Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.

Abstract: A method, computer program, and computer system for detecting and classifying atrial fibrillation by receiving data corresponding to an electrocardiogram (ECG) associated with a patient, extracting RR intervals from the received ECG data, determining previous RR intervals for each of the extracted RR intervals and aggregating features associated with the RR intervals and the previous RR intervals. Patterns associated with the aggregated features are classified, and the computer may determine that a sample of the ECG data corresponding to one or more of the classified patterns contains a pattern associated with atrial fibrillation.

Abstract: A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Abstract: A method, computer program, and computer system is provided for text error identification and correction. A text input having a phonetic component and a glyphic component is received. Information corresponding to the phonetic component and the glyphic component is coded as a fixed-length sequence. One or more candidate replacement words corresponding to the fixed-length sequence are identified. At least a portion of the text input is replaced with a candidate replacement word from among the one or more candidate replacement words.

Abstract: Provided are a method and an apparatus for receiving a query including a sequence of words. The sequence of words is converted into a sequence of feature vectors. A semantic meaning of the sequence of words is generated using a bi-directional long-short term memory (LSTM). The LSTM is regularized using a Gaussian mixed model (GMM) based on the generated semantic meaning of the sequence of feature vectors.

Abstract: A method, computer program, and computer system is provided for text error identification and correction. A text input having a phonetic component and a glyphic component is received. Information corresponding to the phonetic component and the glyphic component is coded as a fixed-length sequence. One or more candidate replacement words corresponding to the fixed-length sequence are identified. At least a portion of the text input is replaced with a candidate replacement word from among the one or more candidate replacement words.

Abstract: A method, computer program, and computer system is provided for query matching of sentences based on co-attention scores. Two or more query inputs to a neural network are received. A correlation factor between the two or more query input is identified based on attention weights. A feature vector is generated based on the attention weights. A probability value corresponding to the two or more query inputs is determined based on the generated feature vector.

Abstract: There is included an apparatus and system including an intra-heartbeat (HB) extraction code configured to extract intra-HB features from electrocardiography (ECG) signals, and an inter-HB extraction code configured to extract inter-HB features from the ECG signals, and at least one attention mechanism code configured to control at least one of the intra-HB extraction code and inter-HB extraction code based on at least one attention mechanism.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.

Abstract: A method, computer program, and computer system for detecting and classifying atrial fibrillation by receiving data corresponding to an electrocardiogram (ECG) associated with a patient, extracting RR intervals from the received ECG data, determining previous RR intervals for each of the extracted RR intervals and aggregating features associated with the RR intervals and the previous RR intervals. Patterns associated with the aggregated features are classified, and the computer may determine that a sample of the ECG data corresponding to one or more of the classified patterns contains a pattern associated with atrial fibrillation.

Abstract: A method and device for performing electrocardiography (ECG) analysis, the method including receiving ECG data that is from one or more leads, generating an image based on the ECG data, obtaining a feature map based on the image, inputting the feature map to a first neural network, the first neural network configured to generate an output based on the feature map inputted, inputting the output of the first neural network to a second neural network, the second neural network configured to obtain at least one temporal feature of the image based on the output of the first neural network and a previous state of the second neural network, and classifying a signal included in the ECG data based on the at least one temporal feature obtained by the second neural network.

Abstract: A method and apparatus for performing a biosignal analysis task using a set of models includes receiving an input biosignal. Information that identifies the biosignal analysis task to be performed in association with the input biosignal is received. A waveform model and a digital signal processing (DSP) model are selected. A first type of feature and a second type of feature of the input biosignal are identified. An analysis model is selected, and the biosignal analysis task is performed using the analysis model.

Abstract: A method of medical data auto collection segmentation and analysis, includes collecting, from a plurality of sources, unstructured medical data in a plurality of formats, recognizing a medical name entity of each piece of the unstructured medical data, using a medical dictionary, and performing semantic text segmentation on each piece of the unstructured medical data so that each piece of the unstructured medical data is partitioned into groups sharing a same topic. The method further includes generating, as structured medical data, each piece of the unstructured medical data of which the medical name entity is recognized, each piece of the unstructured medical data being partitioned into the groups, and indexing the structured medical data into elastic search clusters.

Abstract: Provided are a method and an apparatus for receiving a query including a sequence of words. The sequence of words is converted into a sequence of feature vectors. A semantic meaning of the sequence of words is generated using a bi-directional long-short term memory (LSTM). The LSTM is regularized using a Gaussian mixed model (GMM) based on the generated semantic meaning of the sequence of feature vectors.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: A method for processing unstructured Chinese-language medical text includes identifying a medical entity in the unstructured Chinese-language medical text using an attention-based named-entity recognition (NER) model, structuring the identified medical entity using a multiple-dimensional entity understanding framework, normalizing the structured medical entity using a medical knowledge graph, and outputting the normalized medical entity.

Abstract: A method of medical data auto collection segmentation and analysis, includes collecting, from a plurality of sources, unstructured medical data in a plurality of formats, recognizing a medical name entity of each piece of the unstructured medical data, using a medical dictionary, and performing semantic text segmentation on each piece of the unstructured medical data so that each piece of the unstructured medical data is partitioned into groups sharing a same topic. The method further includes generating, as structured medical data, each piece of the unstructured medical data of which the medical name entity is recognized, each piece of the unstructured medical data being partitioned into the groups, and indexing the structured medical data into elastic search clusters.

Abstract: A method and apparatus include receiving information associated with a bug bite. Information that identifies the bug bite is determined using a model, based on receiving the information associated with the bug bite. Information that identifies the bug bite is provided, based on determining the information that identifies the bug bite.