Abstract: A first terminal determines a collection date of a somatic cell based on a desired collection date and a collectable date transmitted by a second medium; and outputs the collection date to a first medium. A second terminal outputs the collectable date to the second medium; and determines an acceptance date based on the collection date transmitted by the first medium and a production period of an iPS cell transmitted by a third medium. A third terminal determines the production period based on the collection date and a producible period, and determines a shipment date based on the production period and stockable location and period transmitted by a fourth medium; and outputs the production period to the third medium. A fourth terminal outputs the stockable location and period to the fourth medium; and determines stock location and period based on the production period and the stockable location and period.

Abstract: A system for an iPS cell bank includes a terminal and a server. The terminal receives and sends a production request including a desired collection date of a somatic cell and a customer ID to the server. The server stores a collectable date for collecting the somatic cell, a producible period for producing an iPS cell, and a location and a stockable period for stocking the iPS cell; and determines: a collection date based on the desired collection date and the collectable date; a production period based on the collection date and the producible period; an acceptance date of the somatic cell based on the collection date and the production period; a stock location and a stock period based on the production period, the stockable location, and the stockable period; and a shipment date of the iPS cell based on the production period, the stockable location, and the stockable period.

Abstract: Disclosed medical data processing method for planning an external ventricle drainage placement, wherein the method comprises executing, on at least one processor (3) of at least one computer (2), the following steps: a) patient image data describing at least one image of an anatomical structure of a patient is acquired (S1); b) atlas data describing a model of the anatomical structure is acquired (S2); c) mapping data describing a mapping of the patient image data to the atlas data is determined (S3); d) spatial relationship data is acquired (S4) which describes a predetermined spatial relationship between at least one potential trajectory for placing the external ventricle drainage in the anatomical structure on the one hand and a surface of the anatomical structure on the other hand; e) entry point data is determined (S5) which describes the position of an entry point (13) of the external ventricle drainage on the surface of the anatomical structure based on the mapping data and the spatial relationship dat

Abstract: Embodiments of the present systems and methods may provide techniques to predict the success or failure of a drug used for disease treatment. For example, a method of determining drug efficacy may include, for a plurality of patients, generating a directed acyclic graph from health related information of each patient comprising nodes representing a medical event of the patient, at least one first edge connecting the first node to an additional node, each additional edge connecting nodes representing two consecutive medical events, the edge having a weight based on a time difference between the two consecutive medical events, capturing a plurality of features from each directed acyclic graph, generating a binary graph classification model on captured features of each directed acyclic graph, determining a probability that a drug or treatment will be effective using the binary graph classification model, and determining a drug to be prescribed to a patient based on the determined probability.

Abstract: A medical condition based content filter mechanism is provided that analyzes patient information associated with a patient to identify medical condition(s) associated with the patient and correlating the medical condition(s) with one or more medical condition content indicator data structures specifying negative content indicators and/or positive content indicators. A user specific content indicator dictionary data structure (USCID) is generated based on the correlation and used to process received content to filter out portions of the content matching the negative content indicators or present portions of the content matching the positive content indicators, thereby generating modified content that is output to the patient via a content access application executing on a computing device associated with the patient.

Abstract: A stem cell manufacturing system for manufacturing stem cells from somatic cells includes: one or more closed production device(s) configured to produce stem cells from somatic cells; one or more drive device(s) configured to be connected with the production device(s) and drive the production device(s) in such a manner as to maintain the production device(s) in an environment suitable for producing stem cells; one or more cryopreservation device(s) configured to cryopreserve the produced stem cells; a first memory device configured to store whether or not somatic cells have been introduced to the production device(s), as a first state; a second memory device configured to store whether or not the production device(s) is/are connected with the drive device(s), as a second state; and a third memory device configured to store whether or not the produced stem cells can be placed in the cryopreservation device(s), as a third state.

Abstract: Embodiments of the present systems and methods may provide techniques to predict the success or failure of a drug used for disease treatment. For example, a method of determining drug efficacy may include, for a plurality of patients, generating a directed acyclic graph from health related information of each patient comprising nodes representing a medical event of the patient, at least one first edge connecting the first node to an additional node, each additional edge connecting nodes representing two consecutive medical events, the edge having a weight based on a time difference between the two consecutive medical events, capturing a plurality of features from each directed acyclic graph, generating a binary graph classification model on captured features of each directed acyclic graph, determining a probability that a drug or treatment will be effective using the binary graph classification model, and determining a drug to be prescribed to a patient based on the determined probability.

Abstract: An application operating on a portable computing device that generates a suggested hypothesis of at least one of: a diagnosis, a treatment, and subsequent medical investigation. The application receives quantified subfactors, collectively defining factors, for each patient. Subfactors of the medical condition are related to the history, symptoms, signs, tests, and responses to the treatment. The application determines an odds ratio for each factor regarding each hypothesized medical condition as well as an associated prevalence. The application can also determine a respective sensitivity and specificity. Posterior probability distribution, such as using Bayesian statistics, can be applied using odds ratios, prevalence, sensitivity specificity, and the associated correlations to determine the suggested hypothesis of the diagnosis, treatment, and any next tests.

Abstract: A system (100) for detecting and labeling structures of interest includes a current patient study database (102) containing a current patient study (200) with clinical contextual information (706), a statistical model patient report database (104) containing at least one or more prior patient documents containing clinical contextual information (706), an image metadata processing engine (118) configured to extract metadata for preparing an input for an anatomical structure classifier (608), a natural language processing engine (120) configured to extract clinical context information (706) from the prior patient documents, an anatomical structure detection and labeling engine (718) or processor (112), and a display device (108) configured to display findings from the current patient study.

