Abstract: Embodiments of the present disclosure provide a method and an apparatus for automatically discovering medical knowledge. In this method, one or more linking concepts having a semantic relation with a starting concept are obtained from a medical literature library. The starting concept represents a disease. Next, one or more target concepts having a semantic relation with the one or more linking concepts are obtained from the medical literature library, and an association degree of each of the one or more target concepts with respect to the starting concept is calculated. The association degree indicates a probability that the target concept can cope with the starting concept. Further, the one or more target concepts are sorted according to the calculated association degrees. In this method, explainable target concepts can be obtained by using semantic analysis, and these target concepts are sorted to increase a possibility of discovering useful medical knowledge.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for recognizing a medical entity in a medical text. In this method, the medical text is segmented into a plurality of words. Each of the words is used as a target word. A local and a global annotation feature of the target word are determined. The local annotation feature includes the target word. The global annotation feature includes a relationship between the target word and the recognized medical entity. Next, a label for the target word is determined from candidate labels based on the local and global annotation feature. Next, a combination relationship between the target word and a previous word of the target word is obtained based on the label for the target word. The combination relationship includes combination and non-combination. Next, the combined words are recognized as the medical entity based on the combination relationship.

Abstract: There are disclosed a recipe recommendation method and device, a computing device and a storage medium. The method includes: obtaining characteristic information of a user the characteristic information including static characteristic information and dynamic characteristic information of the user; determining a user requirement based on the characteristic information of the user; determining a recommended recipe based on the user requirement.

Abstract: A lung-sound signal processing method, device and computer-readable storage medium. The lung-sound processing method includes: obtaining a lung sound signal; decomposing the lung sound signal, to obtain a crackle-sound component and a respiration-sound component; obtaining an inspiratory-phase period based on the respiration-sound component; extracting a crackle-sound signal located in the inspiratory-phase period from the crackle-sound component; and obtaining at least one feature parameter according to the crackle-sound signal.

Abstract: The present application provides a method of calculating relevancy between a first text and a second text. The method includes respectively mapping the first text and the second text to vectors; determining a similar part and a dissimilar part between the first text and the second text based on the vectors; and calculating the relevancy between the first text and the second text using both the similar part and the dissimilar part.

Abstract: Disclosed are a synonym mining method, an application method of a synonym dictionary, a medical synonym mining method, an application method of a medical synonym dictionary, a synonym mining device and a storage medium. The synonym mining method includes: performing a recognition process on corpus data to obtain a named entity set of at least one category, wherein the named entity set of each category includes a plurality of named entities; performing a clustering process on the plurality of named entities in the named entity set of the each category to obtain a synonym candidate set corresponding to the each category; and performing, based on a word from similarity and a context similarity, a filtering process on the synonym candidate set corresponding to the each category to obtain a synonym set corresponding to the each category.

Abstract: Provided are a semantic classification method and apparatus, a neural network training method and apparatus and a storage medium.

Abstract: A method and device of similarity determination, network training, and search, an electronic device, and a storage medium are provided. The data similarity determination method includes: acquiring first data of a first object; mapping the first sub-data as a first semantic representation in a semantic comparison space, where the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of the first modality to the semantic comparison space and a semantic representation obtained by mapping data of the second modality to the semantic comparison space to be computed; acquiring second data of a second object; mapping the second sub-data as a second semantic representation in the semantic comparison space; and calculating a similarity between the first data and the second data based on at least the first semantic representation and the second semantic representation.

Abstract: The disclosure provides a method and a system for automatically discovering an inference rule, a medical knowledge database and a retrieval method. The method includes: acquiring a relation matrix for each medical relation, the relation matrix representing a relationship between vectors of the medical entities having the medical relation; and acquiring the inference rules based on the relation matrices for the plurality of medical relations, each inference rule including a first medical relation, a second medical relation and a third medical relation, the inference rule representing that the third medical relation is inferred from the first medical relation and the second medical relation.

