Abstract: A personal diabetes equipment management system can include at least one diabetes treatment device adapted for use with one or more disposable diabetes devices; a remote server system including an inventory database identifying operational diabetes treatment devices and operational disposable diabetes devices in the possession of a user; and a controller in communication with (i) the at least one diabetes treatment device over a first network connection and (ii) the remote server system over a second network connection, the controller adapted to automatically detect use of the at least one diabetes treatment device and determine an operational condition for the diabetes treatment device or the one or more disposable diabetes treatment devices based on information received from the at least one diabetes treatment device over the first network connection, the controller transmitting update information to the remote server system over the second network connection to update the inventory database.

Abstract: Provided is a system to allocate resources using an information technology infrastructure. The system receives financial and health data of a participant. The system identifies a healthcare expense prediction model to predict the future healthcare expenses of the participant. The system determines from the prediction model using the multi-dimensional feature vector of the participant, the predicted lifetime healthcare expenses of the participant. The system identifies lifetime non-healthcare expenses of the participant. The system identifies a healthcare tax benefit account to provide funds towards the predicted lifetime healthcare expenses and a non-healthcare tax benefit account to provide funds towards lifetime non-healthcare expenses. The system determines a first amount of funds to allocate to the healthcare tax benefit account and a second amount of funds to allocate to the non-healthcare tax benefit account. The system provides the first and second amount of funds to an interactive user interface.

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: The present disclosure relates to systems, methods, and computer-readable storage devices for radiotherapy treatment planning. For example, a method may generate a treatment plan for a patient. The method may receive training data reflecting radiotherapy treatment data. The training data may include a feature vector and a target vector. The method may further determine a training model based on the feature vector and the target vector. The method may further receive testing data associated with the patient. The testing data may include a descriptive feature vector. The method may further determine a therapy model based on the descriptive feature vector and the training model. The therapy model may be used to generate the treatment plan.

Abstract: Embodiments provide a system and method for medical record error detection. Using a cognitive system having natural language processing capabilities, the error detection system can analyze a corpus of medical records, which can correspond to a particular patient, as well as a new medical record, and parse each into one or more record elements. By comparing the record elements, the error detection system can detect one or more potential errors resulting from a record element in the new medical record being inconsistent or contradictory with the record elements in the prior medical records, or ambiguous. The error detection system can output a list of candidate answers for correcting the one or more potential errors based upon a cognitive analysis of the corpus of medical records. The error detection system can further output a list of suggested therapies based on the selection of one or more of the candidate answers.