Method and system for predicating re-medical treatment information based on cross-attention neural network
A neural network and prediction method technology, which is applied in the field of medical revisit information prediction based on cross-attention neural network, can solve the problems of interfering with patient judgment and affecting prediction effect, so as to improve prediction accuracy, ensure completeness and independence, and achieve good results. The effect of explainability
Active Publication Date: 2019-10-08
浩睿智源山东人工智能有限公司
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Mixed diagnosis and treatment information will interfere with the judgment of the patient's current disease and affect the prediction effect
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[0037] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
[0038] It should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.
[0039] Separating diagnosis and treatment can better grasp the patient's condition changes and analyze the treatment process. ...
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The present disclosure provides a method and a system for predicating re-medical information based on a cross-attention neural network. The method comprises the following steps: acquiring historical electronic health record data of a patient; splitting the first medical treatment information record in a multivariate time-mark sequence of the patient into diagnosis information and treatment information, and respectively carrying out reduced dimensional representation on the diagnosis information and the treatment information; processing the dimension-reduced diagnosis information by using a bidirectional neural network to obtain a corresponding diagnosis information hiding state, and processing the dimension-reduced treatment information by using the bidirectional neutral network to obtaina corresponding treatment information hiding state; integrating the historical diagnosis information and the historical treatment information by a cross-attention mechanism to form a context vector capable of representing the current state of the patient; merging the patient's disease diagnosis information and treatment information of the context vector in a connecting manner after the disease diagnosis information and treatment information are obtained in order to form a representation vector that represents the overall information of the patient; and placing the presentation vector in a final output layer to predict the medical treatment information.
Description
technical field [0001] The present disclosure relates to the technical field of information processing, in particular to a method and system for predicting re-seeking information based on a cross-attention neural network. Background technique [0002] The purpose of analyzing patients' health information is to help people prevent diseases as early as possible and guide treatment methods. Therefore, it is an important task to predict the next medical coding (including disease and treatment) through the patient's historical Electronic Health Record (EHR) data. How to model the temporality and high dimensionality of continuous EHR data and interpret the prediction results is a key issue in accomplishing this task. [0003] The inventors found in the research that existing methods solve these problems by using a Recurrent Neural Network (RNN) to model EHR data and using an attention mechanism to provide interpretability. Previous models mixed treatment and diagnosis informatio...
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Login to View More IPC IPC(8): G16H50/20G16H10/60G16H50/70G06Q10/04G06K9/62G06N3/04G06N3/08
CPCG16H50/20G16H10/60G16H50/70G06Q10/04G06N3/049G06N3/084G06N3/045G06F18/213G06F18/2414
Inventor 郭伟葛伟任艺琴刘静崔立真
Owner 浩睿智源山东人工智能有限公司