Abstract: The present invention discloses a personal health record system with a process decision support function. By constructing a personal health record system with separated characteristic data extraction and characteristic data analysis, characteristic data that meets analysis requirements is expected to be rapidly obtained. A statistics query interface based on characteristic data and time logic is provided, and decision support for users is completed through steps such as characteristic matching, process classification and statistics evaluation, and process recommendation, so as to make medical big data better execute high-level information analysis and decision support functions.

Abstract: A healthcare system, platform and methods for providing enhanced medical diagnostics and treatment are herein provided. The platform includes a healthcare knowledge graph database for storing a medical hybrid knowledge graph, the hybrid knowledge graph including healthcare data from one or more ontology-based knowledge graphs and clinical data from one on more sources; a data processing engine running a diagnostic algorithm, designed to process a patient condition data and to process a hybrid knowledge graph database, and select one or more pathways on the knowledge graph to generates one or more differential diagnoses for suspected disorders, which relate to the given patient condition; and a user interface to enable an end user to view results output by the data processing engine.

Abstract: According to one embodiment, a diagnosis support apparatus includes processing circuitry. The processing circuitry executes identification processing by using medical information as an input to output a first identification result and a first identification reason for providing the first identification result. The processing circuitry modifies the identification processing to refrain from outputting the first identification reason in response to an instruction to reject the first identification reason. The processing circuitry executes the modified identification processing by using the medical information as an input to output a second identification result and a second identification reason for providing the second identification result.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Abstract: Embodiments for intelligent generation of customized questions or questionnaires by a processor. One or more customized questions or questionnaires may be generated according to a user profile, similar profiles of alternative users, one or more historical interactions with the alternative users, one or more goals defined by a domain experts, domain knowledge, historical questions or questionnaires, or a combination thereof using a machine learning operation.

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.