Abstract: The present disclosure relates to a question generating method and apparatus, an inquiring diagnosis system, and a computer readable storage medium. The question generating apparatus includes at least one processor, the at least one processor being configured to: acquire a candidate question set Q; calculate an information value of each candidate question in the candidate question set Q; and generate at least one question according to the information value of each candidate question.

Abstract: The present disclosure relates to automated systems and methods which assess the quality of radiology examinations and identify actionable changes to improve the quality of future exams. For each of a plurality of imaging studies, a study protocol and set of study metrics can be defined and the defined study protocol can be performed to generate data associated with the imaging study. A metrics assessment can be performed by applying at least a portion of the data associated with the result of the imaging study against the set of study metrics for the imaging study so as to generate a metrics score for the imaging study. The metrics scores can be stored in a score repository and analyzed so as to provide recommendations to improve the examination process.

Abstract: Methods and systems providing adaptive assessment of a physical subject to efficiently collect assessment data and modify an assessment schedule based on the analyses. The methods and systems can control the timing of each assessment in order to collect data at times and under conditions that are most informative about the physical subject. Such adaptive methods and systems significantly minimize the frequency of data collection without loss in accuracy or precision and can increase test reliability through reduction in redundancy. The ability to estimate an unknown, underlying function using a small number of free parameters that remain constant regardless of the number of data points being estimated substantially reduces the error of the function estimate. Because estimates of the measurement error are achieved with a minimum of sampled assessments, and with great accuracy, the statistical power of clinical trials, for example, can be greatly increased.

Abstract: A system for physiologically informed zoological selection. The system includes a computing device configured to record a user biological extraction profile. A computing device receives condition state training data and calculates a plurality of condition vector outputs for a user biological extraction profile utilizing a first clustering algorithm. A computing device selects a characteristic condition vector output. A computing device receives zoological training data and calculates a plurality of animal vector outputs utilizing a second clustering algorithm. A computing device creates a ranked listing of animals utilizing a plurality of animal vector outputs.

Abstract: A medical system comprises processing circuitry configured to: receive a first trained model, wherein the trained model has been trained using a first data set acquired in a first cohort; receive a second data set acquired in a second cohort; input data included in the second data set and data representative of the first trained model into a second trained model; and receive from the second trained model an affinity-relating value which represents an affinity between the data included in the second data set and the first trained model.

Abstract: A hierarchical reinforcement learning system for automatic disease prediction, and method thereof, including an agent simulator module for simulating the acts of doctors; a user simulator module for simulating the acts of patients; a disease classifier module, and an internal critic module, wherein the agent simulator module includes a master module which is in a high level, and a plurality of worker modules which are in a low level; the plurality of worker modules each acts as a doctor from a specific department, while the master module appoints the plurality of worker modules to interact with the user simulator module for collecting information; wherein the master module activates the disease classifier module to output a prediction result when information collected from the plurality of worker modules is sufficient; wherein the internal critic module is configured for generating intrinsic reward to the plurality of worker modules, judging the termination condition for the plurality of worker modules, and w

Abstract: Embodiments disclosed herein provide a system, method, and computer program product for providing a triage classification system. The triage classification system uses a computer model that is developed using historical patient data. The developed computer model is applied to collected patient attribute data from a patient in a pre-hospital setting to generate a triage category. Based on the generated triage category, health care professionals can take desired actions, such as transporting the patient to a facility matching the generated triage category.

Abstract: A method, computing platform, and computer program product are provided for monitoring a clinical trial. A computer platform receives, for the clinical trial, study design information including a set of parameters and corresponding parameter values related to travel constraints of a subject for the clinical trial. The computer platform applies the study design information and the corresponding parameter values to a trained machine learning model to calculate a predicted travel score indicative of a travel burden for the subject. When the travel score fails to satisfy a travel score threshold, the computer platform determines at least one suggestion for adjusting the travel score and the at least one suggestion is output. The computer platform outputs the predicted travel score.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and the automatic development of patient care groupings of patient data. Embodiments of computer network architecture automatically generate care grouping, and organize and analyse patient care data accordingly, and generate and transmit reports of the care grouping definitions, data, and analysis. Embodiments may generate care groupings either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination system databases with data provided by system users, and third-party databases to generate the patient care groupings, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: Methods and systems are provided for automated protocol recommendation and selection in a medical imaging system. In one example, a method may include receiving, from a hospital-specific user interface, a standard procedure ID, determining a medical imaging procedure based on the standard procedure ID, and, responsive to having previously received the standard procedure ID, generating one or more protocol recommendations based at least on the determined medical imaging procedure, and performing the determined medical imaging procedure based on at least one of the one or more protocol recommendations.