Abstract: A method for facilitating integration of patient electronic healthcare record information from a community aggregator with electronic healthcare record information native to an electronic healthcare record (EHR) application includes, for each of a plurality of accessed community data items, comparing the community data item to native data items, and based on the comparison, identifying the community data item as a duplicate, a potential duplicate, or not a duplicate, and for each community data item which was not identified as a duplicate, comparing the community data item to entries in one or more native clinical dictionaries, and, based on the comparison, identifying the community data item as an exact match to a clinical dictionary entry, a potential match to one or more clinical dictionary entries, or not matched to any clinical dictionary entry.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for pharmacology model optimization based on distributed data acquisition. A computer system stores a first pharmacology model associated with a drug dosage model for a particular medication. The computer system receives patient data including pharmacological data and data values for a drug identifier and a pharmacology model identifier. The pharmacological data being associated to drug dosing for the particular medication. The computer system aggregates the received data into a first data set based on the drug identifier value. The computer system optimizes the first pharmacology model using the first data set, thereby generating a second pharmacology model. The computer system provides access to the optimized second pharmacology model.

Abstract: A method includes obtaining source data containing feedback information, identifying different phrases of interest in the feedback information, generating count data that indicates how often the different phrases of interest appear in the feedback information, determining counts of how often the phrases of interest appear in the feedback information, and modifying a count that is representative of how often at least one phrase of interest appears in the feedback data. The count is modified by reducing the count by a count of how often another, shorter phrase of interest also appears in the feedback data. The method also includes generating at least one interface respectively reflecting the count data for the different phrases of interest and the count that has been modified.

Abstract: A system including a healthcare risk engine to provide a healthcare risk knowledge graph from open data and knowledge data related to risk by using risk-related terms to retrieve documents from open data and by extracting the healthcare risk knowledge graph as entities from the documents and links between the entities. A patient risk graph predictor predicts risks for a patient by combining information in a Patient Clinical Object (PCO) with entities in the healthcare risk knowledge graph to produce a patient risk graph. An impact estimator estimates an impact of a treatment by taking the treatment and adjacent nodes from the healthcare risk knowledge graph to form a healthcare treatment subgraph, finding an entity and adjacent nodes in the patient risk graph to form a patient treatment subgraph, providing an impact graph by combining the patient treatment subgraph and the healthcare treatment subgraph, and retaining resultant linked nodes.

Abstract: A system for assessing patient risk using open data and input of knowledge data, the system including a healthcare knowledge data input to receive open data and a knowledge input to accept input of knowledge data relating to risk; a healthcare risk engine to provide a healthcare risk knowledge graph from the open data and knowledge data by using input of risk-related terms to retrieve documents from the open data and by extracting the healthcare risk knowledge graph as entities from the documents corresponding to risk-related terms, as well as links between the entities. A patient risk graph prediction module predicts risks for a patient by combining information in a Patient Clinical Object (PCO) with entities in the healthcare risk knowledge graph to produce a patient risk graph.

Abstract: Mechanisms are provided for implementing a patient health management system. The mechanisms analyze a patient electronic medical record (EMR) for an identified patient to identify a medical condition associated with the patient and identify, based on the results of the analysis, one or more exogenous data parameters associated with the medical condition. The exogenous data parameters are parameters specifying conditions outside the patient's body that affect the health of the patient with regard to the medical condition. The mechanisms retrieve exogenous data, corresponding to the exogenous data parameters, from one or more exogenous data sources and generate a health management plan for the patient based on the exogenous data. The health management plan comprises actions to be performed or not performed by the patient to thereby minimize effects of exogenous conditions, corresponding to the exogenous data parameters, on the health of the patient with regard to the medical condition.

Abstract: A method, system and program product comprise accessing a system having a digital currency infrastructure. At least one user address is created. Genetic and health related user data is prepared. The user data is transferred to the system wherein the system links the user data and the user address.

Abstract: A medical legal workflow management system and a medical legal workflow management process are disclosed. The medical legal workflow management system and process transfers scheduling, appointment notices, exam notes, billing invoices, and financial reports to all State and Federal forms based on a single data entry input.

Abstract: Embodiments include methods, systems and computer program products for reducing feature space in analysis of medical data. Aspects include receiving patient temporal traces. Aspects also include conducting sequential pattern mining on the patient temporal traces to produce sequential features. Aspects also include clustering the sequential features with a similarity metric. Aspects also include analyzing the clustered features to predict a healthcare outcome.

Abstract: Presented herein are systems and methods for merging and manipulating data from different sources of clinical trial data. Clinical trial data is collected using multiple different ‘forms’ and can be from either a single clinical trial or from multiple clinical trials. In certain embodiments, the systems and methods described herein are provided in the form of an intuitive graphical user interface (GUI) that enables a user to merge and manipulate data from two or more source tables of clinical trial data associated with one or more clinical studies to produce a custom merged table, without having to rely upon complex computer code.

Abstract: A first terminal determines a collection date of a somatic cell based on a desired collection date and a collectable date transmitted by a second medium; and outputs the collection date to a first medium. A second terminal outputs the collectable date to the second medium; and determines an acceptance date based on the collection date transmitted by the first medium and a production period of an iPS cell transmitted by a third medium. A third terminal determines the production period based on the collection date and a producible period, and determines a shipment date based on the production period and stockable location and period transmitted by a fourth medium; and outputs the production period to the third medium. A fourth terminal outputs the stockable location and period to the fourth medium; and determines stock location and period based on the production period and the stockable location and period.