Abstract: A method for RNA-protein interaction prediction, including: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.

Abstract: Provided are an electrokinetic device and method for in-situ leaching of uranium, belonging to the technical field of in-situ leaching of uranium. The electrokinetic device for in-situ leaching of uranium includes an injection well, a pumping well, a positive electrode, a negative electrode, leaching solution, and a direct current power supply. Uranium ore is provided between the injection well and the pumping well, the negative electrode is arranged in the injection well, and the positive electrode is arranged in the pumping well. The leaching solution is injected from the injection well, flows through the uranium ore, and then is pumped from the pumping well for uranium extraction. The direct current power supply is respectively connected to the positive electrode and the negative electrode, and is configured to apply direct current between the positive electrode and the negative electrode to promote the pooling of uranium-carrying ions towards the pumping well.

Abstract: 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.

Abstract: The present disclosure relates to a method, an apparatus and a system for determining a property of a medicine, and a device and a storage medium. The method includes: inputting (S101) a medical knowledge graph into a pre-trained representation network, such that the representation network outputs a representation vector of at least one node of the medical knowledge graph; and inputting (S102) a representation vector of a medicine node in the at least one node into a pre-trained determination network, such that the determination network outputs a property of a medicine corresponding to the medicine node.

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

Abstract: A method and a device for disease prediction, an electronic device and a computer-readable storage medium are provided. A method for predicting disease comprising the steps of: respectively acquiring a first feature and a second feature of a target object; inputting the first feature and the second feature into a risk prediction model, wherein the risk prediction model comprises a linear sub-model and a non-linear sub-model obtained by joint training; processing the first feature through the linear sub-model to obtain a first risk score; processing the second feature through the non-linear sub-model to obtain a second risk score; calculating a disease risk of the target subject based on the first risk score and the second risk score.

Abstract: 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.

Abstract: Provided is a method and device for determining a queuing scheme. The method includes: obtaining candidate queuing schemes by using each to-be-executed item as a first-order item, using the to-be-executed item other than the first-order item as other items; for each candidate queuing scheme, obtaining a completion time for the first-order item according to a number of current queueing people and a unit execution time for the first-order item; determining a number of increased queuing people for each of the other items according to the completion time for the first-order item; obtaining the completion time for all the other items according to the number of the current queuing people, the number of the increased queuing people and the unit execution time for each of the other items; calculating a total time for completion of all the to-be-executed items in each candidate queuing scheme, and determining a target queuing scheme.

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: 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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: 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.