Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.

Background: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.

Objective: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.

OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer.

Patients with oligometastatic cancer (OMC) exhibit better response to local therapeutic interventions and a more treatable tendency than those with polymetastatic cancers. However, studies on OMC are limited and lack effective integration for systematic comparison and personalized application, and the diagnosis and precise treatment of OMC remain controversial. The application of large language models in medicine remains challenging because of the requirement of high-quality medical data.

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application.

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case classification systems. Our goal is to build a structured public online knowledgebase describing the worldwide practice of DRGs, which includes systematic indicators for DRGs' performance assessment. Therefore, we manually collected the qualified literature from PUBMED and constructed DRGKB website.

From multi-omics approaches to personalized medicine in myocardial infarction.

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers.

Myocardial infarction unveiled: Key miRNA players screened by a novel lncRNA-miRNA-mRNA network model.

Background: Myocardial infarction (MI) is a major contributor to global mortality, and microRNAs (miRNAs) are important in its pathogenesis. Identifying blood miRNAs with clinical application potential for the early detection and treatment of MI is crucial.

Methods: We obtained MI-related miRNA and miRNA microarray datasets from MI Knowledge Base (MIKB) and Gene Expression Omnibus (GEO), respectively.

Mitochondrial genome characterization of (Orthoptera: Gryllidae) and its phylogenetic implications.

is a cricket widely distributed throughout the world. In this study, we reported the first complete mitogenome sequence of Genus and inferred its phylogeny. The mitogenome of was 16,369 bp and consisted of a control region and a typical set of 37 genes.

[The SWOT analysis and strategic considerations for the present medical devices' procurement].

In this paper, the SWOT analysis method is used to find out the internal strength, weakness, exterior opportunities and threats of the present medical devices' procurements in hospitals and some strategic considerations are suggested as "one direction, two expansions, three changes and four countermeasures".