7 results about "Meta-analysis" patented technology

The invention discloses a Meta analysis method and device, equipment and a storage medium, according to the scheme, automatic semantic analysis is carried out on a research problem input by a user through a preset large language model, target literature is processed through a pre-training language model, an efficient and reliable automatic tool is provided for evidence-based medicine, and the efficiency is improved. According to the method, the dependency of Meta statistical analysis on professionals is reduced, the analysis period is shortened, personal errors are reduced, the intelligent degree, efficiency, accuracy and repeatability of Meta statistical analysis are remarkably improved, and standardized and intelligent development of evidence-based medical research is promoted; in addition, retrieval is carried out based on the target structured form, the retrieval strategy is optimized, the retrieval accuracy is improved, and therefore the performance and accuracy of Meta statistical analysis are further improved.

The invention is suitable for the technical field of intelligent medicine, and provides a meta analysis intelligent topic selection system based on a language large model and literature, which comprises a multi-source literature library, a literature dynamic acquisition module, a literature panoramic analysis module, an intelligent topic selection generation module, a feasibility evaluation module and a scheme automatic generation module, the literature dynamic acquisition module comprises a multi-source data access unit, an incremental updating monitoring unit and an intelligent filtering and classifying unit; the intelligent topic selection generation module is used for evaluating the novelty and potential influence of topic selection based on the blank of existing research identification knowledge; the system further comprises a processing module, an enhancement module and an interaction module. The system provides intelligent topic selection suggestions for Meta analysis and research for researchers by combining a large language model and a literature mining technology, so as to solve the problems of low efficiency, incomplete coverage and lack of data support in the traditional Meta analysis topic selection process, shorten topic selection time, avoid omission risk of manual retrieval, and reflect the latest progress of the field in real time.

The invention discloses a Meta analysis method, system and device based on artificial intelligence and a storage medium, and the method comprises the steps: receiving a natural language research problem inputted by a user, analyzing the natural language research problem, and generating a structured research framework according to an analysis result; constructing a retrieval formula based on the structured research framework, and retrieving literatures from the plurality of literature databases by using the retrieval formula to obtain a candidate literature set; and performing information extraction on the candidate literature set to obtain quantitative research data, and performing Meta analysis based on the quantitative research data to obtain a Meta analysis result. According to the method, the natural language research problem is analyzed, the structured research framework is generated according to the analysis result, the retrieval formula is constructed based on the structured research framework to retrieve literatures from the multiple literature databases, information extraction is performed on the candidate literature set, Meta analysis is performed based on the extracted quantitative research data, and the efficiency and accuracy of Meta analysis are improved.

The invention relates to the field of environmental antibiotic resistance gene (ARG) risk assessment, and aims to solve the problems that in existing research, microplastics are inconsistent in ARG abundance conclusion, and evaluation is one-sided only depending on relative abundance (abundance normalized with 16S rRNA gene and the like as a reference) or absolute abundance (abundance based on copy number). The invention provides a double-index collaborative risk evaluation method introducing Meta analysis. The method comprises the steps of data acquisition, index processing, Meta analysis and multi-factor adjustment, double-index interpretation and risk assessment and the like. By integrating ARG relative abundance and absolute abundance data of a micro-plastic exposure group and a control group and combining factors such as environment matrix, exposure time, particle size, concentration and type, the influence trend of micro-plastic on ARG abundance under different conditions is evaluated, the risk type is judged from a double-index perspective, and the accuracy and applicability of resistance risk evaluation are improved.

The invention belongs to the technical field of drug safety pharmacology, and particularly relates to an hERG margin automatic data calculation method and system. The method comprises the following steps: step 1, calculating the statistical magnitude of hERG IC50 of a drug to be analyzed, wherein the statistical magnitude comprises a sample size, an arithmetic mean value, a lower limit and an upper limit of a 95% confidence interval, and a standard deviation; step 2, taking the statistics obtained in the step 1 as basic data, generating probability distribution of key parameters through Monte Carlo simulation, and calculating distribution of safety windows; and step 3, integrating data of all drugs to be analyzed, and obtaining overall safety window estimation through meta-analysis. The invention further provides a system for implementing the method. The method has the advantages of high efficiency, accuracy and accurate personalized calculation for special drugs, provides more scientific and reliable technical support for hERG safety window calculation and risk assessment, and has a good application prospect.

The invention discloses an environmental micro-plastic ecological risk evaluation method based on data driving and normalization, and belongs to the technical field of micro-plastic environmental risk evaluation, and the method comprises the steps: collecting and integrating micro-plastic exposure experiment data, carrying out response ratio normalization processing and Meta analysis, carrying out p value inspection and effect quantity threshold inspection, and identifying significant response species. Screening sensitive species and sensitive effect indexes, traversing a machine learning classification algorithm, taking parameters of a micro-plastic exposure experiment as input features and taking whether the effect is significant as output, forming a supervised learning data set, training a machine learning classifier, carrying out deep analysis by utilizing a model interpretable method, and quantifying and normalizing a required effect threshold value. The method comprises the following steps: establishing a toxicity equivalent model, performing equivalent concentration conversion of microplastic combinations with different characteristics according to different effect threshold ratios, comparing the equivalent concentration with an effect threshold of standard microplastic, determining ecological risks, evaluating microplastic environmental risks, and providing a scientific basis for pollution management and control.

This invention discloses a method and system for analyzing and computable input of outcome indicators in meta-analysis RCTs, relating to the fields of medical statistics and literature data processing. The method first organizes a set of statistical elements with outcome indicators, detection time points, and comparison relationships as their scopes; then, it abstracts the meta-analysis formula into statistical element slot templates and performs slot matching within the outcome scope; when a target slot is missing, it is filled in based on preset substitution rules; after computability verification, a standardized meta-analysis input structure is generated. This invention solves the problems of existing technologies where meta-analysis data preparation is highly dependent on manual processes, computability verification is difficult to automate, and missing elements cannot be automatically filled in. It transforms manual judgment and calculation into a reusable structured process, significantly improving the efficiency, consistency, and data utilization of large-scale RCT outcome data processing. The output standardized data can be directly used by existing meta-analysis software.