Abstract: A medical communication system including a first medical device with a memory storing first additional information to be added to first information related to a subject, a DICOM server, and a second medical device storing second additional information to be added to second information. The first medical device having a processor generating DICOM (Digital Imaging and Communication in Medicine)-format data of the first information, a first transmitter/receiver communicating the second information and at least a portion of the second additional information between the first medical device and the second medical device via a network defined by a prescribed protocol different from the DICOM protocol. The processor generates DICOM-format data of the second information based on at least the second additional information. The second transmitter/receiver communicates generated DICOM-format data of the first information and the second information with the DICOM server via the network defined by the DICOM protocol.

Abstract: A telemedicine device, communicated with a telemedicine information acquiring device, includes a communication unit and an operation unit. The communication unit is configured to communicate with the telemedicine information acquiring device, and receive physiological sign information of a patient from the telemedicine information acquiring device. The operation unit is configured to restore at least part of the physiological sign information according to the physiological sign information received by the communication unit. Also are provided a telemedicine information acquiring device and a telemedicine method.

Abstract: Methods for selecting treatment paths are disclosed generally comprising the steps of: (a) discovering a set of treatment path clusters based on latent patterns in historical patient trace data, (b) building a set of binary classifiers based on historical patient trace data, historical patient data and target outcomes, and (c) given the treatment path clusters, actual patient data, and a selected target outcome, applying the binary classifiers to predict a treatment path for a new patient that optimizing the selected target outcome. Processor based systems to implement the methods are also disclosed.

Abstract: A terminal device configured to be able to communicate with a measuring device for measuring biological information includes a first processing unit executing the processing of a first application and a second processing unit executing the processing of a second application. The first processing unit stores biological information acquired from the measuring device. The second processing unit determines whether the second application is able to use biological information, based on specifying information for specifying an application able to use biological information and the identification information of the second application. When the second application is able to use biological information, the second processing unit reads out biological information from the biological information storage unit and outputs the read biological information.

Abstract: There is provided a method for generating instructions for adjusting a medical treatment of a patient, comprising: detecting an indication of a new symptom appearing in a patient being treated with medications for medical condition(s), computing a value indicative of risk of the new symptom being an adverse drug reaction (ADR) of one or a combination of the medications, detecting an indication of another new medication for treating the patient, computing likelihood of PC when the new medication is for treating the new symptom and the value is according to a requirement, generating a request for substitute medication(s) for the one or combination of medications when likelihood of PC is detected, and generating instructions for adjusting treatment of the patient by substituting the one or combination of medications with the substitute medication(s), and for terminating administration of the new medication or avoiding administration of the new medication.

Abstract: Mechanisms are provided that implement a drug-adverse event causality evaluation engine to identify human resource selections based on a readability of unstructured text within an individual case safety report (ICSR) and a confidence value of the ICSR. The drug-adverse event causality evaluation engine receives the ICSR from a cognitive system. The drug-adverse event causality evaluation engine analyzes the ICSR to determine a readability value of the ICSR. The drug-adverse event causality evaluation engine determines whether or not an assessment, by a human reviewer, of the ICSR is required based on a combination of the readability value of the ICSR and the confidence value. The drug-adverse event causality evaluation engine outputs an indication of whether human reviewer assessment is required.

Abstract: Mechanisms are provided that implement a drug-adverse event causality evaluation engine to identify human resource selections based on a readability of unstructured text within an individual case safety report (ICSR) and a confidence value of the ICSR. The drug-adverse event causality evaluation engine receives the ICSR from a cognitive system. The drug-adverse event causality evaluation engine analyzes the ICSR to determine a readability value of the ICSR. The drug-adverse event causality evaluation engine determines whether or not an assessment, by a human reviewer, of the ICSR is required based on a combination of the readability value of the ICSR and the confidence value. The drug-adverse event causality evaluation engine outputs an indication of whether human reviewer assessment is required.