9 results about "Biomedical information" patented technology

The application discloses a thyroid cancer gene detection report intelligent risk assessment method, and relates to the technical field of biomedical information processing, and comprises the following steps: risk conclusion extraction is carried out on obtained multiple thyroid cancer gene detection reports, and the risk conclusion in each detection report is arranged in order of detection time, the time interval between adjacent detection reports is marked, and continuous risk change records are formed. Through time sequence processing and trend reversal identification, the application realizes dynamic analysis of risk assessment, can capture reversal signals in the early stage of risk increase, and improves the timeliness and reliability of risk identification. By setting a reference point and a risk control entrance, combining time interval insertion and detection frequency improvement, the application realizes adaptive correction and continuous updating of risk results, and enhances the real-time performance and safety of risk assessment.

This application relates to the field of natural language processing technology, and in particular to a biomedical information extraction method based on a large language model. The method includes: acquiring the biomedical dataset to be processed and performing standardized preprocessing; converting the relation extraction data into high-dimensional vectors and constructing a local vector library; acquiring the medical information text to be processed as the query text, and performing a two-stage example retrieval and filtering in the local vector library to obtain a high-quality example set; generating context examples and performing context learning to understand the current task requirements and generate the information extraction results of the query text; and parsing the information extraction results. Based on the reordering capability of the cross-encoder model, this application designs a two-stage retrieval and reordering mechanism. By ensuring that the ICL examples provided to the large language model have both high semantic relevance and high task guidance, it significantly improves the accuracy and robustness of the model in biomedical named entity recognition and relation extraction tasks.

The present application belongs to the cross technical field of biomedical information processing and artificial intelligence, and specifically relates to a medical prediction model literature quality automatic evaluation method based on TRIPOD+AI standard, obtaining a medical literature file to be evaluated, performing multi-modal analysis and layout analysis on the file, segmenting the document into multiple semantic blocks, and extracting structured data and key text blocks therefrom; constructing a scoring evaluation matrix based on the TRIPOD+AI guideline, the matrix containing statistical hard indicators and descriptive soft indicators; and scoring the structured data and key text blocks in a parallel dual-track mode using a rule calculation engine and a large language model semantic reasoning engine. The present application significantly improves the scoring efficiency, significantly improves the fine granularity and discrimination of the evaluation, and solves the trust problem of "black box" evaluation.

This application relates to the field of medical information extraction technology, and particularly to a biomedical information extraction method based on two-stage fine-tuning and preference optimization. The method includes: optimizing a base model using a two-stage fine-tuning and preference optimization strategy, and then using the optimized model to extract biomedical information. This application designs a progressive preference learning framework, employing an improved DPO-Positive algorithm and post-fine-tuning to enhance model accuracy for medical information extraction tasks; it also designs a multi-dimensional preference dataset and its automated generation method to reduce the burden of manual annotation and improve model fault tolerance and generalization ability; and combines efficient parameter fine-tuning and dual-model correction verification to achieve high-performance, standardized output with limited computing power, improving knowledge extraction efficiency and reliability.

This invention relates to the fields of biomedical informatics and machine learning, specifically to a method for generating multimodal longitudinal data for Alzheimer's disease. This method constructs a brain network graph based on structural connectivity, using brain regions as nodes and connection strengths as edges to form a graph topology for modeling pathological spread. A continuous-time dynamic system parameterized by Neural ODE is defined on this graph, dynamically decomposing the disease course into three interpretable modules: generation and degradation, biomarker interactions, and brain network spread. The model initializes the pathological state from a conditional generator, obtains continuous trajectories through ODE numerical solutions, and jointly evaluates the realism and graph physical consistency of the generated data using a discriminator. This method can generate multimodal longitudinal data that conforms to the temporal and spatial spread patterns of AD, and is suitable for disease simulation, data augmentation, and clinical research.

The invention provides an epileptic seizure identification method and device based on multi-modal data, and can be applied to the technical field of artificial intelligence and biomedical information processing. The method comprises the following steps: in response to acquired multi-modal data of a target object, performing segmentation processing on electroencephalogram signal data in the multi-modal data according to a preset granularity to obtain a plurality of electroencephalogram signal segments; for each electroencephalogram signal segment, extracting a time sequence dependence feature of the electroencephalogram signal segment; processing an identification prompt vector and the plurality of time sequence dependence features to obtain a global signal feature; determining a multi-modal feature of the target object based on the global signal feature and a text feature of the text data; and carrying out classification based on the multi-modal features, and outputting an epileptic seizure recognition result about the target object for assisting a user in judgment.

We provide the system. [Solution] Information gathering means for collecting and integrating biomedical information, An information analysis means for extracting biomedical patterns by applying a machine learning algorithm to the integrated information, Access management means to enhance the authentication process based on the extracted pattern, A repositioning method that explores applicability from multiple information resources, A decision-making support tool that proposes the optimal treatment method based on individual subject information, A system that includes this.

The application discloses a health life prediction method, a prediction model construction method and a prediction system, relates to the technical field of computer technology and biomedical information, and the prediction model construction method comprises the following steps: acquiring training data and an intermediate effect value; a baseline risk model is trained based on the training data and a plurality of long-life causal proteins as input characteristics; the modified protein risk contribution of each sample is calculated by using a preset correction rule; the modified protein risk contribution and a disease risk score of the sample obtained by evaluating the sample based on a plurality of preset disease risk models are used as new input characteristics, and the baseline risk model is retrained to obtain a final health life comprehensive risk prediction model. The protein expression data is modified by the intermediate effect value, so that the indirect influencing factors are eliminated, and the comprehensive prediction model is constructed by fusing the disease risk score, and the accurate prediction of the individual health life is realized.