Abstract: A health monitoring system is described that permits daily tracking of medical conditions, journaling of information pertaining to treatments of the conditions, and providing a graphical user interface to see overall progress related to the conditions. The monitoring system includes a conditions schema, a journaling schema, and a user schema. The user schema accesses a conditions table generated by the conditions schema and a separate journal table to generate graphical representations.

Abstract: Methods and systems for explaining an output from a machine learning model. The methods include receiving from a machine learning model a probability score, a plurality of features, and a contribution value for each feature; transforming the plurality of features to a reduced number of features; generating at least one cluster of a portion of a population; grouping the patient in the generated cluster; and visually presenting to a user a comparison of the reduced number of features of the profile with the plurality of features associated with the portion of the population to explain the probability score.

Abstract: A healthcare system measures each item related to health; calculates an evaluation value from the measured measurement value, the evaluation value being classified into evaluation classifications of a standard evaluation, a positive evaluation, and a negative evaluation; sets the importance of an inspection result for each measurement item; selects a predetermined number of items in descending order of a weight of the importance and in which the classification is the negative evaluation; generates an inspection result from contents of the evaluation classification of the selected item; and determines and notifies the advice contents according to the inspection result, the notification being performed on a printed matter or a screen of a mobile terminal.

Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Abstract: The present system is a cloud-based prescription management service that can be used by patients, providers, pharmacies, payors, and pharmaceutical manufacturers to assist with the coordination of prescription management. The system can be accessed via any processing device, such as a smartphone, tablet, desktop, and the like. A patient retains autonomy in selecting where the prescription is ultimately dispensed and can track all existing prescriptions, manage prescriptions, review the status of prescriptions, establish refill reminders, and compare insurance coverage and pricing preferences. The system allows a user to choose one or more payment methods, manage multiple users, delivery preferences, and delivery locations for medications. The system acts as a hub that is integrated with a network of pharmacies where that prescription is “transferred” electronically from the system Digital Wallet or application to the pharmacy of choice.

Abstract: There is provided a smart control system including a host, at least one sensor and an informing device. The host identifies the activity of a specific member according to the detection result of the at least one sensor, and informs the specific member to take medicine on time or avoid eating specific ingredients using the informing device.

Abstract: A biometric information processing system including: measured value acquisition means that acquires a measured value related to biometric information of a patient; comparison means that compares the measured value with an improvement target reference set in advance; dosing information acquisition means that acquires dosing information including at least information on a dosing frequency of the patient when the measured value has not achieved the improvement target reference; medicinal effect confirmation support image generation means that generates a medicinal effect confirmation support image in which measured value transition information indicating a change in the measured value of the patient at each first predetermined period and the dosing information are browsable when the measured value has not achieved the improvement target reference; warning information generation means that generates warning information when the measured value has not achieved the improvement target reference.

Abstract: A group of inventions relates to a field of information technologies, namely, to a method for providing a client with an information regarding main characteristics of a pharmacy product and/or an analogue thereof in an automated pharmacy store or in a mobile application followed by dispensing the pharmacy product and/or the analogue thereof in the automated pharmacy store, and to a system for performing said method, which enable to achieve a technical effect that lies in a maximum quick, accurate and reliable processing of the client's incoming request, and, thus, a timely providing a maximum correct information regarding the main characteristics of the pharmacy product and/or the analogue thereof by means of a virtual assistant having pre-recorded programs of algorithms and fully uploaded pharmacology databases which are capable of being updated on-line, that does not require additional settings from the client and may operate in an autonomous mode, as well as in providing a high trust level of the virtual a

Abstract: A computer-implemented method can be used for monitoring spray delivery of drug-containing liquid for remedying a discomfort. The method involves a) receiving first input data, the first input data containing a number of delivered puffs as detected by a counter of a spray device during an intake period, b) receiving second input data containing user state data representative of self-reported perceived discomfort level, particularly pain level, and/or self-reported perceived side effect severity during the intake period, the user state data being obtained from a user input via a user interface of an application, c) determining, based at least on the first input data and the second input data, a suggested number of delivered puffs for an upcoming intake period, and d) outputting, via the user interface, user instructions indicating the suggested number of delivered puffs.

