Abstract: Embodiments of present disclosure disclose method and system for generating a medical image based on a textual data in a medical report. For generation, a textual data from each of one or more medical reports of the patient is retrieved. The textual data comprises one or more medical events and corresponding one or more attributes associated with each of the one or more medical reports. Further, a matching score for each of plurality of reference images is computed based on the textual data, using a first machine learning model. Upon computing the matching score, one or more images are selected from the plurality of reference images based on the matching score associated with each of the plurality of reference images. The medical image for the patient is generated based on the one or more images and the textual data using a second machine learning model.

Abstract: A method (200) of converting a flowchart (110) to a structured electronic representation (116) of the flowchart includes: identifying, in an image (108) of the flowchart, a plurality of shapes (112) corresponding to flowchart blocks of the flowchart; identifying arrows (114) defining flow paths between the flowchart blocks in the image including identifying flowchart blocks connected by the arrows and their directionality; identifying text labels and their locations in the image and determining text content of the text labels; associating the text labels with flowchart blocks or defined flow paths based on locations of the text labels, flowchart blocks, and arrows; and generating the structured electronic representation of the flowchart based on the flowchart blocks, the flow paths, and the text labels. A structure of the structured electronic representation is determined at least based at least on the flow paths between the flowchart blocks of the image.

Abstract: A device, system, and method is provided for detecting pain in a cardiac-related region of the body and determining whether that pain is cardiac or non-cardiac. The device, system, and method may include calculating or determining a first feature based on a variation in activity level and a variation in the detected heartrate measurement and a second feature based on a variation in the detected ECG features and a first feature and then subjecting at least the first feature and the second feature to a cardiac pain classifier to determine a cardiac classification.

Abstract: Disclosed herein is a method and system for evaluating compliance of standard clinical guidelines in medical treatments of one or more patients. Electronic Health Records (EHRs) of the patients are received and aggregated to obtain patient-specific EHRs. Subsequently, the patients are clustered into groups based on disease conditions and socio-demographic characteristics of patients. Further, deviations in the medical treatments provided to the patients are detected by comparing the medical treatments with standard clinical guidelines, for evaluating compliance of the standard clinical guidelines with the medical treatments. In an embodiment, the method of present disclosure helps in detecting care gaps among the medical treatments provided to the patients, and thereby helps in providing improvised and/or patient-specific medical assistance to the patients.

Abstract: A machine learning based recommendation model, including a supervised learning classifier configured to receive input training data that includes a plurality of behavioral determinants, a supervised learning model configured to receive subject input data that includes a plurality of behavior determinants, wherein the supervised learning model outputs a predicted behavior of a subject, and a channel selection module configured to receive the subject input data and the predicted behavior and to determine a recommended communication channel for the subject to follow to achieve the predicted behavior.

Abstract: A method for generating a textual description from a medical image, comprising: receiving a medical image having a first modality to a system configured to generate a textual description of the medical image; determining, using an imaging modality classification module, that the first modality is a specific one of a plurality of different modalities; determining, using an anatomy classification module, that the medical image comprises information about a specific portion of an anatomy; identifying, by an orchestrator module based at least on the determined first modality, which of a plurality of different text generation models to utilize to generate a textual description from the medical image; generating, by a text generation module utilizing the identified text generation model, a textual description from the medical image; and reporting, via a user interface of the system, the generated textual description.

Abstract: This disclosure describes a system that delivers content (e.g. multimedia video), and analyzes and aggregates feedback of a user viewing the content. The user may interact with the multimedia video in the form of answering questions which are dynamically placed between video segments. The system may determine the response type of the user for a given question. The system may handle both positive and negative responses (e.g. correct and incorrect responses to questions). The system may help the user navigate the segments of the video.

Abstract: This disclosure describes a system that effectuates presentation of video content based on complexity of video content segments therein. The system may analyze the video content using semantic ontology to identify semantic concepts; segment the video content into one or more video content segments based on identified semantic concepts, determine the complexity measure of the one or more video content segments based on a weightage of the identified semantic concepts, present the one or more video content segments based on the complexity measure, and present a visualization of the measure of the complexity.

Abstract: The present invention relates to a device (10) for use in improving a user interaction with a user interface application. It is described to provide (210) data for a user, the data comprising interaction data of the user with a plurality of interaction fields of a first version of a user interface application. The interaction data is clustered (220) into a plurality of groups. The interaction data is analysed (230) to determine the contribution a specific user interaction field of the plurality of interaction fields makes to a specific group of the plurality of groups. The specific user interaction field is modified (240) on the basis of the determined contribution the specific user interaction field makes. A second version of the user interface application is displayed (250) comprising the modified specific user interaction field.

Abstract: This disclosure describes a system that delivers content (e.g. multimedia video), and analyzes and aggregates feedback of a user viewing the content. The user may interact with the multimedia video in the form of answering questions which are dynamically placed between video segments. The system may determine the response type of the user for a given question. The system may handle both positive and negative responses (e.g. correct and incorrect responses to questions). The system may help the user navigate the segments of the video.

Abstract: In certain embodiments, a video file may be obtained based on one or more predetermined criteria. Information associated with a user (to which dynamic content derived from at least a video portion of the video file is to be presented) may be obtained. The video file may be processed based on the information associated with the user to determine reference points within the video file. The dynamic content may be generated based on the reference points such that the dynamic content comprises a first video portion of the video file (that corresponds to at least one of the reference points) and additional content related to the first video portion. The dynamic content may be provided for presentation to the user.