Abstract: A method is provided for authenticating, and establishing and tracking eligibility of a patient to purchase an over-the-counter (OTC) drug from a retailer, the patient using a client device or terminal and the retailer having a server. The server is configured to redirect the client device or terminal to a backend platform to authenticate the patient, access a storage to search for any information that indicates the patient is ineligible to purchase the OTC drug; and without any information that indicates the patient is ineligible, administer a questionnaire to the patient to assess the eligibility for the OTC drug. And the backend platform redirects the client device or terminal back to the server to complete the online sale of the OTC drug to the patient when the patient is eligible, or cancel the online sale of the OTC drug to the patient when the patient is ineligible.

Abstract: A method for capturing patient feedback for a medical treatment includes: receiving user instructions indicating a symptom being treated, a symptom score for the symptom being treated, and an indication of a beginning of a treatment session; receiving patient feedback comprising an updated symptom score for the symptom being treated and/or absence or presence of one or more of a plurality of attributes; storing the received patient feedback and a time in the treatment session at which the respective patient feedback was received; repeating the receiving of patient feedback and storage of data during the treatment session; and receiving a user instruction indicating an ending of the treatment session, wherein an alerting module alerts a user of the computing device for input of patient feedback after a predetermined period of time has elapsed following receipt of patient feedback without a subsequent receipt of patient feedback.

Abstract: Provided are a method and apparatus for providing a counseling service. A method of operating a counseling center server interworking with an instant messaging service includes providing a profile of at least one expert account for counseling linked to a channel of a second user to a terminal of a first user, identifying an initiation condition for a counseling ticket for counseling with a first expert account, creating a counseling ticket to be allocated to the first expert account, and transmitting counseling content through a chat room for counseling of the channel in which the first expert account and an account of the first user participate.

Abstract: The present specification provides data integrity monitoring and feedback in cloud-based computing platforms. While such platforms are ubiquitous and provide a means for globally scaled computing, the need for reliable data integrity also increases with scaling. A system in accordance with an embodiment includes a data integrity monitoring engine that integrates with existing cloud computing platforms and monitors events corresponding to receipt of files and checking the frequency and size of those files.

Abstract: A computer-implemented method for dynamically managing electronic transactions of pet homing data is disclosed. The method includes receiving a pet selection, sending a confirmation indicating that the pet has been homed, determining whether at least one pet owner profile corresponding to at least one user record exists in a data store, in response to the determining that the at least one pet owner profile does not exist in the data store, creating a new pet owner profile based on the at least one user record, determining whether the at least one pet profile corresponding to at least one pet record exists in the data store, in response to determining that the at least one pet profile does not exist in the data store, creating a new pet profile based on the at least one pet record, and associating the new pet profile with the new pet owner profile.

Abstract: Triggering information that defines conditions for presenting a virtual clinical exam may be downloaded to a user device. The user device may collect sensor data and use the sensor data to evaluate the conditions. When certain conditions are fulfilled, the user device may present the virtual clinical exam at the user device.

Abstract: A computer-implemented method and system are applied to a first instance of a first disease-specific semantic model. The method and system: receive a first request; process the first instance of the first disease-specific semantic model based on the first request to generate a first processed instance of the first disease-specific semantic model; and provide the first processed instance of the first disease-specific semantic model as output.

Abstract: Mechanisms are provided to implement a patient summary generation engine with deduplication of instances of medical concepts. The patient summary generation engine parses a patient electronic medical record (EMR) to extract a plurality of instances of a medical concept, at least two of which utilize different representations of the medical concept. The patient summary generation engine performs a similarity analysis between each of the instances of a medical concept to thereby calculate, for a plurality of combinations of instances of the medical concept, a similarity metric value. The patient summary generation engine clusters the instances of the medical concept based on the calculated similarity metric values for each combination of instances in the plurality of combinations of instances of the medical concept to thereby generate one or more clusters, and select a representative instance of the medical concept from each cluster in the one or more clusters.

Abstract: Techniques for predictive disease identification using simulations improved via machine learning. A method includes applying at least one machine learning model to features extracted from data including animal characteristics data of an animal, wherein outputs of the at least one machine learning model include a plurality of disease predictor values, wherein each disease predictor value corresponds to a respective disease type of a plurality of disease types, wherein each disease type of the plurality of disease types corresponds to a predetermined group of diseases; generating disease contraction statistics based on the outputs of the at least one machine learning model; and determining, based on the disease contraction statistics, at least one disease prediction for the animal.