Abstract: The present disclosure provides a medical data analysis method and device. The method comprises locating a semantic subspace of the subject in a medical data set by taking the physical parameter as a feature; and analyzing the probability P1 of the subject being in the semantic subspace that the subject belongs to by judging the semantic consistency of the semantic subspace where the physical parameter of the subject exists. In addition, it is also possible to analyze the probability P2 of the subject being in the node that the subject belongs to based on the evidence transference score of the physical parameter of the subject on the medical knowledge graph. P=?×P1+(1??)×P2 The probability P of the subject being in the semantic subspace or node that the subject belongs to can be determined by P=?×P1+(1??)×P2, wherein ? is a reconciling parameter, 0<?<1. Through these solutions, the analysis accuracy and efficiency can be improved and the cost can be decreased.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for recognizing a medical entity in a medical text. In this method, the medical text is segmented into a plurality of words. Each of the words is used as a target word. A local and a global annotation feature of the target word are determined. The local annotation feature includes the target word. The global annotation feature includes a relationship between the target word and the recognized medical entity. Next, a label for the target word is determined from candidate labels based on the local and global annotation feature. Next, a combination relationship between the target word and a previous word of the target word is obtained based on the label for the target word. The combination relationship includes combination and non-combination. Next, the combined words are recognized as the medical entity based on the combination relationship.

Abstract: The present application provides a method of calculating relevancy between a first text and a second text. The method includes respectively mapping the first text and the second text to vectors; determining a similar part and a dissimilar part between the first text and the second text based on the vectors; and calculating the relevancy between the first text and the second text using both the similar part and the dissimilar part.

Abstract: An intelligent triage method and device as well as a computer readable storage medium are disclosed. The intelligent triage method includes: extracting disease-related symptoms and signs from patient information as candidate factor information; obtaining a plurality of symptom-related candidate diseases and treatment measures from medical literature as identification knowledge information based on the candidate factor information; matching the identification knowledge information with the candidate factor information; repeating the above steps until an affiliated department of a disease is determined or the patient information has been extracted and matched, and returning the affiliated department of the disease as a triage result.

Abstract: A machine-aided dialog system as well as a medical condition inquiry method and apparatus employing the machine aided dialog system are disclosed. There is provided a machine-aided dialog system, comprising: a semantic framework for providing a dialog theme; a knowledge database for storing semantic knowledge, the semantic knowledge comprising at least one group of content-related language expressions manners; and a dialog manager for managing a dialog according to the dialog theme provided by the semantic framework, wherein the managing a the dialog comprises: generating a question expression of the dialog theme based on the semantic knowledge acquired from the knowledge database; and ending the dialog theme once upon determining that the dialog theme is completed in response to a dialog ending condition.

Abstract: A method for processing a drug-disease relevance relation includes receiving drug information; analyzing the drug information and different disease information to obtain a disease in different diseases in the different disease information that has a relevance relation with a drug in the drug information, the relevance relation including a therapeutic relation between the drug and the disease and/or a side-effect relation between the drug and the disease; and outputting the disease having the relevance relation with the drug.

Abstract: The present disclosure provides a computer device, a system, a readable storage medium, and a medical data analysis method. The method includes: acquiring symptom information of a patient and examination result information of a patient; accessing a predetermined knowledge graph; determining an initial weight of each of the plurality of first nodes and the plurality of second nodes of the predetermined knowledge graph according to the above-mentioned information; transmitting evidence through the plurality of edges on the predetermined knowledge graph based on the initial weight of each of the plurality of first nodes and the plurality of second nodes, to determine a final weight of each node on the knowledge graph; and determining prediction information for the symptom information of the patient and the examination result information of the patient based on the final weight of each node.

Abstract: A terminal for analyzing a drug-disease relevance relation includes a transceiver and a processor. The transceiver is configured to receive drug information, disease information and drug-disease relevance relation information. The processor is configured to construct a pharmacodynamic relation model based on the drug information, the disease information and the drug-disease relevance relation information.

Abstract: The present disclosure relates to a question generating method and apparatus, an inquiring diagnosis system, and a computer readable storage medium. The question generating apparatus includes at least one processor, the at least one processor being configured to: acquire a candidate question set Q; calculate an information value of each candidate question in the candidate question set Q; and generate at least one question according to the information value of each candidate question.

Abstract: A triage dialogue method, a device and a system are provided. The triage dialogue method includes: receiving triage dialogue content; dividing the triage dialogue content into a series of dialogue features, and generating dialogue feature vectors in one-to-one correspondence with the dialogue features; inputting the dialogue feature vectors into an encoder, and generating an intermediate representation vector through the encoder; determining whether the triage task is successfully completed according to the intermediate representation vector; and adjusting the triage dialogue policy according to the determination result of the triage task.