Abstract: A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Abstract: Embodiments provide a computer implemented method of identifying a group of medical professionals for performing an endoscopic procedure on a particular patient, including: training a machine learning model with a plurality of electronic medical records of different patients having a history of one or more endoscopic procedures, wherein each electronic medical record includes a group of medical professionals performing the one or more endoscopic procedures, and recovery time of each endoscopic procedure; receiving an electronic medical record of a new patient intending to have an endoscopic procedure; calculating a score for each medical professional in a medical organization representing a level of match between the new patient and each medical professional; and identifying a group of medical professionals for performing the endoscopic procedure on the new patient.

Abstract: A system and associated method provides an assessment of patient's heart health status based on past medical data. The system has a collection system, a processing system, a profiling system, and a user tools system. The collection system collects data from at least one data source having the past medical data of a plurality of patients. The processing system processes unstructured data to extract information related to patient health at different points in time. The profiling system develops a classifier based on the extracted information and other collected data. The profiling system further categorizes the extracted information into one of a plurality of health statuses at the different points in time. The user tools system generates a user interface comprising the plurality of health statuses at the different points in time. The health statuses relate to a compensated or decompensated status of the patient's heart.

Abstract: A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Abstract: A method for training a machine learning model for open domain question answering includes receiving trained classifiers for question answering. The received trained classifiers are used to generate a set of candidate answers to a question. Second trained classifiers are used for scoring each of the candidate answers. The scoring indicates a measure of how well each candidate answer answers the question. Using the second trained classifiers for scoring each of the candidate answers includes comparing each candidate answer to a first ground truth corresponding to the question. A set of top-scoring candidate answers is presented to a human operator who marks each as correct or incorrect. The correct candidate answers are treated as additional ground truths for further training the first trained classifiers.

Abstract: A method for training a machine learning model for open domain question answering includes receiving trained classifiers for question answering. The received trained classifiers are used to generate a set of candidate answers to a question. Second trained classifiers are used for scoring each of the candidate answers. The scoring indicates a measure of how well each candidate answer answers the question. Using the second trained classifiers for scoring each of the candidate answers includes comparing each candidate answer to a first ground truth corresponding to the question. A set of top-scoring candidate answers is presented to a human operator who marks each as correct or incorrect. The correct candidate answers are treated as additional ground truths for further training the first trained classifiers.

Abstract: Systems and methods for the transmission of electronic computer files are provided. One embodiment of the invention includes a framework of generalized components that allows a certain file, such as and email attachment, to properly operate on a recipient computer system regardless of whether the recipient computer includes a copy of the application program for which the attachment originated.