Abstract: A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

Abstract: A data combination device and a data combination method are provided. The data combination device includes a storage device and a processor. The storage device is configured to store component instances. The processor is coupled to the storage device and is configured to receive a combining instruction. The processor parses a component description and data type in the combining instruction. The processor obtains a plurality of matching instances corresponding to the component description from the plurality of component instances. The processor packages the plurality of matching instances into package data.

Abstract: A method for determining a defection probability includes receiving user data. The method also includes determining, from the user data, a set of features characterizing a likelihood of the user to change from the initial service channel to an alternative service channel. The method further includes generating an input encoding for the set of features. The method additionally includes determining, using a predictive model, a probability that indicates how likely the user is to change from the initial service channel, wherein the predictive model is trained to receive, as input, the input encoding and to generate, as output, a respective probability that indicates how likely a respective user is to change from the initial service channel to the alternative service channel. The method also includes determining whether the probability from the predictive model satisfies a defection threshold and selectively generating a remedial action based on the probability.

Abstract: Methods and systems are described for generating dynamic interface options using machine learning models. The dynamic interface options may be generated in real time and reflect the likely goals and/or intents of a user. The machine learning model may provide these features by interpreting multi-modal feature inputs. For example, the machine learning model may include a first machine learning model, wherein the first machine learning model comprises a convolutional neural network, and a second machine learning model, wherein the second machine learning model performs a Weight of Evidence (WOE) analysis.

Abstract: Provided herein are exemplary embodiments including a secure intelligent networked architecture for real-time precision care plan remote support including a secure intelligent data receiving agent having a specialized hardware processor and a memory, the secure intelligent data receiving agent configured to automatically receive a digital data element over a network from a Bluetooth® equipped peripheral device, the digital data element representing an output in response to a predetermined plan, the secure intelligent data receiving agent caching the digital data element within a non-relational database for short term storage and the secure intelligent data receiving agent configured to process the digital data element using a serverless compute functionality and configured with logic for anomaly detection.

Abstract: A computer-implemented method of analysing transcript data, comprising: receiving a plurality of transcripts and a set of outcome data for each transcript, the outcome data associating each transcript with one or more outcomes; receiving a query transcript; processing the query transcript and each transcript within the plurality of transcripts; comparing, the processed data representing the query transcript with the processed data representing the plurality of transcripts, to identify a subset of the plurality of transcripts that meet a threshold similarity criterion with respect to the query transcript; and thereby determining a relationship between the query transcript and one or more outcomes.

Abstract: Disclosed embodiments provide techniques for estimating imputation algorithm performance. Multiple imputer algorithms are selected, and an evaluation of how well each of the imputer algorithms can estimate the missing data is performed. Disclosed embodiments obtain an imputer candidate dataset (ICD). The imputer candidate dataset is compared to the incomplete data range, and a similarity metric is determined between the data range and the ICD. When the similarity metric exceeds a predetermined threshold, an imputer evaluation dataset (IED) is created from the ICD by removing one or more data points from the ICD. Each imputer algorithm is evaluated by applying the IED to it, and computing an imputer evaluation metric based on its performance. The multiple imputer algorithms are ranked based on the imputer evaluation metric. The best ranked imputer algorithm can then be selected for use on the incomplete data range within the measurement dataset.

Abstract: A computer-implemented method includes receiving a group of sets. Each set has values for immutable attributes that match values for at least one mutable attribute in a prediction request. The method includes generating a deviation model based on the group of sets. The method includes generating multiple sets of likely mutable attribute values using the deviation model. The method includes automatically selecting a neural network based on at least one of the likely mutable attribute values. The neural network includes a set of layers. Each layer includes a set of nodes. A first layer receives inputs at the set of nodes of the input layer. A last layer outputs, from the neural network, outputs modified by a preceding layer. The method includes generating a likelihood corresponding to the at least one mutable attribute in the prediction request using the neural network.

Abstract: Systems and methods herein describe a mental health management system. The mental health management system accesses patient data associated with a patient from a database, determines that the patient is associated with a trigger event, identifies a category associated with the trigger event, generates a notification based on the trigger event and the identified category, validates the notification based on a notification history and transmits the notification to a computing device.

