Research on privacy protection in the context of healthcare data based on knowledge map.

With the rapid development of emerging information technologies such as artificial intelligence, cloud computing, and the Internet of Things, the world has entered the era of big data. In the face of growing medical big data, research on the privacy protection of personal information has attracted more and more attention, but few studies have analyzed and forecasted the research hotspots and future development trends on the privacy protection. Presently, to systematically and comprehensively summarize the relevant privacy protection literature in the context of big healthcare data, a bibliometric analysis was conducted to clarify the spatial and temporal distribution and research hotspots of privacy protection using the information visualization software CiteSpace. The literature papers related to privacy protection in the Web of Science were collected from 2012 to 2023. Through analysis of the time, author and countries distribution of relevant publications, we found that after 2013, research on the privacy protection has received increasing attention and the core institution of privacy protection research is the university, but the countries show weak cooperation. Additionally, keywords like privacy, big data, internet, challenge, care, and information have high centralities and frequency, indicating the research hotspots and research trends in the field of the privacy protection. All the findings will provide a comprehensive privacy protection research knowledge structure for scholars in the field of privacy protection research under the background of health big data, which can help them quickly grasp the research hotspots and choose future research projects.

Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks.

BACKGROUND: X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs. PURPOSE: To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi-criteria AI system for automatically assessing the image quality of knee radiographs. METHODS: A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single-person QC results were denoted as QC1 and QC2, and the multi-person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double-blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard. RESULTS: For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively. CONCLUSIONS: The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications.

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation.

Introduction: Large Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases. Methods: We fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI's Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named "GastroBot," which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information. Results: When evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results. Discussion: Research findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.

Comprehensive Exploration of the Neuroprotective Mechanisms of Ginkgo biloba Leaves in Treating Neurological Disorders.

Neurological disorders (NDs) are diseases that seriously affect the health of individuals worldwide, potentially leading to a significant reduction in the quality of life for patients and their families. Herbal medicines have been widely used in the treatment of NDs due to their multi-target and multi-pathway features. Ginkgo biloba leaves (GBLs), one of the most popular herbal medicines in the world, have been demonstrated to present therapeutic effects on NDs. However, the pharmacological mechanisms of GBLs in the treatment of neurological disorders have not been systematically summarized. This study aimed to summarize the molecular mechanism of GBLs in treating NDs from the cell models, animal models, and clinical trials of studies. Four databases, i.e., PubMed, Google Scholar, CNKI, and Web of Science were searched using the following keywords: "Ginkgo biloba", "Ginkgo biloba extract", "Ginkgo biloba leaves", "Ginkgo biloba leaves extract", "Neurological disorders", "Neurological diseases", and "Neurodegenerative diseases". All items meeting the inclusion criteria on the treatment of NDs with GBLs were extracted and summarized. Additionally, PRISMA 2020 was performed to independently evaluate the screening methods. Out of 1385 records in the database, 52 were screened in relation to the function of GBLs in the treatment of NDs; of these 52 records, 39 were preclinical trials and 13 were clinical studies. Analysis of pharmacological studies revealed that GBLs can improve memory, cognition, behavior, and psychopathology of NDs and that the most frequently associated GBLs are depression, followed by Alzheimer's disease, stroke, Huntington's disease, and Parkinson's disease. Additionally, the clinical studies of depression, AD, and stroke are the most common, and most of the remaining ND data are available from in vitro or in vivo animal studies. Moreover, the possible mechanisms of GBLs in treating NDs are mainly through free radical scavenging, anti-oxidant activity, anti-inflammatory response, mitochondrial protection, neurotransmitter regulation, and antagonism of PAF. This is the first paper to systematically and comprehensively investigate the pharmacological effects and neuroprotective mechanisms of GBLs in the treatment of NDs thus far. All findings contribute to a better understanding of the efficacy and complexity of GBLs in treating NDs, which is of great significance for the further clinical application of this herbal medicine.

CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions.

