Privacy protection of sexually transmitted infections information from Chinese electronic medical records.

The comprehensive adoption of Electronic Medical Records (EMRs) offers numerous benefits but also introduces risks of privacy leakage, particularly for patients with Sexually Transmitted Infections (STI) who need protection from social secondary harm. Despite advancements in privacy protection research, the effectiveness of these strategies in real-world data remains debatable. The objective is to develop effective information extraction and privacy protection strategies to safeguard STI patients in the Chinese healthcare environment and prevent unnecessary privacy leakage during the data-sharing process of EMRs.

Double-target self-supervised clustering with multi-feature fusion for medical question texts.

Background: To make the question text represent more information and construct an end-to-end text clustering model, we propose a double-target self-supervised clustering with multi-feature fusion (MF-DSC) for texts which describe questions related to the medical field. Since medical question-and-answer data are unstructured texts and characterized by short characters and irregular language use, the features extracted by a single model cannot fully characterize the text content.

Methods: Firstly, word weights were obtained based on term frequency, and word vectors were generated according to lexical semantic information.

Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.

Background: Schizophrenia is a serious mental disease. With increased research funding for this disease, schizophrenia has become one of the key areas of focus in the medical field. Searching for associations between diseases and genes is an effective approach to study complex diseases, which may enhance research on schizophrenia pathology and lead to the identification of new treatment targets.

Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system.

Background: When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answering systems for chronic diseases, such as rheumatoid arthritis, and the related technology to construct a question-answering system about chronic diseases is not sufficiently mature.

Methods: Our research embedded the classification information of the question into the sentence vector based on the bidirectional encoder representations from transformers (BERT) language model.

Surgical procedure long terms recognition from Chinese literature incorporating structural feature.

With rapid development of technologies in medical diagnosis and treatment, the novel and complicated concepts and usages of clinical terms especially of surgical procedures have become common in daily routine. Expected to be performed in an operating room and accompanied by an incision based on expert discretion, surgical procedures imply clinical understanding of diagnosis, examination, testing, equipment, drugs and symptoms, etc., but terms expressing surgical procedures are difficult to recognize since the terms are highly distinctive due to long morphological length and complex linguistics phenomena.

Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study.

Background: With the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies.

Objective: The purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored.