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 patient monitor (8) includes a display (10). Patient values (38) are obtained for one or more known variables of a risk prediction function (30). One or more unknown variables of the risk prediction function are determined, and at least one hyperplane (40) is defined as values assumable by the one or more unknown variables. Values of the risk prediction function are computed over the at least one hyperplane using the obtained patient values. A visualization template is selected from a database of visualization templates (50) using template selection indices including the risk prediction function and the one or more unknown variables. Using the visualization template, a visualization (52) of the computed values of the risk prediction function over the at least one hyperplane is displayed.

Abstract: Systems and methods for deriving classification rules from documents and a database using rule-based machine learning. The method includes extracting first variables from documents corresponding to an organization. The method further includes extracting second variables from a database corresponding to the organization. The method also includes filtering the extracted second variables based on at least one of null values, repeat variables, location variables, ID variables, or data variables. The method further includes deriving first classification rules based on the first variables using a rule-based machine learning algorithm. The method also includes calculating an accuracy of the derived first classification rules. The method also includes deriving second classification rules based on the first variables and the filtered second variables. The method further includes determining a suggested additional variable based on the derived second classification rules and the calculated accuracy.

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: The present invention provides a system and method for evaluating Range of Motion of a subject. An aspect of the present invention proposes a system for evaluating Range of Motion of a subject, comprising markers attached to at least two joints of a body part of the subject respectively; an image capturing device for capturing images of the markers when the subject is doing an exercise with the body part attached with the at least two markers; and an evaluating device communicated with the image capturing device, wherein the evaluating device includes a determining unit configured to determine whether the exercise done by the subject is valid for the evaluation of Range of Motion of the body part of the subject, and a calculating unit configured to, if the exercise is valid, calculate the parameters related to Range of Motion of the body part of the subject according to the images related to the valid exercise.

Abstract: Disclosed is a computer-implemented method (1) of assigning a task to a mobile communication device (212, 222) belonging to a pool (210, 220) of mobile communication devices, wherein each mobile communications device is associated with a trusted owner and is locatable by means of location information, the method comprising generating (10) a task to be performed; generating a set of task locations at which the task can be performed; receiving (20) location information for said mobile communications devices; deriving (30) a location for each mobile communications device from said location information; using the derived location to calculate (40) a cost score for each mobile communication device, said cost score indicating the cost of the trusted owner of said mobile communication device to reach a target location; and assigning (60) the task to a selected location in said set and to one of said mobile communications devices, wherein the task is assigned to the one of said mobile communications devices based on

Abstract: Systems for determining an erroneous code in a medical report comprising a plurality of codes, each code representing a comment in the medical report the system, comprise a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to determine a respective vector representation for each of the plurality of codes in the medical report, wherein relative values of any selected pair of vector representations are correlated with a co-occurrence of the corresponding codes in a set of reference medical reports. The set of instructions when exectured by the processor further cause the processor to determine an erroneous code in the medical report, based on the vector representations.

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: 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: A patient monitor (8) includes a display (10). Patient values (38) are obtained for one or more known variables of a risk prediction function (30). One or more unknown variables of the risk prediction function are determined, and at least one hyperplane (40) is defined as values assumable by the one or more unknown variables. Values of the risk prediction function are computed over the at least one hyperplane using the obtained patient values. A visualization template is selected from a database of visualization templates (50) using template selection indices including the risk prediction function and the one or more unknown variables. Using the visualization template, a visualization (52) of the computed values of the risk prediction function over the at least one hyperplane is displayed.

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: 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 configured to facilitate computer-assisted linkage of healthcare records. The system is configured to process a whole collection of records, using a reference set of record attributes, to generate a first prediction for a first portion of the collection of healthcare records of individuals of which healthcare records of the first collection portion have matching values with respect to the reference set of record attributes; re-process the first portion of records (the already matched portion of records) using other sets of record attributes to determine which ones of the other sets are reliable predictors of matching records; and use the reliable predictors to process a remaining unmatched portion of the healthcare records.

Abstract: A medical imaging apparatus includes a radiology workstation (10) with a workstation display (14) and one or more workstation user input devices (16). A medical imaging device controller (26) includes a controller display (30) and one or more controller user input devices (32). The medical imaging device controller is connected to control a medical imaging device (40) to acquire medical images (44).

Abstract: The present invention provides a training garment (1) for persons suffering from upper limb dysfunction. According to one aspect of the invention, the training garment (1) comprises a shoulder garment (10), which comprises a shoulder portion (11) adapted to be worn across a person's shoulder and a first main body portion (12) adapted to be worn around the trunk of the person. Both ends of the shoulder portion (11) are attached to the first main body portion (12), and the shoulder portion (11) has an area (101) for receiving a shoulder marker. The proposed training garment (1), due to its special design of the shoulder portion (11) and the first main body portion (12), can prevent displacement of the shoulder marker while the person is performing active rehabilitation exercises, such as raising up his arm, and therefore, it can improve the recognition accuracy of the movement tracking system for the rehabilitation training.

Abstract: Methods and apparatuses are described for recommending digital content in a network environment. A web server determines a session context of a user associated with a remote device in which the user interacted with digital content objects. The web server identifies prior content interactions in which prior users interacted with digital content objects. The web server builds a recommendation feature vector from the session context and the prior content interactions. The web server trains a machine learning model using the generated recommendation feature vector to minimize a predicted rating. The web server selects target digital content objects that have a predicted rating of the user that is at a threshold. The web server provides indicia of the selected digital content objects to the remote device. The web server causes at least one of the selected target digital content objects to be transmitted from a content server to the remote device.

Abstract: A device, system, and method optimizes a patient flow. 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 determining a step in a patient flow for a patient of a primary care physician (PCP) associated with the healthcare network based upon first information relative to the patient. The method includes determining a referral of a healthcare provider to perform the step based upon the first information and second information relative to a region associated with the patient and the healthcare organization. The method includes determining whether the referral is acceptable based upon third information relative to the healthcare provider and the healthcare organization. The method includes generating a recommendation including the referral for the PCP when the referral is acceptable.

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: A method and an apparatus for accurately predicting and modeling patient events, such as avoidable admissions, within a healthcare network are disclosed herein. The present disclosure provides systems and methods of predicting and modeling patient events with the use of a constantly updated data set, a sliding windows format, and a random survival forest model. Further, the present disclosure provides methods and systems for accurately predicting and modeling patient events and patient flows amongst various facilities within, and outside of, the healthcare network. The present application provides systems and methods for overcoming problems associated with conventional simulation systems by providing an intuitive and concise validation procedure to tune the simulation system, particularly targeting at patient flow among the various facilities, or nodes.