Abstract: A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Abstract: Provided is a patient data visualization method and system for assisting decision making in chronic diseases. According to the present application, a management data model diagram of a patient on a hyperplane is constructed by constructing a chronic disease knowledge graph, and combining static data and dynamic data of the patient, and then the management data model diagram is projected onto a two-dimensional plane. The difference of the Euclidean distance between features of a patient information model on a two-dimensional plane graph from the distance of standard features is compared, and a management plan is generated and recommended in combination with path node concepts and an attribute relationship between the concepts.

Abstract: Techniques that facilitate natural language processing of a motion alphabet for unsupervised clinical scoring are described. In one example, a system converts sensor data from one or more movement capturing devices associated with a user identity into symbolic data indicative of a symbolic representation of the sensor data. The system also analyzes the symbolic data associated with the one or more movement capturing devices based on a symbolic analysis technique.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

Abstract: A mechanism is provided in a data processing system to implement a personalized patient engagement engine.

Abstract: A method, system, and computer program product are provided for generating a predictive model. A processor(s) obtains a raw data set (peptide libraries) of patients designated as diagnosed/pre-diagnosed with a condition or not diagnosed with the condition. The processor(s) segments the raw data set into a pre-defined number of groups and separates out a holdout group. The processor(s) performs a principal component analysis on the remaining groups to identify, based on a frequency of features in the remaining groups, common features (principal components) in the remaining groups and weighs the common features based on frequency of occurrence. The processor(s) determines a smallest number of the principal components that yields a pre-defined level of validation accuracy. The processor(s) generates a predictive model, by utilizing the smallest number for a best fit in a logistic regression model. The predictive model provides binary outcomes.

Abstract: In an embodiment, a computer-implemented process comprises accessing a plurality of digitally stored, unstructured medical diagnostic data; digitally displaying a first subset of the medical diagnostic data, the first subset of the medical diagnostic data including at least a first set of diagnostic reports, using a computer display device, concurrently with digitally displaying one or more quality control checklists that are specific to a medical discipline represented in the first set of diagnostic reports; receiving digital input specifying one or more errors in the first set of diagnostic reports and digitally storing the digital input in association with the first subset of medical diagnostic data; training a hierarchical Bayesian machine learning model using the digital input and the first subset of medical diagnostic data; evaluating the hierarchical Bayesian machine learning model, after training, for a second subset of the medical diagnostic data, the second subset being different from the first subs

Abstract: Disclosed is a system for generating Clinical trial protocol design document with selection of a Patient and an Investigator for a clinical trial process. The system inputs meaningful information derived from the raw data, a pre-Drafted protocol, a regulatory authorities' protocol curated by regulatory authorities, and a pre-stored dataset, present in a repository. A Clinical trial protocol design document is drafted by generating a case frame upon extracting data in form of a key value into a standard document. Each key value is validated and a prediction score is computed based on overlapping of the interim Clinical trial protocol design template with the pre-Drafted protocol and the regulatory authorities' protocol to determine whether the interim Clinical trial protocol design document is approved or rejected. A Clinical trial protocol design document is generated when the interim Clinical trial protocol design document is approved.

Abstract: Methods and systems for processing data inquiries are described. In one embodiment, data from a data source can be received, a data processing request indicating a request to perform a first function can be received, and a first identifier associated with a user can be determined based on the received data. A table storing a plurality of adjudication results including a plurality of field parameters may be accessed. The plurality of adjudication results in the table can be filtered by determining which of the plurality of adjudication results includes one of the plurality of field parameters having a value that matches or corresponds to the selected field value to create a first subset of filtered adjudication results from the plurality of adjudication results. The first subset of adjudication results can be transmitted when the processor determines that at least one adjudication result of the plurality of adjudication results is within the first subset. Additional methods and systems are disclosed.

Abstract: Various methods and systems are provided for generating an optimized workflow and assisting in clinical decision making during analysis of a patient image by a user at a site. In an example, a method includes automatically identifying one or more exam parameters associated with a diagnostic exam that includes one or more medical images and automatically generating a workflow for analyzing the one or more medical images, the workflow including one or more of a diagnostic protocol, a toolset, a prior imaging scan, a prior finding, and a reference image, the workflow generated based on the one or more exam parameters.