Abstract: A method includes receiving a prescription benefit request and identifying one or more covered alternative drugs. The method also includes identifying one or more additional alternative drugs based on at least one of the requested drug and determining whether the one or more additional alternative drugs includes at least one covered additional alternative drug. The method also includes determining, for each of the at least one covered additional alternative drug and the one or more covered alternative drugs, an alternative drug value. The method also includes identifying the at least one covered additional alternative drug and the one or more covered alternative drugs having a lowest alternative drug value and generating a request response indicating at least the alternative drug of the at least one covered additional alternative drug and the one or more covered alternative drugs having the lowest alternative drug value.

Abstract: Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.

Abstract: An apparatus for diagnosing a medical condition including at least a processor and a memory, the memory containing instructions configuring the at least a processor to receive a medical image that is the result of a medical imaging procedure and train a medical image classifier and a medical report classifier. Training the classifiers includes receiving training data, including a plurality of prior medical images and a plurality of prior medical reports, training the medical image classifier and the medical report classifier, and optimizing both classifiers using a common loss function. The memory further containing instructions configuring the at least a processor to generate a label for the medical image, including inputting the medical image into the medical image classifier and receiving the label as output from the medical image classifier.

Abstract: A computer-implemented method includes defining model attributes including a training iteration value that defines a set of training iterations to be used in machine learning to associate portions of feedback data with a set of topic groups based on similarities in concepts conveyed in the feedback data. The method includes removing at least some of the confidential information from the feedback data. The method includes receiving a topic model number selection that indicates a subset of the set of topic groups. The method includes using machine learning to train a machine model based on the model attributes and the topic model number selection. The method includes generating a display showing at least one of a topic cluster graph or a word cloud based on the machine model.

Abstract: A model-assisted system for identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Abstract: Systems and methods are disclosed for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score.

Abstract: A computer system for controlling access to medical resources, the system comprising at least one processor configured to: provide at least one trained machine learning model which has been trained on a training data set comprising healthcare event associated data, the at least one trained machine learning model having an input for receiving at least one operating data set associated with a particular healthcare event and an output for generating at least one value associated with the particular healthcare event; provide the at least one operating data set to the trained model and to receive the at least one value; and in dependence upon the at least one value, generate an indication of an assignment of medical resources for a patient associated with the event.

Abstract: A physiological patient monitoring system with a healthcare professional-facing interface is disclosed. The healthcare professional interface may only display by default the most critical non-confidential patient information. To access the full range of features in the system, clinicians can unlock the device. Once unlocked, clinicians can access all of a patient's treatment history, and all the data may be consolidated and presented intuitively.

Abstract: Embodiments described herein provide an online domain adaptation framework based on cross-domain bootstrapping for online domain adaptation, in which the target domain streaming data is deleted immediately after adapted. At each online query, the data diversity is increased across domains by bootstrapping the source domain to form diverse combinations with the current target query. To fully take advantage of the valuable discrepancies among the diverse combinations, a set of independent learners are trained to preserve the differences. The knowledge of the learners is then integrated by exchanging their predicted pseudo-labels on the current target query to co-supervise the learning on the target domain, but without sharing the weights to maintain the learners' divergence.

Abstract: A system for manufacturing an artificial covering includes a 3D surface scanning module and a 3D printing module where an anatomical surface is imaged and the image is processed for manufacturing and embedding a sensor or device in the covering which can be used for diagnosis or sensing medical issues in the user.

Abstract: Systems, methods, and computer-readable media are provided for patient case and care complexity characterization, and detecting matches of an individual patient's record with collections of other patients' records, based on serial, longitudinal patterns, for facilitating efficient health services utilization, implementing programs to reduce complexity, preventive medicine, and risk management in health care.

Abstract: An input mechanism for generating an input for a search engine, which is of particular use in searching medical databases comprising electrocardiogram (ECG) results. The input for the search engine is constructed in iterative stages, by initializing the input with a placeholder symbol, and iteratively replacing (responsive to a user input) placeholder symbols with input sections. An input section may comprise a variable to be searched, one or more additional placeholder symbols or a function.

Abstract: A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

Abstract: A computer-implemented system includes a treatment device configured to be manipulated by an individual while the individual performs a treatment plan, a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session, and a computing device configured to: identify two or more types of healthcare providers to participate during the telemedicine session in the treatment plan associated with the individual; identify at least one healthcare provider associated with each of the two or more types of the healthcare providers; identify, based on schedules of the identified at least one healthcare provider, one or more available time slots for each respective healthcare provider; identify a first time slot of the available time slots; generate, based on the first time slot, a schedule event; and communicate the schedule event to each of the identified healthcare providers.