Abstract: A medication adherence service receives patients' medication schedules from health care professionals and systems. A medication adherence device is communicatively coupled to the medication adherence service. Based upon a patient's prescribed medication schedule, the medication adherence device manages and monitors a patient's adherence to the prescribed medication schedule. The medication adherence device captures images of compartments holding medications in accordance with the prescribed medication schedule. These images are analyzed to aid the patient in filling the compartments and to manage and monitor the patient in adhering to the prescribed schedule.

Abstract: A distributed system can include a server outside of a clinical environment and a connectivity adapter and a plurality of infusion pumps within the clinical environment. The connectivity adapter can monitor microservices that measure the quality of connectivity adapter's performance. If the performance is below a threshold level, a message indicating poor performance can be sent to the server. The message can be sent when a prior message relating to poor performance has not already been sent within a predetermined time period.

Abstract: Systems and methods for wireless electronic communication between a medical apparatus and a remote electronic computing device comprise generating and outputting by the medical apparatus an output signal, including: encoding data in the output signal according to a predetermined characteristic of the output signal; receiving and decoding by the remote electronic computing device the encoded data in the output signal; and establishing a pairing between the medical apparatus and the remote electronic computing device.

Abstract: An athletic parameter measurement device worn by an athlete during an athletic activity session includes a housing which attaches to the athlete, a display, a processor associated with the display, and an athletic parameter measurement sensor. During the athletic activity, the device detects, using the sensor, a vertical jump height of the athlete, and displays, during the performance of the athletic activity session, a representation of the vertical jump height on the display.

Abstract: Systems and methods for evaluating the efficacy of entertaining elements during physical and cognitive therapeutic sessions are disclosed. The system is comprised of a means for providing visual and auditory information, and prompting a patient to perform physical and cognitive tasks of varying difficulty. The system is further comprised of a means of tracking the patient's movements to provide feedback to the patient and assess the performance of the patient's movements. Prompted movements are designed to provide therapeutic mental, behavioral, and physical health benefits to the patient, and may increase or decrease in difficulty based on prior assessments of a patient's movements. Entertaining elements are introduced in the visual and auditory information and assessed for their impact on the patient's performance of the movements.

Abstract: Systems and methods for radioablation treatment planning are disclosed. In some examples, a computing device provides for display a user interface that allows a medical professional to define a target region of a patient for treatment. The user interface may allow the medical professional to select a treatment area using interactive target maps generated for the patient. The computing device also receives image data from an imaging system for the patient, such as image data identifying a 3D volume of the patient's scanned structure. The computing device may generate for display a 3D image of the scanned structure based on the received image data, and may superimpose on the 3D image a target region map that the medical professional can manipulate to define the target region of treatment for the patient. Once defined, the computing device may transmit the defined target region to a treatment system for treating the patient.

Abstract: Techniques, described herein, enable enhanced de-identification solutions for digital imaging and communications in medicine (DICOM) systems and records. DICOM header tags may be de-identified (e.g., anonymized) using one or more hashing algorithms and salt values. Solutions for evaluating and resolving collisions between de-identified. DICOM records may be de-identified in large batches or in smaller batches on a per-request basis. Additional features of the techniques are described herein.

