predictive model, a threshold value for each of the one or more features, beyond which the likelihood of the presence of the defined target condition is increased or decreased. A method and a computer program product are also disclosed.

Abstract: The present disclosure pertains to computer-assisted linkage of healthcare records. In some embodiments, a first portion of a collection of healthcare records of individuals may be processed using a set of record attributes (corresponding to strong identifiers) that includes one or more record attributes corresponding to strong identifiers. Based on such processing, a first set of matches between healthcare records of the first collection portion may be predicted, and a number of matches in the first set of matches may be determined. At least one other portion of the collection of healthcare records of individuals may be processed using another set of record attributes that includes one or more record attributes different from the strong-identifier-corresponding record attributes. Based on the number of matches in the first set of matches, the processing of the other collection portion with respect to predicting healthcare record matches may be caused to stop.

Abstract: The present disclosure pertains to a system for providing model-based predictions of patient-related metrics based on location-based determinants of health. In some embodiments, the system (i) obtains (a) one or more patient-related features and (b) one or more location-related features associated with an individual; (ii) performs one or more queries based on the one or more patient-related features associated with the individual to obtain one or more location-related features associated with similar individuals; and (iii) provides the one or more location-related features associated with the similar individuals and the one or more location-related features associated with the individual to the machine learning model to predict (a) one or more metric values for the patient-related metrics associated with the individual and (b) at least one location-related feature associated with the individual likely to contribute to the one or more metric values.