CPRS: a clinical protocol recommendation system based on LLMs.

Background: As fundamental documents in clinical trials, clinical trial protocols are intended to ensure that trials are conducted according to the objectives set by researchers. The advent of large models with superior semantic performance compared to traditional models provides fresh perspectives for research recommendations in clinical trial protocols.

Method: A clinical trial protocol recommendation system based on Large Language Models (LLMs) is proposed in this paper, combining GPT-4 and knowledge graph to assist in clinical trial protocol recommendations.

Structural analysis and intelligent classification of clinical trial eligibility criteria based on deep learning and medical text mining.

Objective: To enhance the efficiency, quality, and innovation capability of clinical trials, this paper introduces a novel model called CTEC-AC (Clinical Trial Eligibility Criteria Automatic Classification), aimed at structuring clinical trial eligibility criteria into computationally explainable classifications.

Methods: We obtained detailed information on the latest 2,500 clinical trials from ClinicalTrials.gov, generating over 20,000 eligibility criteria data entries.

Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm.

Objective: To address the challenges arising from the rapid growth of text data in the biomedical field, including the problems of irrelevant argument interference and deep semantic association neglect in existing event argument detection methods, as well as the difficulty of multiple event extraction. We aim to propose a new event argument detection method that can accurately mine valuable information from biomedical texts through multi-feature fusion and the question-and-answer paradigm, while also addressing the limitations of existing methods.

Methods: We propose an event argument detection method based on multi-feature fusion and the question-answer paradigm.

[Overview of the application of knowledge graphs in the medical field].

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction.

A review of research on eligibility criteria for clinical trials.

The purpose of this paper is to systematically sort out and analyze the cutting-edge research on the eligibility criteria of clinical trials. Eligibility criteria are important prerequisites for the success of clinical trials. It directly affects the final results of the clinical trials.

Characteristics of COVID-19 Clinical Trials in China Based on the Registration Data on ChiCTR and ClinicalTrials.gov.

Objective: This study aimed to evaluate the fundamental characteristics of coronavirus disease (COVID-19) clinical trials registered in China.

Methods: COVID-19 clinical trials registered in China were analyzed from databases on ChiCTR and ClinicalTrials.gov.