Abstract: A method (100) of providing remote monitoring of a local operator (LO) of a medical imaging device (2) during a medical imaging examination, the method including: mapping image features to medical imaging examinations based at least on timestamps of the image features, the medical imaging examinations to which the image features are mapped including completed medical imaging examinations and a current medical imaging examination; converting the image features mapped to each completed medical imaging examination into a representation (43) of the completed medical imaging examination and a representation (47) of a current status of the current medical imaging examination; generating at least one completion time statistic (45) representing the completion times for the completed medical imaging examinations; and during the current medical imaging examination, displaying the representation of the current status of the current medical imaging examination and the at least one completion time statistic on the worksta

Abstract: One or more example embodiments of the present invention relates to a method for transmitting medical video data via a network. According to one more example embodiments, medical video data is provided via at least one of a medical imaging modality or a hospital data system. The medical video data is held in a buffer. Furthermore, a network connection is established in the network from a terminal to the buffer via a web protocol. The medical video data is transmitted from the buffer to the terminal in at least one network data stream via the network connection via the web protocol. The medical video data is displayed on a display apparatus of the terminal. One or more example embodiments of the present invention further relates to a system for transmitting medical video data and a buffer and also a terminal for such a system.

Abstract: A text data generation device 100 includes an acquisition unit 121 that acquires object person information representing time-series physical condition of an object person, an estimation unit 122 that estimates time-series states of the object person on the basis of the object person information, and a generation unit 123 that generates text data representing the state of the object person in a given time period, on the basis of the estimated time-series states of the object person.

Abstract: A surgical exercise performed with a surgical robotic system is sensed by depth cameras, generating 3D point cloud data. Robot system data associated with the surgical robotic system is logged. Object recognition is performed on image data produced by the one or more depth cameras, to recognized objects, including surgical equipment and people, in the operating room (OR). The surgical exercise is digitized by storing the 3D point cloud data of unrecognized objects, a position and orientation associated with the recognized objects, and c) the robot system data.

Abstract: The disclosure relates to a system and a method to eliminate noise from imaging data The disclosure provides system comprising a plurality of coils configured to generate one or more data sets and a processor may be configured to receive the data sets from the plurality of coils. The processor may determine an interference removal parameter from the data sets received from the plurality of coils. The interference removal parameter may remove noise from the one or more data sets.

Abstract: Systems and methods for transferring multi-frame data to remote computing entities. Systems including dedicated communications for multi-frame data and other data, and data transfer methods. An example computer-implemented method includes receiving, at an agent module positioned at a primary location, multi-frame medical image data over an intranet data connection from an image acquisition device also positioned at the primary location, compressing at the agent module the multi-frame medical image data, sending the compressed data from the agent module to a server over a first data transmission via an Internet data connection, storing the compressed data at the server, sending the compressed data to a user terminal device, and receiving medical data at the server from one or more computing devices positioned at the primary location over a second data transmission via the Internet data connection.

Abstract: A method and system for predicting a treatment outcome. Three-dimensional imaging data for a retina of a subject is received. A first output is generated using a deep learning system and the three-dimensional imaging data. The first output and baseline data are received as input for a symbolic model. A treatment outcome is predicted, via the symbolic model, for the subject undergoing a treatment for neovascular age-related macular degeneration (nAMD) using the input.

Abstract: A computer implemented method for centralizing the credentialing process of healthcare providers including receiving credential information from each of a plurality of healthcare providers; storing the respective credential information of each healthcare provider; verifying the authenticity of the respective credential information of each healthcare provider; and communicating credential information to at least one of a plurality of healthcare providing entities.

Abstract: In information processing device, the information acquisition means acquires patient information, medical professional information, and environment information. The treatment order determination means determines an order of treatment of the patients for each medical professional based on the patient information, the medical professional information, and the environment information.

Abstract: This patent describes an innovative cloud-based system that leverages machine learning algorithms to predict and facilitate the establishment of strong therapeutic alliances between care providers and help seekers. The system processes demographic, personality trait, and characteristic variables of both care providers and help seekers. These variables are used as inputs for a machine learning model that ranks care providers based on their predicted ability to establish a positive therapeutic alliance with a specific help seeker. The term “Therapeutic Alliance” refers to a collaborative relationship between a help seeker and a help giver, such as therapists, coaches, nurses, mentors, and others. The system is applicable to various help giver-help seeker relationships, including mental health providers and clients. The cloud-based system provides scalability, flexibility, and cost-effectiveness by using cloud computing resources.

