A method for answering questions posed by a user, the method comprising: receiving from a medical conversational user interface a user-generated natural language medical information query at an artificial intelligence-based medical conversation cognitive agent; extracting a medical question; compiling a medical conversation language sample; extracting internal medical concepts and medical data from the sample; inferring a therapeutic intent of the user; generating a therapeutic paradigm logical framework, wherein logical framework comprises medical logical progression paths from the medical question to respective therapeutic answers, each of the logical progression paths includes medical logical linkages from the medical question to a therapeutic path-specific answer, and the medical logical linkages include the internal medical concepts and external therapeutic paradigm concepts derived from a store of medical subject matter ontology data; selecting a likely medical information path based upon the therapeutic intent of the user; and answering the medical question.

Systems and methods are provided for identifying one or more health care applications in which a search request is received from a user at a remote client device. The request comprises an alphanumeric query and filtering criteria. A plurality of applications is searched to identify applications that satisfy the filtering criteria and further match the alphanumeric query thereby identifying a set of matching applications. Each respective application in the plurality of applications is (i) for a clinical indication and (ii) includes an evidence score generated by health care providers. The search query response is formatted for display by sorting the matching applications by sorting criteria thereby forming a sorted list of matching applications. At least a portion of the sorting criteria is the evidence score. The search query response, including the sorted list of matching applications, is communicated to the remote client device.

Systems and methods are provided for identifying one or more health care applications in which a search request is received from a user at a remote client device. The request comprises an alphanumeric query and filtering criteria. A plurality of applications is searched to identify applications that satisfy the filtering criteria and further match the alphanumeric query thereby identifying a set of matching applications. Each respective application in the plurality of applications is (i) for a clinical indication and (ii) includes an evidence score generated by health care providers. The search query response is formatted for display by sorting the matching applications by sorting criteria thereby forming a sorted list of matching applications. At least a portion of the sorting criteria is the evidence score. The search query response, including the sorted list of matching applications, is communicated to the remote client device.

A mechanism is provided for automated curation of genetic variants using machine learning and natural language processing on multitude sources. A functional study publication in a corpus of information is identified using a supervised classifier. Focal entity detection in the functional study publication is performed by detecting one or more genetic variant mentions in corresponding text using a regular expression based dictionary. Focal genetic variants are identified based on the focal entity detection and based on weighted scores from one or more sections of the corresponding text. For a given identified focal genetic variant, the functional study publication is classified. Sentences in the classified functional study expressing a relation between the given genetic variant and other entities are identified using a relation extraction model. The classified functional study is summarized and facts and relations expressed in the classified functional study are presented.

Methods, computer systems, and computer readable media for transforming raw healthcare data into relevant healthcare data are provided. Transformation of raw healthcare data into relevant data is accomplished by receiving raw data from a plurality of disparate data sources, indexing the data, and mapping the data. Embodiments transform raw data into relevant data through the use of natural language processing, synonymy and ontology mapping, and adaptive knowledge processing. The relevant data is stored in a one record that includes information necessary to understand and represent that patient.

Systems and methods for tracking healing progress of multiple adjacent wounds are provided. In one embodiment, a system may include a processor configured to receive a first image of a plurality of adjacent wounds near a form of colorized surface having colored reference elements, determine colors of the plurality of wounds, correct for local illumination conditions, receive a second image of the plurality of wounds near the form of colorized surface, to determine second colors of the plurality of wounds in the second image, match each of the plurality of wounds in the second image to a wound of the plurality of wounds in the first image, and determine an indicator of the healing progress for each of the plurality of wounds based on changes between the first image and the second image.

Systems and methods for tracking healing progress of multiple adjacent wounds are provided. In one embodiment, a system may include a processor configured to receive a first image of a plurality of adjacent wounds near a form of colorized surface having colored reference elements, determine colors of the plurality of wounds, correct for local illumination conditions, receive a second image of the plurality of wounds near the form of colorized surface, to determine second colors of the plurality of wounds in the second image, match each of the plurality of wounds in the second image to a wound of the plurality of wounds in the first image, and determine an indicator of the healing progress for each of the plurality of wounds based on changes between the first image and the second image.

Methods, systems, and computer-readable media are disclosed herein for automated susceptibility identification and alerting. An occurrence of an outbreak of an infectious disease occurring at a particular time or time range and at a particular location is determined. In response to determining the occurrence of the outbreak, patient information comprising location information and medical history information is automatically scanned. Patients who are within a predetermined range of the particular location of the outbreak and who have at least one high risk factor corresponding to the outbreak are identified using the patient information. A notification to at least one of the patients who are within the predetermined range and who have the at least one high risk factor corresponding to the outbreak is provided. The notification comprises a representation of an increased risk to the outbreak.

Data related to the adherence of a patient to a therapy regime can be collected from multiple medical devices, each of which may have an incomplete data set with data entries associated with times. Each incomplete data set represents a portion of a complete data set. The data sets can be integrated to create a complete data set with data entries from each incomplete data set being arranged in a temporal sequence with respect to one another. A report may be generated, the report having the data entries of the complete data set or a function of the data entries of the complete data set.

Data related to the adherence of a patient to a therapy regime can be collected from multiple medical devices, each of which may have an incomplete data set with data entries associated with times. Each incomplete data set represents a portion of a complete data set. The data sets can be integrated to create a complete data set with data entries from each incomplete data set being arranged in a temporal sequence with respect to one another. A report may be generated, the report having the data entries of the complete data set or a function of the data entries of the complete data set.