Boe Technology Group Co., Ltd.

Beijing Boe Technology Development Co., Ltd.

Proplus Design Solutions, Inc.

Boe Technology Groupco., Ltd.

Zhenzhong Zhang

Hui Li

Bruce W McGaughy

Xue Chen

Jun Fang

Chenyin Shen

Tuo Sun

Shuo Zhang

Yanzhao Li

Chao Zhou

Wei Deng

Pengfei Zhang

Cuifang Zhang

Hongxiang Shen

Ge Ou

Yulan Hu

Sifan Wang

Chao Ji

Chuqian Zhong

Longfei Li

Boran Jiang

Xinjun Niu

Shuqi Wei

The present disclosure relates to a method for training a natural language processing model, including: obtaining a sample text of natural language; determining one or more triples in the sample text, wherein each of the triples comprises two entities in the sample text and a relation between the two entities; processing the sample text based on the triples to obtain one or more knowledge fusion vectors; and training a natural language processing model by inputting the knowledge fusion vectors into the natural language processing model to obtain a target model.

Method, apparatus and storage medium for training natural language processing model

A linewidth measurement method includes: obtaining a target image of the line, the line including a first line segment; performing region connecting on an edge image or binary image of the target image to obtain a region connected image, the region connected image including a target connected region corresponding to a pattern of the line, and a pixel value of each pixel in the target connected region being different from that of each pixel outside the target connected region; determining a first edge point and a second edge point of the first line segment based on the region connected image; and determining a width of the first line segment according to the first edge point and the second edge point, and determining the width of the line according to the width of the first line segment.

Linewidth measurement method and apparatus, computing and processing device, computer program and computer readable medium

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.

Method for processing drug-disease relevance relation, electronic device and non-transitory computer-readable storage medium

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.

Medical knowledge graphs for use in an intelligent diagnostic assistant

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.

Similarity determining method and device, network training method and device, search method and device, and electronic device and storage medium

There are provided a method and an apparatus for distributing physical examination information, an electronic device, a computer-readable storage medium, and a computer program product. The method includes: obtaining physical examination information and information of a plurality of distribution objects; inputting the physical examination information and the information of the plurality of distribution objects into an information matching model obtained by pre-training to obtain a matching degree between the physical examination information and the plurality of distribution objects; and determining a target distribution object from the plurality of distribution objects according to the matching degree between the physical examination information and each of the plurality of distribution objects, and distributing the physical examination information to the target distribution object.

Physical examination information distribution to distribution objects with workload information and matching model

An object information processing method includes: acquiring an image of an object to be detected; inputting the image into a joint model to obtain a plurality of labels of the object to be detected; and obtaining object information of the object to be detected based on the plurality of labels. The joint model includes a convolutional neural network, the convolutional neural network includes a plurality of sub-networks. The plurality of sub-networks include an underlying processing network output of the underlying processing network and perform operation processing on the output of the underlying processing network to output a label, and labels output by different output networks are different.

Method for training joint model, object information processing method, apparatus, and system

A question-answer processing method, apparatus, electronic device, and computer-readable storage medium are provided. A question-answer processing method includes the following steps: acquiring an input question and a candidate question-answer pair, where the candidate question-answer pair comprises a candidate question and a candidate answer matching the candidate question; inputting the input question into a first model to obtain an input question vector; calculating a similarity between the input question vector and a candidate question vector, and calculating a relevance between the input question vector and a candidate answer vector; and determining a matching degree of the candidate answer and the input question according to the similarity and the relevance.

Question-answer processing method, apparatus, electronic device and computer-readable storage medium

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.

Synonym mining method, application method of synonym dictionary, medical synonym mining method, application method of medical synonym dictionary, synonym mining device and storage medium

Provided are a semantic classification method and apparatus, a neural network training method and apparatus and a storage medium. The semantic classification method includes: inputting a first remark relating to a first object; extracting a first common representation vector for representing a common representation in the first remark by processing the first remark using a common representation extractor; extracting a first single representation vector for representing a single representation in the first remark by processing the first remark using a first representation extractor; obtaining a first representation vector by splicing the first common representation vector and the first single representation vector; and obtaining a semantic classification of the first remark by processing the first representation vector using a first semantic classifier; where the common representation includes an intention representation which is used to remark on both the first object and a second object, the second object is an associated remarked object different from the first object, and the single representation in the first remark includes an intention representation which is only used to remark on the first object.

Neural network training method and apparatus, semantic classification method and apparatus and medium

An image recognition method and apparatus, a training method, an electronic device, and a storage medium are provided. The image recognition method includes: acquiring an image to be recognized, the image to be recognized including a target text; and determining text content of the target text based on knowledge information and image information of the image to be recognized.

Image recognition method and apparatus, training method, electronic device, and storage medium

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.

Question generating method and apparatus, inquiring diagnosis system, and computer readable storage medium

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.

Method and apparatus for recognizing medical entity in medical text

The present disclosure relates to a data processing method, a medical term processing system, a medical diagnostic system, a medical intelligent triage apparatus and a computer readable storage medium. The method comprises: acquiring statement information inputted by a user; dividing the statement information into a plurality of information segments by means of a predetermined algorithm, the plurality of information segments comprising a first information segment; establishing a Gaussian distribution of each of the information segments; calculating the similarity between the first information segment and words in a database by means of a similarity model; and obtaining, from words in the database, at least one second information segment for describing the first information segment based on the calculated similarity. The present disclosure can provide professional terms, such as medical terms, corresponding to colloquial expressions, to facilitate users' diagnosis and treatment.

Data processing method, medical term processing system and medical diagnostic system

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.

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Beijing, China

Zhenzhong Zhang