Abstract: A system and platform are provided that enables end-users to gain insight into group and individual practices. Those insights may be obtained for any desirable aspect of each practice and the collection of practices including, but not limited to, financial information, medical information, human resources information, and patient information. That information can be used by group management to identify best practices, and it can be used to advise individual practices on strengths to be reinforced and weaknesses to be addressed. That information may be conveyed to group management and individual practices in the form of dashboards customizable to the particular functions of specific users and their personas.

Abstract: Disclosed herein are methods for intelligently populating medical compliance forms (MCFs) with at least patient data to meet compliance requirements (e.g., meeting patient data compliance requirements such as HIPAA requirements, as well as compliance requirements concerning patient forms). In particular, methods involve training and deploying machine learning models that can appropriately analyze a wide array of MCFs with varying formats. Advantages of the methods disclosed herein are three-fold: 1) reducing the amount of time and resources that a healthcare provider needs to commit to satisfying compliance requirements and 2) improving patient outcome by more intelligently incorporating data in medical compliance forms, and 3) ensuring meeting of compliance requirements (e.g., HIPAA compliance requirements).

Abstract: A perioperative monitoring system which includes a selection module configured to receive operational requirements, a database of conditions, a condition module responsive to the selection module and in communication with the database of conditions. The condition module is configured to select conditions from the database based on the received operational requirements. At least one validation system receives the selected conditions from the condition module and determines a validation. The one validation system includes a filter that filters selected conditions based on a functional selection in order to provide a functional configuration.

Abstract: A dental equipment service application that allows for capture of an image of a component of a dental equipment, and using the image to search for a replacement, is described. In embodiments, the image is used to search a database of replacement parts, such as with a machine learning algorithm. Possible replacement parts may be provided via an API to facilitate purchase. The application may further allow access to diagnostic information on the equipment and documentation relevant to the replacement parts. Other embodiments may be described and/or claimed.

Abstract: A modular medical device platform can include a chassis forming a plurality of rack bays each including a pair of drawer slides, a plurality of controller modules each including a drawer enclosure including flanges couplable to the pair of drawer slides with the rack bay of the plurality of rack bays, and a plurality of module locks affixed to the chassis and aligned with each rack bay of the plurality of rack bays. Each module lock of the plurality of module locks can include an electrical connector and a locking mechanism adapted to automatically lock a controller module of the plurality of controller modules as the controller module is slid into the rack bay on the pair of drawer slides.

Abstract: A computing device is provided having a processor in communication with a memory configured to store machine-readable instructions. The processor can receive, from a wireless communication interface of a medication delivery device, an advertising packet comprising injection event information generated by the medication delivery device, analyze contextual data associated with the advertising packet to determine validation data, and choose, based on the validation data, between implementing any two or three of the following actions: (i) include the injection event information in a medication log, (ii) prompt a user for additional information, or (iii) ignore the advertising packet. The medication delivery device comprises (a) a reservoir configured to hold medication, (b) an actuating button for initiating an injection of the medication, and (c) a processing circuit in communication with the wireless communication interface.

Abstract: Inadvertent activation of a portable medical device such as an ambulatory infusion pump can be reduced by locking a touchscreen of the device when it is indicated that an uninterrupted operation is to be performed. When a processor receives a device operation input from the touchscreen that indicates an uninterrupted operation is to be performed on the portable device, the touchscreen is automatically locked such that touch input at the touchscreen is not processed by the processor to navigate between or among menu screens or set pump parameters. Following completion of the uninterrupted operation, the touchscreen can be unlocked. In one embodiment, the touchscreen can be unlocked by selection of an unlock icon on the touchscreen.

Abstract: Methods, devices, and kits are provided for determining a compatibility of one or more devices in an analyte monitoring system.