Abstract: A matching computer system for electronically generating, matching, and providing online user profiles, and determining a trust score for a user based upon at least social media data and insurance data is provided. The matching computer system may be configured to register users within the matching computer system, receive consent from the users to capture the social media data, and collect the social media data and the insurance data from each registered user. The matching computer system may also be configured to retrieve the social media data and the insurance data associated with each registered user. The matching computer system may be further configured to determine a trust score for each registered user based upon each respective social media data and each respective insurance data. Each trust score represents a level of trustworthiness of the user.

Abstract: The present disclosure contemplates a system and a medical device such as a smartphone or tablet that can operate as a secure medical device for tracking consumption of medications dispensed to patients by gathering and creating medical quality data about consumed medications that are accurate, precise, legally binding, and reliable. This medical quality data can be securely stored in a patient's digital medication diary.

Abstract: A system and method for identifying a plurality of candidates for one or more clinical trials comprising receiving health related data associated with each of the plurality of candidates, applying a predictive risk model to the health related data and for generating and assigning a risk score to each of the plurality of candidates based on a selected condition and then ranking each of the plurality of candidates based on the risk score, and generating a similarity score for each of the candidates having an assigned risk score higher than the threshold risk score with a matching machine learning technique. The similarity score is indicative of a similarity between each of the plurality of candidates and one or more participants in the one or more clinical trials.

Abstract: A method, system, and apparatus for cleansing personally identifiable information from transaction history records. Descriptive text from each transaction entry of the transaction history is converted to tokens and each token is evaluated, on a per-user basis, for repetitiveness and, on a global basis, for uniqueness to compute a metric by which a given token may be indicated as containing personally identifiable information. Ad-hoc rules may further be employed to indicate whether a given token contains personally identifiable information. Tokens indicated as containing personally identifiable information are then masked in a cleansed transaction history output which may be further associated with metadata from the cleansing process.

Abstract: Systems and methods for intelligent medication monitoring are disclosed. The intelligent medication monitoring system includes a question engine, a supply management module, a monitoring module, a personalized adherence scoring module, an intervention module, and an outcome reporting module to provide intelligent medication monitoring, intervention and outcome reporting. The intelligent medication monitoring system reduces waste of unused medication and processing, provides targeted and personalized outreach to patients, and it is able to capture and report outcomes of interventions.

Abstract: A method of relieving fear in an individual, the method can include experiencing at least one fearing event by the individual; obtaining an audio listening device; accessing at least one fear-relieving file corresponding to the at least one fearing event; placing the audio listening device on the individual's ears; playing the at least one fear-relieving file; and listening to the at least one fear-relieving file to relieve the fear in the individual.

Abstract: Each of a first server installed inside a medical institution and a second server installed on a network outside the medical institution includes a server program that receives a processing request for a medical image and that outputs a processing result by executing image processing corresponding to the processing request. An image processing distributor calculates a processing waiting time required until the processing result is obtained in a case where each server executes the image processing on the medical image, based on a required time required for executing the image processing on the medical image, a network connecting situation, and the number of processing requests in a processing waiting state inside each server, and transmits the processing request for the medical image to a server having a shorter processing waiting time.

Abstract: Systems and methods for determining a user's health/wellness condition. Gait-based biometric data from a user is gathered using a sensor module. The biometric data is transmitted to a data processing module that compares characteristics of the biometric data with previously obtained baseline biometric data from the same user. Differences between the current data and the baseline data indicate changes in the user's condition. Databases containing kinematic chain models for the user are used to obtain more accurate and more specific indications regarding determined changes in the user's condition. A base kinematic chain model is created for the user when the user first uses the system and current kinematic chain models are generated for each biometric data set gathered. Characteristics of the base and the current kinematic chain models are compared to determine changes in the user's condition.

Abstract: A handling system suitable for interaction with both a plurality of containers suitable for carrying hot and cold beverages and a plurality of carriers suitable for carrying a plurality of containers; said plurality of containers comprising a base; a walled portion upwardly extending from said base; and a lid for attachment to said walled portion; said plurality of carriers comprising at least one compartment for receiving lids and at least a second compartment for receiving containers; wherein both said carriers and said containers comprise a remotely interrogatable tag for respectively identifying said carrier and said container; wherein said system further comprises a remote processing module; and a plurality of points of interaction with said remotely interrogatable tags which provide one or more of the following factors: a date and time stamp of an interaction, an identification of the point, an identification of an individual interacting with the system and an identification of the container or carrier

Abstract: According to some embodiments, data may be received indicative of a plurality of insurance claims along with an indication of an appropriate claim segment classification for each insurance claim. A first claim handler may then be automatically selected for a first insurance claim based at least in part on: (i) a first segment classification associated with the first insurance claim, (ii) numbers of other insurance claims currently assigned to claim handlers, and (iii) load factors associated with claim handlers. An indication of the selected first claim handler may then be transmitted.

