Abstract: One or more processors may be configured to detect whether a patient with diabetes is operating a vehicle based on one or more detected gestures. Based on the detection of operating the vehicle, the one or more processors may cause a patient device to output alerts according to a driving alert protocol. In another example, based on the detection of operating the vehicle, one or more processors may cause an insulin pump to operate according to a driving therapy protocol.

Abstract: Hierarchical data objects are generated via a computer-based system for applying a series of rules to establish episode-specific data objects reflecting a plurality of discrete claim records before further dissecting the generated episode-specific data objects prior to finalization of those episode-specific data objects to identify claim records within the episode-specific data objects that are eligible for generation of one or more sub-episodes within the episode-specific data objects. The identified sub-episodes are reflected within the episode-specific data object to designate complete episodes of care that additionally reflect interactions with the corresponding parent episode.

Abstract: Example methods, apparatus, and articles of manufacture to process insurance claims using artificial intelligence are disclosed herein. An example computer-implemented method, executed by a processor, to process an insurance claim using artificial intelligence includes interacting, with the processor, electronically with at least one of an insured person, a representative of the insured person, or a representative of an insured entity to obtain information related to an insurance claim, processing, with the processor, the information with a machine learning engine to identify an action to be taken for the insurance, and performing, with the processor, the action using a backend processing module that is also used by a person to process an insurance claim.

Abstract: Systems and methods are described, and an example system includes an AI enhanced multi-source health data integration logic that receives a first source electronic health record (EHR) data from a first EHR system and a second source EHR data from a second EHR system, and transforms, according to a knowledge representation schema, health-related information content of the first source EHR data and the second source EHR data to a first source transformed health data and second source transformed health data. The system includes a collaboration platform, configured to host a multi-source transformed health data database, including the transformed first source health data and the transformed second source health data, and hosts AI-enhanced, multiple level telecollaborative analyses by a plurality of participants of the multi-source transformed health data database, generating health management data.

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

Abstract: The MIHIC system in various embodiments described herein helps clinicians predict the risk of maternal mortality by detecting diseases early and identifying possible risks in mothers, fetuses and infants across pre, peri and post-natal stages of pregnancy. The system quantifies risk as a single MIHIC score, which through quantification assigns possible risks to the mother, fetus and infant. The MIHIC score uses a specialized algorithm to derive the individual and overall risk as a value between 0 and 1 and uses the risk scores to stratify the patients into High, Medium and Low risk for preventive intervention and improved pregnancy outcome.

Abstract: Techniques are described for providing live first aid response guidance using a machine learning based cognitive aid planner. In one embodiment, a computer-implemented method is provided that comprises classifying, by a system operatively coupled to a processor, a type of an injury endured by a patient. The method further comprises, employing, by the system, one or more machine learning models to estimate a risk level associated with the injury based on the type of the injury and a current context of the patient.

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

Abstract: A prehospital telemedicine system comprises a physiologic monitor; an electronic patient care reporting system (ePCR) system; and a point-of-care blood analyzer communicatively coupled to the physiologic monitor and the ePCR system. The point-of-care blood analyzer is configured to perform an analysis of a blood sample based on an indication of a need for a specific blood analysis provided by one of the physiologic monitor and the ePCR system, and to automatically transmit a result of the analysis to a remote data receiving system. The indication of a need for a specific blood analysis may be based upon any one of the following: vital signs data obtained for a patient by the physiologic monitor; and/or current documentation or past medical history captured on or available through the ePCR system.

Abstract: A system may receive a query for a patient evaluation. The system may then receive historical patient information or hospital data, current medical information, patient information, and testing information. The system may then use the received information to generate a set of rules using a neural network and uses the rules to process an evaluation for the patient.

Abstract: Systems, catheters, and methods for accessing and treating along the central nervous system are disclosed. An example method may manage inflammation of the patient to treat a condition of the patient by processing values related to one or more physiological parameters of a patent, identifying when an inflammation condition of the patient has reached a treatment condition based on the processed values, and automatically providing an indication that the inflammation condition has reached the treatment condition. An example indication may include actuation of a treatment protocol. The example method may be performed with an inflammation management system.

Abstract: A system for a physiologically informed virtual support network includes a computing device a support module operating on the computing device, the support module configured to receive a biological extraction related to a user, wherein the biological extraction comprises an element of user physiological data, generate a request for the user to join a support network as a function of the biological extraction, identify a support network for the user from a plurality of support networks, as a function of the biological extraction; and display to the user on the computing device, the identified support network, and a machine-learning module operating on the computing device, the machine-learning module configured to assess a membership of the plurality of support networks, organize member participants of the plurality of support networks utilizing a first machine-learning process and assign member participants to the plurality of support networks as a function of the first machine-learning process.

Abstract: Computer systems and computer-implemented analysis methods may be used for assistance in planning and/or performing a medical procedure, such as percutaneous nephrolithotomy or percutaneous nephrolithotripsy. The method may include receiving one or more radiographic images of an anatomical structure of a patient, generating a display of the radiographic image(s), generating at least one request for user input to identify features of the anatomical structure, receiving user input identifying the features of the anatomical structure, identifying at least one access plan based on the received user input, and generating a display of the identified access plan(s) associated with the radiographic image(s). The method may include generating a patient template that indicates an insertion site according to the identified access plan(s).

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

Abstract: Device for blood container processing having a data storage reader unit 20 being adapted to read an blood donator identifier from a data storage of a blood container, and a data processing unit 50 being adapted to request for blood donator related information from an data base 163 based on the read blood donator identifier to allow a correct processing of all relevant information with respect to a blood donator and a blood recipient, to match the blood correctly and to avoid any serious injuries on the blood recipient side.

Abstract: A method is provided for lowering blood pressure in a subject in need thereof by administering an angiotensin II receptor blocker pharmaceutical composition to a subject qualified for over-the-counter access to the angiotensin II receptor blocker pharmaceutical composition. In some embodiments, the angiotensin II receptor blocker pharmaceutical composition includes azilsartan medoxomil, candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan, or valsartan. In some embodiments, the angiotensin II receptor blocker pharmaceutical composition comprises an active ingredient that is (5-methyl-2-oxo-1,3-dioxol-4-yl)methyl 2-ethoxy-1-{[2?-(5-oxo-4,5-dihydro-1,2,4-oxadiazol-3-yl)biphenyl-4-yl]methyl}-1H-benzimidazole-7-carboxylate or a pharmaceutically acceptable salt thereof.

Abstract: A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching, storing the system structured data in a second database, for each user application program, selecting the application specific subset o

Abstract: Method and apparatus for predicting amyloid beta (A?) and phosphorylated tau (p-tau) biomarker levels in the cerebrospinal fluid (CSF) of patients. Embodiments include determining current values for a plurality of easily-measurable attributes of a first patient. Embodiments include analyzing data associated with a cohort of patients having known measurements of A? and p-tau biomarker levels, including determined values for the plurality of easily-measureable attributes. Embodiments include generating a predicted value for A? and/or p-tau biomarker levels for the first patient. Embodiments include generating a risk of the first patient developing AD at a future time, generating a probability of a patient's predicted rate of decline, and/or generating a probability of a patient's age at the onset of dementia, based on the predicted values for A? and/or p-tau biomarker levels.

Abstract: Mechanisms are provided for training a hybrid machine learning (ML) computer model to simulate a biophysical system of a patient and predict patient classifications based on results of simulating the biophysical system. A mechanistic model is executed to generate a training dataset. A surrogate ML model is trained to replicate logic of the mechanistic computer model and generate patient feature outputs based on surrogate ML model input parameters. A transformation ML model is trained to transform patient feature outputs of the surrogate ML model into a distribution of patient features. A generative ML model is trained to encode samples from a uniform distribution of input patient data into mechanistic model parameter inputs that are coherent to the target distribution of patient features and are input to the surrogate ML model. Input patient data for a patient is processed through the ML models to predict a patient classification for the patient.