Abstract: Methods of treatment and diagnosis using facilitated communication of health information from a patient device to a physician device are provided herein. Methods include improved dissemination and management of health information data while safeguarding patient privacy. A patient may initiate a tele-medicine session for treatment using a portable user device of the patient, after which a physician may request a set of data through the patient device relevant to the treatment. In response, the device of the patient identifies a subset of the data set requested by the physician that is authorized by the patient for release to the physician. The system further facilitates display of differing types of information on a user interface of the physician's device to facilitate improved treatments and diagnostics, health information collection and patient follow-up.

Abstract: A system for health monitoring comprised of one or more neural networks. A first function of the one or more neural networks includes an autoencoder functionality, tuned to have high recall, configured for monitoring data and for detecting an anomaly within the data. A second function of the one or more neural networks is a false positive reduction (FPR) function, configured for distinguishing false positive anomalies from true positive anomalies by analyzing the data or querying an individual. Confirmed positive anomalies are classified by a classification engine. The system may be configured to train one or more machine learning models thereof, such that the system is trained for monitoring of the individual.

Abstract: An intervention system and method for performing an intervention, for example a medical intervention, for example on a patient, at a first location, this being an intervention location. The intervention system includes at least one drivable intervention device at the first location to be used for the intervention, a first operating device provided at the first location for control of the at least one intervention device by a local group of people situated at the first location, a second operating device provided at a second location which is remote from the intervention location, for control of the at least one intervention device by a remote group of people situated at the second location, and an authorization device for authorizing the control of the at least one intervention device by the operating devices.

Abstract: Systems, methods, and graphical user interfaces for remotely proctored, at-home drug testing are disclosed herein. In some embodiments, a video connection, an audio connection, and/or a real-time messaging service may be established between a user device and a supervising user device for the purposes of conducting remote drug testing. User interface data may be sent to the user device to display various instructions to assist in the remote drug testing, such as instructions for providing an image of user identification or instructions for collecting a sample or developing results. User interface data may also be sent to the supervising user device for the purposes of supervising the remote drug testing, such as for verifying the user identification or recording the results.

Abstract: Provided herein is a digital care circle platform for electronic communication between a senior and virtual care circle members. The digital care circle platform may include a chatbot and an application associated with the senior, a mobile application associated with the plurality of virtual care circle members, at least one processor in communication with at least one memory device, and one or more servers, sensors, wearables, and transceivers. The at least one processor may be configured to: (i) accept event posts from the senior via the chatbot and application and from each virtual care circle member of the plurality of virtual care circle members via the mobile application, (ii) detect pro-active check-ins from the senior, and (iii) generate, once a pro-active check-in is detected, an electronic communication detailing the pro-active check-in as a pro-active check-in event, and transmit the pro-active check-in event to the virtual care circle members.

Abstract: Devices, systems, and processes are disclosed herein relating to the collection, transmission, and/or use of physiological condition information (e.g., blood glucose information) for a remote healthcare determination. One embodiment includes a physiological condition information system. This system embodiment includes a physiological condition management device and a remote server. The physiological condition management device can be at a first location (e.g., accompanying a patient at the first location) and the remote server can be at a second location that is different from the first location. The physiological condition management device and remote server can be in data communication over a transmission link. The physiological condition management device is configured to collect physiological condition information associated with the patient and send this information over the transmission link to the remote server.

Abstract: A method, apparatus, and non-transitory computer-readable medium are provided for image processing of medical images. The method may include receiving one or more medical images. Tissue regions may be identified in each of the one or more medical images. The method may include generating patches associated with respective identified tissue regions. A patch tumor burden (TMB) score may be predicted for each of the patches based on a neural network model, wherein the neural network model is trained using a distribution smoother and a weighted loss function. The method may also include a tumor burden score being generated for each of the one or more medical images based on the patch tumor burden score for patches associated with the respective identified tissue regions in respective medical images from the one or more medical images.