Abstract: Systems for filling and managing a patient's healthcare needs are provided. Some systems and methods described herein relate to a total healthcare fulfillment system. The healthcare fulfillment system of various embodiments is a central portal which coordinates with multiple parties, for example, with physicians, retail pharmacies, mail-order pharmacies, specialty providers, and patients in order to address all of a patient's prescription drug needs or other healthcare needs.

Abstract: Systems for preparing, training, and deploying a machine learning algorithm for making medical condition state determinations include at least one processing unit that includes the machine learning algorithm. The at least one processing unit is programmed to receive image input from an imaging device, receive patient health data, encode the patient health data to convert the patient health data to encoded patient health data, and transmit the encoded patient health data into the machine learning algorithm. Systems are configured to make a medical condition state determination based on the image input and the encoded patient health data, via the machine learning algorithm, and provide visual output for the medical condition state determination via a display device such that the visual output may be augmented with the patient health data. Dynamic state information also may be input to the machine learning algorithm and used to make medical condition state determinations.

Abstract: A method for identity (ID) verification, including: acquiring one or more ID indications of at least one item using a handheld verifying device; automatically verifying by the verifying device a desired relation between the one or more acquired ID indications and stored verification data associated with the verifying device; physically marking the item using the verifying device with a verifying marking if the desired relation is automatically verified.

Abstract: A medical information navigation engine (“MINE”) is capable of inferring referral activity not reported into a referral workflow system by utilizing intent-based clustering of medical information. The intent based clustering reconciles received medical data, from a variety of sources, and then clusters the data by applying one or more clustering rules. After the referrals not otherwise reported are inferred, they may be utilized to generate metrics that can be utilized to enhance patient care, and reduce costs. Metrics may be generated for both in-network and out-of-network referrals in order to distinguish differences in reporting activity.

Abstract: Blockchain latency is improved by unclogging a mempool, which frees up electronic memory and reduces CPU usage and network bandwidth. Mining data of one or more initial blocks of a blockchain is accessed. The mining data reveals, for each miner, the time delay between individual transactions mined by that miner. A subset of miners is then determined to have lower time delays than miners not in the subset. Thereafter, a different random number is generated for each new block of the blockchain system to be mined. Based on a comparison of this random number and a predefined threshold, either an exploitation phase or an exploration phase is entered for the mining of each new block. In the exploitation phase, mining tasks are assigned only to the subset of the miners. In the exploration phase, mining tasks are assigned to both miners within the subset and miners not in the subset.

Abstract: A consumer health records access platform includes a software application for operation on a user device, the system comprising a processor, a tangible, non-transitory electronic memory in electronic communication with the processor a set of computer readable code on the non-transitory electronic memory, including a consumer module configured for use by consumers to support registering consumers for a consumer account pending authentication and approval by a healthcare provider, a provider module configured to enable, via a healthcare provider account, the healthcare provider to look up the consumer account pending authentication and to authenticate and approve or reject creation of the consumer account, a recording agency module configured for use by recording agency representatives to create, via a recording agency account, the healthcare provider account, a consumer health record access database comprising a plurality health records associated on a many to one basis with each of a plurality of consumer acc

Abstract: Various aspects described herein relate to presenting drug dispensing information. Data related to a plurality of dispensing events initiated by one or more employees, of an electronic drug dispensing system can be received. A set of dispensing events of the plurality of dispensing events can be determined as constituting possible misappropriation of drugs by the one or more employees. An alert related to the set of dispensing events can be provided based on determining that the set of dispensing events constitute possible misappropriation of drugs.

Abstract: Systems and methods to determine and ensure proper usage technique of a respiratory medicament device for a respiratory ailment. Medication data relating to a medication plan for a patient is collected and used to determine the respiratory medicament device type in use. For respiratory medicament device types requiring multiple consecutive uses, use data is collected and used to determine proper or improper use technique based on the timing between consecutive uses of the respiratory medicament device. Notifications of proper or improper usage technique are provided based on the determined usage technique. Real-time monitoring of the use data is used to provide directions on the required timing of consecutive uses of the respiratory medicament device and thus ensure proper usage technique.