Method and system for tumor individualized clinical trial text mining

Abstract

The application discloses a method and system for tumor individualized clinical trial text mining. The method for the tumor individualized clinical trial text mining in the application includes the steps of (1) querying a preset clinical trial database according to to-be-tested object individualized information data, and screening clinical trial texts matching with the individualized information data; screening and obtaining the priority: disease type>gender>age>country>biomarker mutation information; (2) sorting the trial texts obtained from the screening in the step (1) into: gene mutation site complete matching>other gene mutation matching>gene matching>related gene matching; and (3) outputting the sorted trial texts, that is, individualized clinical trial texts. The method of the application can not only provide more and more accurate individualized treatment plans for the majority of patients and doctors; but also obtain more comprehensive, accurate and effective reference data, improve the accuracy and efficiency of individualized treatment plans, and save time and money.

Description

technical field [0001] The present application relates to the field of tumor detection, in particular to a method and system for text mining of tumor individualized clinical trials. Background technique [0002] Since the "Precise Medicine" plan was proposed in 2015, the prelude to precision medicine has been kicked off. The precision medicine plan mainly increases medical research funding, promotes individualized genomics research, and develops individual medical plans for patients with cancer and other diseases based on personal genetic information. Since the use of an individual's genetic information can effectively find the cause of a patient's disease, it could save tens of billions of dollars currently spent on ineffective drugs. [0003] With the completion of the human genome and the development of genetic testing technology, individualized tumor treatment testing based on sequencing platform data has been applied to various platforms around the world. With the red...

AI Technical Summary

Problems solved by technology

Text mining around the development of protein functional interactions; from identifying protein entities, forming entity lists, counting occurrence frequencies, forming candidate entities, and finally entity association to complete the relationship search between proteins; protein interaction relationships are mainly used in scientific research text mining. However, there are still some deficiencies in the interpretation of tumor gene detection, which cannot solve the problem of intervention treatment well

Text mining around biomedical literature is very important in the evidence collection of evidence-based medicine, but the relevant biological literature information is based on the existing clear experimental conclusions and results, and covers less treatment options in frontier development; especially Yes, biomedical literature text mining lacks sources of information on the progress of new drugs or biologics in development

[0007] In addition, most of the text mining tools developed by some computer companies rely on data and algorithms, and are not well integrated with biomedical knowledge and background. Most of the mined data are redundant and have low accuracy, so it is of little significance to provide solutions for the actual treatment of patients. , there is still a long way to go before it can be combined with practical applications

At present, there are still relatively few studies on clinical experiment text mining, and it is impossible to carry out clinical experiment text mining on the comprehensive text of large-scale gene sequencing combined with other detection data and patient information, which cannot meet the current needs of individualized and precise treatment of tumors

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