Abstract: A system is provided for operation with a facility providing peritoneal dialysis exchanges for multiple peritoneal dialysis patients. The system can include a peritoneal dialysis solution supply, peritoneal dialysis sets to deliver peritoneal dialysis solution to the peritoneal dialysis patients at the facility, a computer, and a treatment station. The computer can electronically read a prescription for a patient, electronically identify a quantity of the peritoneal dialysis solution from the prescription, and electronically identify a peritoneal dialysis set from the peritoneal dialysis sets for use with the identified quantity of the peritoneal dialysis solution.

Abstract: Described are data abstraction systems, methods, and media for aggregating and abstracting data records from data providers, which are not substantially interoperable with each other. Features include data provider connector modules dynamically loaded, based on definitions stored on disk, that facilitate data mapping and individual matching.

Abstract: Systems and methods for reducing acute incident risks for dementia patients are provided. Benefits of the systems and methods include reducing healthcare costs, improving patient and caregiver outcomes and reducing caregiver burden. A server system analyzes data related to a dementia patient and the corresponding caregivers (e.g., family member, paid caregiver, physician) and calculates an acute incident risk. Based on the acute incident risk, the server system determines patient therapies to reduce the acute incident risk and also identifies caregiver education to improve the caregiver belief state, which reduces the acute incident risk. The system periodically re-calculates the acute incident risk and identifies patient therapies and caregiver education and motivation to further reduce or maintain the acute incident risk.

Abstract: A method of building a machine learning pipeline for predicting the efficacy of anti-epilepsy drug treatment regimens is provided.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: Devices, systems, and methods are provided for adherence monitoring and patient interaction. In general, the devices, systems, and methods can facilitate a patient's adherence to a medication schedule and can facilitate monitoring and tracking of the patient's adherence to the medication schedule. In one embodiment, a medical accessory such as a cap is provided that can be configured to attach to existing medication dispensers, such as asthma inhalers, or to be integrated into a custom-made medication dispenser. The accessory can be configured to provide a notification to the patient when medication is due. The accessory can be configured to detect usage of the dispenser. The accessory can be configured to provide data regarding dispensing of the medication to an external device. The data can be analyzed and provided for visualization on a user interface.

Abstract: A method, a system, and a computer program product are provided. A machine learning model is generated to process adverse event information and produce multiple corresponding medical codes associated with the adverse event information, wherein the multiple medical codes are semantically and hierarchically related in a medical taxonomy. The machine learning model includes multiple parallel output layers, each of which is associated with a corresponding medical code. The machine learning model is trained with training data elements, each of which includes adverse event information mapped to respective multiple medical codes, wherein results from each of the output layers adjusts the machine learning model. After completing the training, information pertaining to an adverse event is applied to the machine learning model to determine the corresponding multiple medical codes within the medical taxonomy.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: A data capture and storage apparatus for use in confirming administration of medication in a clinical trial or other medication administration scenario is provided. The apparatus includes a data capture device for capturing one or more types of data related to administration of a medication, a storage device for storing the captured data and a processor for processing the stored captured data. The processor further includes an analysis element for performing a local analysis of the stored captured data.

Abstract: A system for selecting an intervention based on effective age includes configured determine a user endocrinal age factor using at least a measure of user endocrine function, to determine a user telomeric age factor using a user telomere length, determine a user negative habit factor, and to multiply each factor by a user chronological age to obtain a user effective age. The at least a server is configured to derive a user health quality vector listing user priorities including life-expectancy increase. The at least a server is configured to generate a plurality of interventions, each with a vector having similar entries to the health quality vector. The at least a server is configured to select an intervention from the plurality of interventions by generating a loss function of the plurality of interventions and the user health quality vector and minimizing the loss function.

Abstract: A system for informing product decisions. The system includes a computing device configured to locate an article of interest for a user. The computing device is configured to receive a user biological extraction. The computing device is configured to generate using ingredient training data and a classification algorithm an ingredient metabolic classifier. The ingredient metabolic classifier inputs a user biological extraction and outputs an ingredient metabolic profile. The computing device identifies at least an ingredient contained within an article of interest. The computing device determines the tolerability of an article of interest as a function of a metabolic profile and at least an ingredient contained within an article of interest.

Abstract: The local surveillance sub-system recognizes that a patient medical procedure has or will soon commence by sensing the presence of a healthcare professional in or near the surveillance area, and in response, creates a separate patient medical procedure A/V file for the surveillance data that will be captured. A dedicated procedure remote may be provided for receiving manual interactions from HC professionals present for a procedure or, alternatively, the local surveillance sub-system may autonomously interact with a personal security token device possessed by the HC professional. A procedure data file is also created that holds all of the pertinent information concerning the procedure that is known by the local surveillance sub-system. The patient procedure surveillance A/V file is given a higher priority than ordinary surveillance data captured by the local surveillance sub-system and is then copied to a nonvolatile memory.

