Abstract: Methods for providing a computer implemented medical diagnosis are provided. In one aspect, a method includes receiving an input from a user comprising at least one symptom of the user, and providing the at least one symptom as an input to a medical model. The method also includes deriving estimates of the probability of the user having a disease from the discriminative model, inputting the estimates to the inference engine, performing approximate inference on the probabilistic graphical model to obtain a prediction of the probability that the user has that disease, and outputting the probability of the user having the disease for display by a display device.

Abstract: An apparatus tracks patient recovery following an orthopedic procedure. A statistical computing engine implements a predictive model of the patient's post-procedural state for the orthopedic procedure based on a database of patient demographic data, comorbidities, pre-procedural walking parameters, including steps taken, and the orthopedic procedure that the patient is undergoing. The pre-procedural walking parameters are populated from physical sensor data automatically collected from the patient. The predictive model creates a temporal trendline of post-procedural walking parameters, including steps taken, and a temporal trendline of post-procedural pain level. A processor then compares the patient's actual post-procedural state to the predictive model of the patient's post-procedural state. The post-procedural walking parameters are also obtained from the physical sensor.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for collecting monitoring data and predicting outcomes for communities. In some implementations, monitoring data is received, including location tracking data that indicates locations visited by individuals in a community. Community data for the community that describes characteristics of the community and a geographic region associated with the community is received. One or more predictive models are used to evaluate regions for potential for transmission of a disease based on behavior patterns of individuals in the community. The one or more predictive models can be models trained based on training data describing a plurality of different communities and behavior patterns and disease outcomes of individuals in the different communities over time.

Abstract: A system and associated method for alerting to ambiguous medical advice through a data analysis system connected to one or more data sources and a client terminal. The data analysis system has an extraction system, a machine learning system, a categorization system, and an alerting system. The data analysis system extracts first information related to outcomes of one or more medical treatments from one or more first data sources, extracts second information related to outcomes of one or more medical treatments from one or more second data sources, and applies machine learning to the extracted first information and second information to develop a classifier for categorizing proposed treatments. The data analysis system also categorizes a proposed treatment or a combination of treatments as potentially harmful based on the classifier, and provides an alert to a client terminal regarding the proposed treatment.

Abstract: A computer system provides nutrition-based management of health conditions using machine learning. Health information of a user is evaluated to determine a health condition. Medication information for the user is analyzed and the medication information is mapped to the health condition. One or more natural treatments for the user are determined from information sources based on the mapped health condition and medication information, wherein the one or more natural treatments include one or more from a group of nutrition and mind/body treatments, and wherein the information sources include information from crowdsourcing. The one or more natural treatments and medication information for the health condition are indicated. Embodiments of the present invention further include a method and program product for providing nutrition-based management of health conditions using machine learning in substantially the same manner described above.

Abstract: An integrated multi-facility document management system is configured to allow an access right attribute to be dragged and dropped from one tree form to a root of a another tree form whereby when the access right attribute is dragged and dropped from the one tree form to the root of the other tree form a corresponding access right attribute of the root is changed to the access right attribute and a corresponding access right attribute in branches connected to the root is changed to the access right attribute.

Abstract: Systems and methods for recommending foods to a user based on health data, includes a database, a memory and a processor. The database stores user health data for each user within a plurality of users, including vitals, genotypic and phenotypic data, user food preference data and foods data that includes macronutrient and micronutrient data for foods that may be recommended to a user. The memory stores program instructions, including program instructions that are capable of (i) classifying user health data into predetermined diet types and micronutrient recommendations, (ii) filtering the food data to determine available foods for a user; (iii) a ranking available meals for the user based on the micronutrient recommendations and the food data, and (iv) translating micronutrient recommendations and/or food data for the available foods for the user into specific food recommendations for the user.

Abstract: Methods, apparatus, and systems for predicting and controlling communicable diseases are disclosed. In one example aspect, a method for predicting a communicable disease includes receiving, for each member of a community, multiple data streams associated with the member from multiple sensor devices, and computing, based on a social or locational relationship between the member and other entities in the community and a timeline of activities performed by the member according to the timestamp for each data packet, a list of behavioral indicators for the community indicating a current state of the communicable disease using one or more machine learning models.

Abstract: A system for creating a unique transaction ID (UTID) securely representing a medical diagnostic transaction between a user/patient and a telemedicine professional is provided. The system includes a first central office database configured to store medical information and profile information for a plurality of users/patients, a second central office database configured to store UTIDs, a memory, and a processor coupled to the memory. The processor is configured to receive test information, initiate generation of a UTID for the new diagnostic transaction, analyze the received test information to determine if the test information indicates a positive or a negative result, transfer the analysis results back to the user/patient's MU, and receive from the MU a request for a telemedicine professional session.

Abstract: The example embodiments may relate to systems, methods, apparatuses, devices, and computer readable media for collecting and presenting information at the patient encounter level. The example embodiments may include processing an encounter detail file associated with treatment a healthcare provider provided to a patient during a patient encounter, generating a linkage that links the encounter detail file with at least one other file associated with the patient encounter, and storing the linkage in a longitudinal patient administration record. The example embodiments may further include processing the longitudinal patient administration record to determine a metric and select an item based on the metric, and generating and communicating an order to a supplier for the selected item to cause delivery of the selected item to a desired location.

