Construction and Evaluation of Intelligent Medical Diagnosis Model Based on Integrated Deep Neural Network

Mechanistic Investigation of Calcium Channel Regulation-Associated Genes in Pulmonary Arterial Hypertension and Signatures for Diagnosis

Pulmonary arterial hypertension (PAH) is a severe cardiopulmonary disorder with complex causes. Calcium channel blockers have long been used in its treatment. Our study aimed to validate experimental results showing increased calcium ion concentration in PAH patients. We investigated the impact of genes related to calcium channel regulation on PAH development and developed an accurate diagnostic model. Clinical trial data from serum of 18 healthy individuals and 18 patients with PAH were retrospectively analyzed. Concentrations of calcium and potassium ions were determined and compared. Datasets were retrieved, selecting genes associated with calcium ion release. R packages processed the datasets, filtering 174 common genes, and conducting Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Six hub genes were identified, and nomogram and logistic regression prediction models were constructed. Random forest filtered cross genes, and a diagnostic model was developed and validated using an artificial neural network. The 174 intersection genes related to calcium ions showed significant correlations with biological processes, cellular components, and molecular functions. Six key genes were obtained by constructing a protein-protein interaction network. A diagnostic model with high accuracy (> 90%) and diagnostic capability (AUC = 0.98) was established using a neural network algorithm. This study validated the experimental results, identified key genes associated with calcium ions, and developed a highly accurate diagnostic model using a neural network algorithm. These findings provide insights into the role of calcium release genes in PAH and demonstrate the potential of the diagnostic model for clinical application. However, due to limitations in sample size and a lack of prognosis data, the regulatory mechanisms of calcium ions in PAH patients and their impact on the clinical prognosis of PAH patients still need further exploration in the future.

What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases

High mortality rates from cardiovascular diseases (CVDs) persist worldwide. Older people are at a higher risk of developing these diseases. Given the current high treatment cost for CVDs, there is a need to prevent CVDs and or develop treatment alternatives. Western and Chinese medicines have been used to treat CVDs. However, several factors, such as inaccurate diagnoses, non-standard prescriptions, and poor adherence behavior, lower the benefits of the treatments by Chinese medicine (CM). Artificial intelligence (AI) is increasingly used in clinical diagnosis and treatment, especially in assessing efficacy of CM in clinical decision support systems, health management, new drug research and development, and drug efficacy evaluation. In this study, we explored the role of AI in CM in the diagnosis and treatment of CVDs, and discussed application of AI in assessing the effect of CM on CVDs.

A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification

Text classification is widely studied by researchers in the natural language processing field. However, real-world text data often follow a long-tailed distribution as the frequency of each class is typically different. The performance of current mainstream learning algorithms in text classification suffers when the training data are highly imbalanced. The problem can get worse when the categories with fewer data are severely undersampled to the extent that the variation within each category is not fully captured by the given data. At present, there are a few studies on long-tailed text classification which put forward effective solutions. Encouraged by the progress of handling long-tailed data in the field of image, we try to integrate effective ideas into the field of long-tailed text classification and prove the effectiveness. In this paper, we come up with a novel approach of feature space reconstruction with the help of three-way decisions (3WDs) for long-tailed text classification. In detail, we verify the rationality of using a 3WD model for feature selection in long-tailed text data classification, propose a new feature space reconstruction method for long-tailed text data for the first time, and demonstrate how to effectively generate new samples for tail classes in reconstructed feature space. By adding new samples, we enrich the representing information of tail classes, to improve the classification results of long-tailed text classification. After some comparative experiments, we have verified that our model is an effective strategy to improve the performance of long-tailed text classification.