Abstract: A method, a computing system and a computer program product are provided. A model is generated and trained. The model is based on clinical data with outcomes from clinically-defined hierarchical metadata in a selected level of clinically-defined hierarchical metadata serving as an initial set of prediction targets. A score is determined for each of the prediction targets based on the generated model and the set of evaluation factors. The set of prediction targets, the generated model, and the scores for the set of prediction targets are updated until the updated scores for the updated set of prediction targets satisfy acceptance criteria. The updated generated model, using the updated set of prediction targets, is applied to predict one of a set of updated prediction targets of mutually exclusive outcome categories.

Abstract: A medical scan hierarchical labeling system stores labeling application data that includes application operational instructions and a plurality of prompt decision trees. A medical scan and the labeling application data are sent to a client device for storage. The client device executes the application operational instructions of the labeling application data, causing the client device to display, via an interactive interface, the medical scan and a plurality of prompts of each prompt decision tree in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. The client device transmits labeling data indicating the ultimately selected leaf node of each prompt decision tree. A medical scan entry of the medical scan in a medical scan database is populated based on the set of labels.

Abstract: A system and method for coordinating cooperative diagnosis construction, decreasing diagnostic error, reducing delays and repetition in performing tasks, and improving application of data gathering devices through structured data tracking of symptoms, diagnoses, conditions and records using encrypted, event-based blockchain ledgers distributed throughout a network of processors. Symptom and diagnosis devices are used to collect participant data. Data is encrypted and used to generate a secure chain of decentralized ledger blocks. The secure chain is stored in a network of secure processing devices. Data access is controlled by a gatekeeper. Structured data blocks are integrated, creating a comprehensive digital record that may be shared without sacrificing control. Pathology collections are generated from integrated data.

Abstract: A method for improving a machine learning allergy diagnosis process is provided. The method includes identifying databases comprising data identifying menu items served by food establishments. Allergy data defining a food products associated with potential allergens is analyzed in real time and in response, baseline allergen indication software code for defining an allergen baseline indicator is generated. Ingredients of the menu items are analyzed with respect to the allergy data and allergen baseline levels for the ingredients determined. An allergen baseline level for each menu item is determined and an allergen alert software application for alerting a user with respect to each overall allergen assessment is generated.

Abstract: A system for improving a machine learning allergy diagnosis process is provided. The system implements a method that includes identifying databases comprising data identifying menu items served by food establishments. Allergy data defining a food products associated with potential allergens is analyzed in real time and in response, baseline allergen indication software code for defining an allergen baseline indicator is generated. Ingredients of the menu items are analyzed with respect to the allergy data and allergen baseline levels for the ingredients determined. An allergen baseline level for each menu item is determined and an allergen alert software application for alerting a user with respect to each overall allergen assessment is generated.

Abstract: Embodiments presented herein provide a dynamically adaptive care plan. More specifically, embodiments presented herein describe techniques for modifying a care plan assigned to a patient based on data observed from the patient. According to one embodiment of the present disclosure, data corresponding to one or more types of biometric data for the patient is received. One or more reported symptoms for the patient is received. The care plan includes one or more conditions corresponding to the one or more reported symptoms. Upon determining that at least one of the received data satisfy at least one of the biometric thresholds and the one or more reported symptoms satisfy at least one of the conditions, an event rule for modifying the care plan is identified. The care plan is modified based on the event rule.

Abstract: Provided are mechanisms and processes for a lighting system for medical schedule management. According to various examples, an apparatus is provided which comprises a lighting interface configured to connect to a lighting element for illuminating a medical examination room. The apparatus further comprises a power interface coupled to a power source. The apparatus further comprises a transceiver configured to connect to a device corresponding to a physician. The duration of the connection is used to track the presence of the physician in the medical examination room. The transceiver is tuned to transmit a signal strength corresponding to the size and characteristics of the medical examination room. The apparatus is located in a lighting fixture in the medical examination room. The lighting fixture may be centrally located in the medical examination room.

Abstract: A method is disclosed for providing, by an artificial intelligence engine, an optimal treatment plan to use with a treatment apparatus. The method includes receiving, from a data source, clinical information pertaining to results of using the treatment apparatus to perform particular treatment plans for people having certain characteristics. The clinical information has a first data format. The method also includes translating a portion of the clinical information from the first data format to a medical description language used by the artificial intelligence engine, determining, based on the portion of the clinical information described by the medical description language and a plurality of characteristics pertaining to a patient, the optimal treatment plan for the patient to follow using the treatment apparatus to achieve a desired result, and providing the optimal treatment plan to be presented on a computing device of a medical professional.