Abstract: In the strange feeling prediction device, the prediction result acquisition means inputs diagnosis data to a target prediction model which is a trained prediction model serving as a target, and acquires a prediction result by the target prediction model. The label acquisition means acquires a strange feeling label indicating a strange feeling of an expert with respect to the prediction result. The strange feeling prediction model training means trains a strange feeling prediction model using the prediction result and the strange feeling label. The strange feeling prediction means outputs a strange feeling index indicating the strange feeling with respect to the prediction result outputted by the target prediction model, using the trained strange feeling prediction model.

Abstract: Methods and medical devices comprising a processor comprising a plurality of data analytic process modules and a diagnostic integrator; a memory communicably coupled to the processor; an input/output device communicably coupled to the processor, the processor being configured to execute instructions stored in the memory to: cause the patent interface to record a first data from a subject; analyze the first data with a first of the plurality of data analytic process modules and determine a first diagnostic output; analyze the first date with a second of the plurality of data analytic process modules and determine a second diagnostic output; integrate the diagnostic outputs from the plurality of data analytic process modules and determine a unified final diagnosis to a subject.

Abstract: An endoscopic diagnosis support method whereby an examined area and an unexamined area can be clearly discriminated. After a preparatory step of an observation canvas is performed in advance, a frame marking step, a key point calculation step, a preceding and following frame displacement amount calculation step, a preceding and following frame marking step are executed to thereby perform observation recording. In an image diagnosis support step IDS, support is performed such that the existence of a lesion is diagnosed in an organ on the basis of a plurality of position data marked with respect to a plurality of frames in the observation canvas data and an endoscopic image in the plural frames.

Abstract: A system and method for identifying differential diagnoses using a multi-classifier disease model. The method includes constructing the multi-classifier disease model based on at least one disease profile, wherein the multi-classifier disease model is a machine learning network of at least one disease and a plurality of health variables; extracting at least one patient health variable from input patient data, wherein the input patient data indicates a patient condition; applying the multi-classifier disease model to the at least one patient health variable to determine probabilities for the at least one disease; identifying, based on the probabilities of the at least one disease, the differential diagnoses for the input patient data; and providing the differential diagnoses.

Abstract: In some embodiments, a current state of a medical condition or a progression of the medical condition is predicted by processing one or more digital pathology images and expression levels of genes using a machine-learning model. In some embodiments, one or more predicted gene-expression levels are generated by processing a data set corresponding to one or more digital pathology images using a machine-learning model. In some embodiments, one or more predicted digital pathology metrics are generated by processing a data set that corresponds to expression levels of a set of genes using a machine-learning model.

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

Abstract: A method and system for managing a treatment for a subject diagnosed with neovascular age-related macular degeneration (nAMD). Spectral domain optical coherence tomography (SD-OCT) imaging data of a retina of the subject is received. Retinal feature data is extracted for a plurality of retinal features using the SD-OCT imaging data, the plurality of retinal features being associated with at least one of a set of retinal fluids or a set of retinal layers. Input data formed using the retinal feature data for the plurality of retinal features is sent into a first machine learning model. A treatment level for an anti-vascular endothelial growth factor (anti-VEGF) treatment to be administered to the subject is predicted, via the first machine learning model, based on the input data.

Abstract: The present disclosure relates to methods and systems for determining the risk of a complication of pregnancy occurring. Certain embodiments of the present disclosure provide a method of determining the risk of a complication of pregnancy occurring in a subject, the pregnancy arising in the subject from a conception from a maternal donor and a paternal donor.

Abstract: Methods and systems for predicting whether a patient is likely to develop post-cardiotomy cardiogenic shock (PCCS) are described. The method includes receiving medical information for a patient, extracting one or more features from the received medical information, providing the one or more features as input to a trained classification model configured to output a risk assessment that the patient is likely to develop PCCS, and outputting an indication of the risk assessment.