Abstract: Provided is a device for ensembling data received from prediction devices and a method of operating the same. The device includes a data manager, a learner, and a predictor. The data manager receives first and second device prediction results from first and second prediction devices, respectively. The learner may adjust a weight group of a prediction model for generating first and second item weights, first and second device weights, based on the first and second device prediction results. The first and second item weights depend on first and second item values, respectively, of the first and second device prediction results. The first device weight corresponds to the first prediction device, and the second device weight corresponds to the second prediction device. The predictor generates an ensemble result of the first and second device prediction results, based on the first and second item weights and the first and second device weights.

Abstract: Disclosed herein a method and apparatus for learning a multi-label ensemble based on multi-center prediction accuracy. According to an embodiment of the present disclosure, there is provided a multi-label ensemble learning method comprising: collecting a prediction value for learning data for each of a plurality of prediction models; calculating a prediction error of each of the prediction models using the prediction value of each of the prediction models and a correct answer prediction value; generating a weight label for each of the prediction models based on the prediction error; and learning an ensemble weight prediction model for predicting a weight of each of the prediction models using the weight label.

Abstract: Disclosed are a method and apparatus for selective ensemble prediction based on dynamic model combination. The method of ensemble prediction according to an embodiment of the present disclosure includes: collecting prediction values for input data of each of the prediction models; calculating a model weight of each of the prediction models using a pre-trained ensemble model that uses the prediction value as an input; selecting at least some model weights from the model weights using a predetermined optimal model combination parameter; and calculating an ensemble prediction value for the input data based on the selected model weight and a prediction value of a prediction model corresponding to the selected model weight.

Abstract: Disclosed is an operation method of a health state prediction system which includes an ensemble prediction model. The operation method includes sending a prediction result request for health time-series data to a plurality of external medical support systems, receiving a plurality of external prediction results associated with the health time-series data from the plurality of external medical support systems, generating long-term time-series data and short-term time-series data for each of the health time-series data, and the plurality of external prediction results, extracting a plurality of long-term trends based on the long-term time-series data, extracting a plurality of short-term trends based on the short-term time-series data, calculating external prediction goodness-of-fit based on the plurality of long-term trends and the plurality of short-term trends, and generating an ensemble prediction result based on the external prediction goodness-of-fit and the plurality of external prediction results.

Abstract: Disclosed is a device which includes a data manager, a learner, and a predictor. The data manager generates output data based on time-series data, receives device prediction results corresponding to the output data from the prediction devices, and calculates device errors based on the difference between device prediction results and time-series data. The learner may adjust a parameter group of a prediction model for generating device weights, based on device prediction results and device errors. The predictor generates the ensemble result of first and second device prediction results based on device weights.

Abstract: Provided is a device for ensembling data received from prediction devices and a method of operating the same. The device includes a data manager, a learner, and a predictor. The data manager receives first and second device prediction results from first and second prediction devices, respectively. The learner may adjust a weight group of a prediction model for generating first and second item weights, first and second device weights, based on the first and second device prediction results. The first and second item weights depend on first and second item values, respectively, of the first and second device prediction results. The first device weight corresponds to the first prediction device, and the second device weight corresponds to the second prediction device. The predictor generates an ensemble result of the first and second device prediction results, based on the first and second item weights and the first and second device weights.

Abstract: Disclosed herein is an open healthcare apparatus and method in which a user personally performs integrated management of health records including data concerning western medicine, oriental medicine, life logs, etc. health-related big data is built by receiving health information, from which personal information is deleted, under the agreement of the user, and customized health service is provided by various service providers based on the health-related big data. The disclosed apparatus includes a personal cloud repository for storing personal health data, a health-related big data repository for storing public and personal health data on a health case basis, a healthcare platform for storing and analyzing personal health data and for relaying and managing a customized service, and an analysis platform for analyzing user's health based on the information of the health-related big data repository and for providing customized service depending on the analysis result.

Abstract: Disclosed are a method and an apparatus for detecting a translocation. The apparatus acquires BAM for a first pair of chromosomes and a second pair of chromosomes. Also, the apparatus detects a translocation generated in at least one chromosome by selecting at least one translocation case corresponding to the translocation information produced on the basis of BAM from among multiple translocation cases in which a translocation may be generated in at least one of the first and the second chromosomes.

Abstract: The inventive concept relates to a multi-dimensional time series data processing device, a health prediction system including the same, and a method of operating the time series data processing device. A time series data processing device according to an embodiment of the inventive concept includes a network interface, a data generator, a predictor, and a processor. The network interface receives the first time series data having the first type. The data generator generates second time series data having a second type based on the first time series data. The predictor generates prediction data based on the first time series data and the second time series data.

