Abstract: A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit a cellular-based LPWAN signal to provide location information based on the acquired data. The device may also include sensors to collect one or more of environmental, activity and physiological data. The device may transmit some or all of its acquired data to the system to provide a predictive model to correlate changes in the acquired data to corresponding health, safety or related changes to a wearer of the device. In one form, the predictive health care protocol uses a machine learning model at least some of which may be performed on the device.

Abstract: A method may include identifying a shift associated with a clinician by applying a machine learning model trained to identify, based on a series of transaction records associated with the clinician, one or more shifts associated with the clinician. The clinician may be identified as likely to engage in a hazardous behavior based at least on the shift associated with the clinician. In response to determining that the clinician as likely to engage in the hazardous behavior, activating a protective workflow. The protective workflow may be configured to prevent the clinician from engaging in the hazardous behavior as well as to collect evidence associated with the hazardous behavior. Related methods and articles of manufacture are also disclosed.

Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage media for generating psychiatric treatment recommendations. In one aspect, the method includes actions of receiving, by a server and from a first user device, one or more data structures that collectively include fields structuring data that represents (i) current patient symptoms and (ii) current patient diagnoses, generating, by the server, rendering data structure that includes fields structuring data that represents rendering data that, when rendered on a display device, presents a patient dashboard based on the data that is received from the mobile device, and providing, by the server, the rendering data structure to a second user device that is different than the first user device, wherein the second user device is configured to render the rendering data structured by the fields of the rendering data structure to output the patient dashboard on the display of the second user device.

Abstract: A system, method, and device for medical data collection are disclosed. An example portable device includes a camera configured to record images, a memory storing the recorded images, and a display interface for displaying the images. The portable device also includes a processor configured to instruct the camera to record at least one of the images and extract medical data from the at least one image using an optical character recognition routine. The example processor is further configured to establish a connection with a clinician computer such that the portable device is recognized by the clinician computer as a keyboard. Moreover, the example processor is configured to transmit at least some of the extracted medical data to the clinician computer as a keyboard input to the clinician computer.

Abstract: A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Abstract: A method (300) for identifying a characteristic of a user of a personal care device (10) comprising a sensor (28), a controller (30), and a database (34), comprising the steps of: training (320) the device with training data, comprising the steps of: (i) obtaining (322) sensor data for at least one personal care session for each of at least two different users; (ii) extracting (324), via an extraction module, a plurality of features from each of the personal care sessions; and (iii) training (328), using the extracted features, a classifier to identify a characteristic of each of the at least two different users of the device; obtaining (330) sensor data for at least part of a new personal care session; extracting (340) a plurality of features from the sensor data for the new personal care session; and identifying (360), using the trained classifier, a characteristic of the user of the device.

Abstract: A medical system according to an embodiment includes a medical apparatus and a first terminal. The medical apparatus is configured to have medical information therein. The first terminal is configured to be communicably connected to the medical apparatus so as to be able to transmit and receive the medical information thereto and therefrom, to have a function of an access point, and to cause a display to display a first code containing identification information of the access point. The first terminal is configured to be communicably connected to a second terminal having read the first code so as to be able to transmit and receive medical information thereto and therefrom.

Abstract: Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

Abstract: Described are web-based clinical data stores, mobile clinician applications, web-based patient portals, and aggregated decision engines comprising: a translation module configured to express aggregated electronic medical records in a format that is human-readable and accessible to an inference engine and/or a machine learning algorithm; a notation module configured to express relationships between treatment steps to generate human-readable diagrams and to transmit the diagrams as instructions to the inference engine or the machine learning algorithm; an encryption-based decentralized zero-trust de-identification and anonymization module to protect private patient information when releasing records for research purposes outside the treating physician’s protected network; an inference engine configured to receive inputs from the translated medical records of a patient and a subset of interlinked treatment steps selected by the treating physician to generate outputs comprising predictions and probabilities; and

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: Embodiments of the present disclosure provide a method and apparatus for determining a psychological counseling training scheme. The method includes: obtaining training feeling data of a user after each session of psychological counseling training through an interactive inquiry with the user; inputting the training feeling data into a first classification model, identifying a training result of the user after each session of psychological counseling training by using the first classification model, and collecting statistics about a training result of the user in a current training period; and determining a training scheme of the user for a next training period based on the training result in the current training period of the user. The invention resolves the problem that a counseling result is not ideal because individual demands of different users cannot be satisfied if a psychological robot performs psychological counseling on the user according to a preset counseling procedure.

Abstract: Various aspects described herein relate to a location-based and population-based health metric processes. In one example, a computer-implemented method for generating one or more predicted health metrics for a location includes receiving a request to assess the one or more health metrics associated with the location, and identifying at least one current or future built, social, or natural environment parameter associated with the location. The method may further include calculating one or more predicted health metrics associated with the location based upon at least one of the current or future built, social, or natural environment parameters associated with the location, monetizing the healthcare costs of the predicted health metrics and displaying the one or more predicted health metrics and costs.

Abstract: Systems and methods for management of a distributed ledger including prescription information are disclosed. An example apparatus includes a processor and a logical data structure to configure a device according to an electronic prescription defining an action for a patient, the electronic prescription organized as record(s) in a distributed ledger and processible by the device to apply the action to the patient. The electronic prescription is to cause the device to at least: configure the device to apply the action to the patient; validate the action for the patient using the distributed ledger; and propagate a record of the action to the distributed ledger.

Abstract: Embodiments of the present disclosure pertain to verifying medical diagnoses. In one embodiment, a first classification unit is configured to process manually generated medical input data. The first classifier generates one or more first probabilities corresponding to one or more medical diagnoses. A second classification unit is configured to process machine generated medical input data associated with the manually generated medical data. The second classifier generates one or more second probabilities corresponding to the one or more medical diagnoses. A verification unit is configured to verify the manually generated input data by combining the first and second probabilities.

Abstract: Embodiments for intelligent recommendation of useful medical actions to a user by a processor. One or more useful medical actions may be cognitively recommended with evidence in support thereof for impacting a health state of a user according to historical data collected from one or more data sources, one or more user profiles, a domain knowledge, feedback data, or a combination thereof. The one or more useful medical actions may be ranked according to a scoring criteria, the domain knowledge, the historical data, a set of rules, previously recommended medical actions, or a combination thereof.

Abstract: Provided is a system for estimating a compliance metric indicating the usage of hygiene equipment by one or more operators, such as in a healthcare setting. The system includes a receiving section and a calculation section. The receiving section is configured to receive activity data from a sensor arrangement, the activity data indicating activities by the one or more operators including usage of the hygiene equipment. The calculation section is configured to estimate, based on the activity data and a trained function, the compliance metric.

Abstract: A recommendation method, system, and computer program product include monitoring a patient using a plurality of sensors, receiving patient information including a comfort level corresponding to a sensor of the plurality of sensors, determining a relevance of each sensor of the plurality of sensors to at least one health conditions of the patient, and determining at least one sensor of the plurality of sensors to disconnect based on the comfort level and the relevance of each sensor.