Abstract: A system for generating an alimentary plan is disclosed. The system comprises a computing device which is configured to receive an input that includes physiological data related to a skin sample. Computing device is configured to extract a plurality of biological indicators related to disease state from the physiological data. Computing device is configured to determine a biological indicator score for each biological score for each biological indicator of the plurality of biological indicators. Computing device is configured to generate a skin disorder classifier by receiving skin disorder training data. The computing device is configured to classify, using the skin disorder classifier, the at least one biological indicator and the biological indicator score to a positive result for a skin disorder. Computing device is configured to generate an alimentary plan as a function of the positive result. A method for generating an alimentary plan is also disclosed.

Abstract: A medication inventory system may include a medication tray that includes partitions defining compartments. The medication tray has a tray identifier associated therewith, and each compartment is for storing a respective medication having a radio frequency identification (RFID) tag associated therewith. The system may include an RFID tag reader, and a processor and an associated memory configured to maintain in the memory a desired respective medication within each compartment based upon the tray identifier, and cooperate with the RFID tag reader to determine the respective medication within each compartment based upon the respective RFID tag. The processor and associated memory may also be configured to determine a discrepancy between the determined medication within each compartment and the desired respective medication within each compartment, and when so generate a display notification that includes a visual indicator corresponding to each compartment determined to have the discrepancy.

Abstract: A computer-implemented method for providing a real-time estimate of glycosylated hemoglobin (HbA1c) of a patient from a self-monitoring blood glucose (SMBG) measurement, and tracking changes in average glycemia of said patient over time is disclosed.

Abstract: A clinical information extraction and training mechanism is provided for automatically extracting and identifying information in plain text narratives in a set of electronic medical records. The mechanism segments each clinical note in a plurality of clinical notes into one or more identified sections, labels each identified section with an associated tag, and generate a tag data structure utilizing explicitly tagged sequences of sentences and associated tags. The mechanism performs statistical analysis of the identified sections that contain tags identified in the tag data structure to identify one or more valid stop/start conditions; extracts a first set of positive examples of sentences for a selected type of information, and then trains a cognitive system to identify sentences in the plurality of clinical notes that fail to have a tag associated with the selected type using the positive examples of sentences for different types of information.

Abstract: Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.

Abstract: Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

Abstract: Instructions (108) cause a processor (104) to: classify a clinical report for a subject under evaluation by one of anatomical organ or disease; identify and retrieve clinical reports for the same subject from the healthcare data source(s); group the retrieved clinical report by one of anatomical organ or disease; select a group of the clinical report, wherein the group includes reports for a same or related one of the anatomical organ or the disease; build a model that predicts semantic relationships between nodes in the reports in the selected group of reports based on one or more of extracted parameters or keywords; compare one of the parameter values or the keywords across the reports using the model; construct a graphical timeline of the reports; highlight differences in the parameter values or the keywords based on a result of the compare; and visually present the graphical timeline with the highlighted differences.

Abstract: A screen output control unit of a medical checkup result output server transmits a medical checkup result display screen to a client terminal. An improvement proposal configured to include an abnormal item, which is a measurement item having a measurement value in the abnormal range, and an improvement action, which is an action corresponding to the abnormal item and performed in a case where there is a significant improvement in the measurement value, is displayed on the medical checkup result display screen. A non-extraction item, which is an abnormal item for which no improvement action is extracted, is not displayed on the medical checkup result display screen.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizing monitoring programs involving remote devices. In some implementations, at least one of subject attribute data describing characteristics of subjects or subject outcome data including results from monitoring programs that involved the subjects are extracted from a subject database. The subjects are grouped into different groups according to levels of similarity among attributes of the subjects or monitored outcomes for the subjects. A profile is constructed for a group which includes defining inclusion criteria for the group based on aggregate data for the subjects in the group. Data sets are used in the subject database to characterize, for each profile, differing effects of elements of monitoring programs on program compliance outcomes for subjects that satisfy the criteria of the profile.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of storing, in a pharmacy database of a computer system, pharmacy account information, wherein the pharmacy account information comprises: a unique patient ID associated with a customer; a pharmaceutical drug record indicating a pharmaceutical drug prescribed to the customer; and an action record indicating triggering actions of the pharmaceutical drug; determining, by the computer system, that the customer is entering or has entered a retail store; after determining that the customer is entering or has entered a retail store: confirming, by the computer system, that the customer has at least one prescription fill order pending with a pharmacy of the retail store; prioritizing, by the computer system, the at least one prescription fill order to a top of a workflow of a pharmacist in the pharmacy of the retail store;

Abstract: A technique relates to coronary reconstruction. Angiogram data associated with cardiac catheterization of an artery for a subject is received, where a contrasting agent is used during the cardiac catheterization. Frames that match from the angiogram data are selected, the frames being from different views. Two-dimensional ordered point clouds for the frames are formed. A three-dimensional ordered point cloud of a reconstructed coronary artery for the subject from the two-dimensional ordered point clouds of the frames is formed. Observed contrast motion caused by the contrasting agent onto the reconstructed coronary artery is mapped, the reconstructed coronary artery thereby providing a three-dimensional model of the artery of the subject during the cardiac catheterization.