Abstract: Provided are a device and method for processing multi-dimensional time series medical data. The device for processing multi-dimensional time series medical data according to an embodiment of the present invention includes a network interface, a preprocessing unit, a data analysis unit, and a processor. The network interface may receive time series medical data including first visit data corresponding to the first time and second visit data corresponding to the second time before the first time. The preprocessing unit preprocesses the time series medical data to generate the modeling data. The preprocessing unit is configured to preprocess the first visit data based on a difference between the first time and the second time. The data analysis unit may generate a time series analysis model for predicting future visit data from the modeling data.

Abstract: Provided are a device and method for diagnosing cardiovascular disease for providing rapid and accurate treatment and prescription for cardiovascular disease by accurately performing a diagnosis of cardiovascular disease for a particular user using the user's personal health checkup data and genome information measured periodically and the target gene of cardiovascular disease.

Abstract: Provided are a search method and device for searching for a case similar to user's health data at high-speed from large scale multi-dimensional time series health data. The method includes preprocessing health data inputted through an interface circuit, performing a multi-dimensional feature extraction learning based on machine learning on the preprocessed health data, and generating one or more feature extraction models for dimension reduction based on the multi-dimensional feature extraction learning.

Abstract: The present disclosure herein relates to a future health trend forecasting system and a method thereof through a similar case cluster-based prediction model, and more specifically, to a server and a method thereof for extracting multiple associated feature similar case clusters that match a prediction query for the user's health information through a class prediction model and a future value prediction model for health features of a similar case cluster generated by cyclically clustering the target feature that is a health feature for personal health information and an associated feature of the target feature, predicting future health trends for each associated feature using multiple prediction models based on corresponding similar case clusters, and combining and outputting the prediction results.

Abstract: Provided are a clinical decision supporting ensemble system and method. Clinical prediction results for a patient obtained through machine learning and received from a plurality of external medical institutions are integrated to perform an ensemble prediction, so that not only a current condition of the patient but also a future process of an illness of the patient is predicted to assist a medical person in making a quick and correct medical decision.

Abstract: The inventive concept relates to a method and apparatus for predicting health data values through the generation of a health data pattern. The inventive concept provides a health data value prediction method and apparatus. The health data value prediction method and apparatus may select health data values and important health characteristics associated with the health data values from big data on a plurality of pieces of time-series health information. The health data value prediction method and apparatus may form a health data value prediction model that has repetitively learned the pattern, and accurately predict a user's health data value through the prediction model.

Abstract: Provided are a personal cloud based personal health record management method and device for allowing users to dominantly manage personal health records, which are accumulated and managed by each of various health service institutions, through a personal health record management device and a personal cloud repository.

Abstract: Provided are a search method and device in which, in order to search for health data having a multivariate (multi-dimensional) time-series characteristic with high calculation complexity for a search, a format of the health data is converted and a dimension of the health data is reduced through feature extraction to which a learning model is applied, so that the calculation complexity for the search may be remarkably reduced and the similar case search may be performed efficiently.

Abstract: An apparatus and method for counting alleles are disclosed herein. The apparatus for counting alleles includes a file input unit and a counting unit. The file input unit receives one or more files including human genome data. The counting unit reads the files on a predetermined window size basis by means of parallel reading using multi-threading based on the position information of the files, performs allele counting, and merges the results of the counting.

Abstract: Disclosed are a method and an apparatus for detecting a translocation. The apparatus acquires BAM for a first pair of chromosomes and a second pair of chromosomes. Also, the apparatus detects a translocation generated in at least one chromosome by selecting at least one translocation case corresponding to the translocation information produced on the basis of BAM from among multiple translocation cases in which a translocation may be generated in at least one of the first and the second chromosomes.

Abstract: Disclosed herein are an apparatus and method for providing a customized personal health service based on personal health information. The apparatus includes a health information input unit for receiving individual health information, a similar case search unit for calculating weights of the health information for respective features and calculating similarities based on the weights, thus searching for similar cases, a health pattern analysis and future health prediction unit for analyzing patterns of found similar cases and predicting a health pattern of the corresponding individual, a healthcare planning unit for designing individual customized healthcare plans based on the predicted health pattern for the corresponding individual, and a healthcare knowledge base including a knowledge map required for calculation of the weights of the health information for respective features and a knowledge map required for the analysis and prediction of patterns.