Abstract: Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.

Abstract: Disclosed herein are platforms, systems, devices, software, and methods for processing and analyzing real-time data to generate predictions of near future events. Machine learning algorithms can be configured for varying levels of aggression to enhance timeliness of the predictions. Ensemble machine learning techniques combining a plurality of trained models configured with higher and lower aggression levels can be used to improve both timeliness and accuracy.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for medication decision support. A computer generates one or more patient profiles detailing one or more patient health conditions and one or more medication profiles detailing one or more medication side effects. The computer then determines an association between the one or more patient health conditions and one or more medication side effects and quantifies the association as a conflict score. In addition, the computer determines whether the conflict score exceeds a threshold and, if so, the computer identifies and recommends an alternative medication having a lower conflict score.

Abstract: A patient case may be evaluated whenever new information is received or as scheduled. Evaluation may include resolving a Diagnosis-Related Group (DRG) code and determining a CDI scoring approach based at least in part on a result from the resolving. Resolving a DRG code may include determining whether a DRG code is associated with the patient case. If no DRG code is found, the system may search for an International Classification of Diseases code or ask a user to select or assign a DRG code. Using the determined CDI scoring approach, a first score may be generated and adjusted by at least one of length of stay, documentation accuracy, payer, patient location, documentation novelty, review timing, case size, or documentation sufficiency. The adjusted score may be normalized and presented to a CDI specialist, perhaps with multiple CDI scores in a sorted order.

Abstract: Disclosed are systems and methods for secure and seamless set up and modification of bolus calculator parameters for a bolus calculator tool by a health care provider (HCP). In one aspect, a method for enabling HCP set up of a bolus calculator includes providing a server accessible by both an HCP and a patient; upon login by the HCP, displaying, or transmitting for display, a fillable form, the fillable form including one or more fields for entry of one or more bolus calculator parameters; receiving data from the fillable form, the data corresponding to one or more bolus calculator parameters; and upon login by the patient, transmitting data to a device associated with the patient, the transmitted data based on the received data, where the transmitted data corresponds to one or more of the bolus calculator parameters in a format suitable for entry to a bolus calculator.

Abstract: A system for generating a viral alleviation program including a computing device configured to receive at least a viral biomarker relating to a user, retrieve a viral epidemiological profile, identify, using the viral epidemiological profile, a plurality of nutrition elements for the user, wherein identifying includes assigning the viral epidemiological profile to a viral infection category, calculating, according to the viral infection category, a plurality of nutrient amounts, wherein calculating a plurality of nutrient amounts includes determining an effect of the plurality of nutrient amounts on the viral epidemiological profile, and calculating the plurality of nutrient amounts as a function of the effect, identifying, as a function of the plurality of nutrient amounts, the plurality of nutrition elements, wherein the plurality of nutrition elements are intended to prevent viral infection as a function of the viral infection category, and generate, using the plurality of nutrition elements, viral allevia

Abstract: A method (and the associated system) for risk evaluation in a vascular region of interest V are provided, making it possible, based on a fluidic study and/or on a treatment algorithm using a learning method, to generate risk models MR predictively indicating the risk level NR for the occurrence of events such as: occurrence and rupture of an aneurysm, coarctation, etc., and the associated anatomical areas. The models MR are intended to serve as an assistance support for the medical decision-making of a physician.

Abstract: A system including a range of motion module, a quality of sleep module, an overall module, and a control module. The range of motion module, subsequent to performing a procedure on a patient, determines a first range of motion score of the patient based on a signal generated by a sensor. The quality of sleep module, subsequent to performing the procedure on the patient, determines a first quality of sleep score or a first pain score based on the signal generated by the sensor. The overall module determines a combined score based on (i) the first range of motion score, and (ii) the first sleep score or the first pain score. The control module (i) determines whether an outcome of the procedure is positive based on the combined score, and (ii) stores the determined outcome and the combined score in a memory.

Abstract: A method for providing a computer implemented medical diagnosis includes receiving an input from a user comprising a symptom of the user. The method also includes providing the symptom as an input to a medical model comprising a probabilistic graphical model comprising probability distributions and relationships between symptoms and diseases, and an inference engine configured to perform Bayesian inference on said probabilistic graphical model. The method also includes generating a question for the user to obtain further information concerning the user to allow a diagnosis, and outputting said question to the user. The method also includes outputting said question to the user, wherein generating a question for the user comprises ranking said questions by determining the utility of the possible questions.

Abstract: A power-optimized eco-system for tracking a user's health comprises: one or more wearable remote sensors, each wirelessly communicating only with one wearable central sensor; a portable device readily accessible to the user; and a cloud platform. Each sensor is configured to measure data indicative of one or more physiological parameters. The central sensor is configured to receive and subsequently process data measured by each remote sensor, to process data measured by the central sensor, and to generate corresponding instructions. The portable device comprises: a receiver wirelessly receiving the processed data and instructions from the central sensor; a processor running a mobile application handling the processed data and instructions; and a transmitter. The cloud platform is configured to: receive the processed data from the transmitter; analyze the received processed data; and transmit the results of the analysis to at least one of the portable device and an authorized healthcare entity.