Chinese patent medicine (CPM) is a typical type of traditional Chinese medicine (TCM) preparation that uses Chinese herbs as raw materials and is an important means of treating diseases in TCM. Chinese patent medicine instructions (CPMI) serve as a guide for patients to use drugs safely and effectively. In this study, we apply a pre-trained language model to the domain of CPM. We have meticulously assembled, processed, and released the first CPMI dataset and fine-tuned the ChatGLM-6B base model, resulting in the development of CPMI-ChatGLM. We employed consumer-grade graphics cards for parameter-efficient fine-tuning and investigated the impact of LoRA and P-Tuning v2, as well as different data scales and instruction data settings on model performance. We evaluated CPMI-ChatGLM using BLEU, ROUGE, and BARTScore metrics. Our model achieved scores of 0.7641, 0.8188, 0.7738, 0.8107, and - 2.4786 on the BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L and BARTScore metrics, respectively. In comparison experiments and human evaluation with four large language models of similar parameter scales, CPMI-ChatGLM demonstrated state-of-the-art performance. CPMI-ChatGLM demonstrates commendable proficiency in CPM recommendations, making it a promising tool for auxiliary diagnosis and treatment. Furthermore, the various attributes in the CPMI dataset can be used for data mining and analysis, providing practical application value and research significance.

Optimization of pneumonia CT classification model using RepVGG and spatial attention features.

Introduction: Pneumonia is a common and widespread infectious disease that seriously affects the life and health of patients. Especially in recent years, the outbreak of COVID-19 has caused a sharp rise in the number of confirmed cases of epidemic spread. Therefore, early detection and treatment of pneumonia are very important. However, the uneven gray distribution and structural intricacy of pneumonia images substantially impair the classification accuracy of pneumonia. In this classification task of COVID-19 and other pneumonia, because there are some commonalities between this pneumonia, even a small gap will lead to the risk of prediction deviation, it is difficult to achieve high classification accuracy by directly using the current network model to optimize the classification model. Methods: Consequently, an optimization method for the CT classification model of COVID-19 based on RepVGG was proposed. In detail, it is made up of two essential modules, feature extraction backbone and spatial attention block, which allows it to extract spatial attention features while retaining the benefits of RepVGG. Results: The model's inference time is significantly reduced, and it shows better learning ability than RepVGG on both the training and validation sets. Compared with the existing advanced network models VGG-16, ResNet-50, GoogleNet, ViT, AlexNet, MobileViT, ConvNeXt, ShuffleNet, and RepVGG_b0, our model has demonstrated the best performance in a lot of indicators. In testing, it achieved an accuracy of 0.951, an F1 score of 0.952, and a Youden index of 0.902. Discussion: Overall, multiple experiments on the large dataset of SARS-CoV-2 CT-scan dataset reveal that this method outperforms most basic models in terms of classification and screening of COVID-19 CT, and has a significant reference value. Simultaneously, in the inspection experiment, this method outperformed other networks with residual structures.

Study on the effect of internet plus continuous nursing on functional recovery and medication compliance of patients with knee joint replacement.

OBJECTIVE: To evaluate the effect of "Internet + " continuity of care on postoperative functional recovery and medication compliance in patients with knee arthroplasty. METHODS: In this retrospective study, 100 patients who underwent knee replacement in our hospital between January 2021 and December 2022 were recruited and assigned to receive routine care (routine group) or "Internet + " continuity of care (continuity group), with 50 patients in each group. Outcome measures included knee function, sleep quality, emotional state, medication compliance, and self-care ability. RESULTS: Patients in the continuity group showed better knee function after discharge and during follow-up versus those in the routine group (P < 0.05). Continuity care resulted in significantly lower Pittsburgh Sleep Quality Index (PSQI), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) scores versus routine care (P < 0.05). Patients in the continuity group showed higher treatment compliance, ability of daily living (ADL) scores, and nursing satisfaction than those in the routine group (P < 0.05). CONCLUSION: The "Internet + " continuity of care is highly feasible and can effectively promote the postoperative functional recovery of knee replacement patients, improve patients' medication compliance, sleep quality, and self-care ability, mitigate negative emotions, and provide enhanced home care.

Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.

Diabetic retinopathy (DR) is a common retinal vascular disease, which can cause severe visual impairment. It is of great clinical significance to use fundus images for intelligent diagnosis of DR. In this paper, an intelligent DR classification model of fundus images is proposed. This method can detect all the five stages of DR, including of no DR, mild, moderate, severe, and proliferative. This model is composed of two key modules. FEB, feature extraction block, is mainly used for feature extraction of fundus images, and GPB, grading prediction block, is used to classify the five stages of DR. The transformer in the FEB has more fine-grained attention that can pay more attention to retinal hemorrhage and exudate areas. The residual attention in the GPB can effectively capture different spatial regions occupied by different classes of objects. Comprehensive experiments on DDR datasets well demonstrate the superiority of our method, and compared with the benchmark method, our method has achieved competitive performance.