Abstract: Systems and methods for assisting a user in discovering nearby medical services are disclosed. A method includes identifying a user based on matching of at least one unique identity or biometric details of the user with data stored in a database. Details of an event data may be received from the user. A current geographical location of the user may be determined. Relevant support network data may be identified based on the event data and the current geographical location of the user. The support network data may include data related to pharmacies, doctors, hospitals, clinics, third parties, insurance, and payment agents. Thereafter, the relevant support network data may be presented to the user in form of an event map. The event map may include details related to the event data and the user preferences.

Abstract: The application of deep machine learning controllers to derive models of phenotypic patient data from primary economic data is disclosed herein. The use of systems and methods of employing the model are disclosed and useful in predicting treatment outcomes and compound efficacy, suggesting treatment plans and compounds, and clinical studies and phenotypical correlation studies in conjunction with medical records, economic data sets or combinations thereof.

Abstract: Disclosed is a system for documenting clinical trials, the system when operated identifies at least one publication related to a clinical trial entry to obtain documented clinical trial. The system comprises: an information repository comprising plurality of publications; a clinical trials registry database comprising a plurality of clinical trial entries; and a server arrangement. The server arrangement is configured to: obtain the plurality of publications; analyze the plurality of publications, using a filtering module, to determine a filtered set of publications; obtain the plurality of clinical trial entries; analyze a context of each of the publications in the filtered set and each of the plurality of clinical trial entries using a mapping module to identify a relationship of each of the publication with at least one of the plurality of clinical trial entries; and associate the plurality of clinical trial entries with publications related thereto to obtain documented clinical trials.

Abstract: A computer implemented method includes obtaining a schema defining variables representing patient treatment experiences for one or more health conditions, obtaining data from historical patient treatment experiences, the data being arranged in accordance with the schema, training a machine learning system based on a training set of the data for a specified time-period, and generating a model from the trained machine learning system, the model being based on variables selected by the trained machine learning system to predict patient treatment experiences for the one or more health conditions.

Abstract: Generally described, one or more aspects of the present application relate to enabling determination of a predicted surgical efficacy of a proposed surgical intervention and a predicted pattern of post-operative recovery for the patient. More specifically, the present disclosure provides a system that can analyze the patient data of a plurality of patients data, generate analytics data indicating the post-operative recovery of the plurality of patients, and store the analytics data in association with the biological traits of the patients and the types of surgical interventions that the patients had. Subsequently, the system can analyze the patient traits and activity data of a patient, and output, based on the previously generated analytics data, a prediction of how efficacious a given surgical intervention might be for that patient and/or how the patient might recover from the given surgical intervention.

Abstract: Systems, methods, and apparatus are disclosed aggregating at least two of vital signs data, diagnostic test results, medical data, and user parameters into a single medical record. In one example, a method may include periodically receiving vital signs data and/or diagnostic test results performed on a user; periodically receiving medical data in response to a medical examination of the user performed by a health care practitioner; receiving user parameters about the user; aggregating the vital signs data and/or diagnostic test results, the medical data, and the user parameters into a single medical record; associating the single personal medial record with the user; and storing the single personal medial record into a database of personal medical records. Life expectancy can be calculated. Data aggregation can be performed over time for comparison in real time to medical standard values allowing the user to set personal goals towards greater life expectancy.

Abstract: An artificial intelligence (AI) system and methods for management of an autoimmune or inflammatory condition, disorder or disease in a patient for the diagnosis, prognosis, or risk assessment of symptoms thereof. The AI system includes patient and provider applications and a payer application interface accessible via a communications network. The system contains a data-driven recommendation engine using machine learning and/or deep learning based on active monitoring of patients with an autoimmune or inflammatory-related condition, disorder, or disease. The system can alert clinicians to an impending symptom flare and provide a treatment solution that reduces symptom severity, reduces or eliminates the onset.

Abstract: Methods, systems, and computer-readable media for a system and method are provided for assessing the value of predictive models monitoring medical conditions. Trends in an individual's medical condition are determined based on monitoring values, and the trends are associated with the actions performed in response to the monitoring values and in accordance with the predictive models. The trends may indicate that an individual's condition is improving, worsening, or remaining stable in response to the action taken. Models used for multiple conditions for an individual or for a population of individuals may be assessed in this manner to generate knowledge regarding the performance of the models based on the actions taken. This knowledge may be used to assess the value of the models in terms of the models' performance and may provide insight on way to improve the models.

Abstract: A gamified pharmaceutical packaging, dispensing system, and methodology is disclosed. The innovative gamified smart packaging system and methodology provides a variety of incentives to patients and relevant teams to take their medications, on time, and at the correct dosage. The system comprises monitored pharmaceutical dispensing devices that communicate with a plurality of compute devices, or smartphones. Each time the patient dispenses the medicine from the dispensing device, a signal is transmitted to the compute device. Upon receiving the signal, the compute device records the dispensing event, calculates a specific “score” for that event, and reports the patient's cumulative “score” of successfully dispensing the medicine. If a dispensing event signal is not received in a timely fashion, the system sends a prompt to the patient to take their medication. Accumulating points by properly taking the prescribed medication allows the patient and/or team to be eligible to receive various benefits.