Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study.

Background: Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging.

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach.

Background: In oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research.

Beyond the tubule: pathological variants of , encoding the megalin receptor, result in glomerular loss and early progressive chronic kidney disease.

Pathogenic variants in the gene, encoding the multiligand receptor megalin, cause a rare autosomal recessive syndrome: Donnai-Barrow/Facio-Oculo-Acoustico-Renal (DB/FOAR) syndrome. Because of the rarity of the syndrome, the long-term consequences of the tubulopathy on human renal health have been difficult to ascertain, and the human clinical condition has hitherto been characterized as a benign tubular condition with asymptomatic low-molecular-weight proteinuria. We investigated renal function and morphology in a murine model of DB/FOAR syndrome and in patients with DB/FOAR.

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.

Objectives: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a "Dr. Answer" AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy.

Nephron loss detected by MRI following neonatal acute kidney injury in rabbits.

Background: Acute kidney injury affects nearly 30% of preterm neonates in the intensive care unit. We aimed to determine whether nephrotoxin-induced AKI disrupted renal development assessed by imaging (CFE-MRI).

Methods: Neonatal New Zealand rabbits received indomethacin and gentamicin (AKI) or saline (control) for four days followed by cationic ferritin (CF) at six weeks.

Research trends in Korean medicine based on temporal and network analysis.

Background: Much research on Korean medicine has been recently published in Korea. The aim of this study was to determine the research trends in Korean medicine by performing a comprehensive analysis of articles that have been published in Korea using temporal and network analysis methods.

Methods: A total of 29,876 articles from 1963 to 2018 were prepared from OASIS (Oriental Medicine Advanced Searching Integrated System), the largest portal for Korean medicine.

Exploring Research Topics and Trends in Nursing-related Communication in Intensive Care Units Using Social Network Analysis.

This study used social network analysis to identify the main research topics and trends in nursing-related communication in intensive care units. Keywords from January 1967 to June 2016 were extracted from PubMed using Medical Subject Headings terms. Social network analysis was performed using Gephi software.

DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

Background: Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE.

Structuralizing biomedical abstracts with discriminative linguistic features.

Objective: Nearly 75% of the abstracts in MEDLINE papers present in an unstructured format. This study aims to automate the reformatting of unstructured abstracts into the Introduction, Methods, Results, and Discussion (IMRAD) format. The quality of this reformatting relies on the features used in sentence classification.

Marginal and Internal Fit of Conventional Metal-Ceramic and Lithium Disilicate CAD/CAM Crowns.

This study aimed to evaluate and compare the marginal and internal gap widths of lithium disilicate computer-aided design / computer-assisted manufacture (LDC) crowns and conventionally produced porcelain-fused-to-metal (PFM) crowns. A convenience sample of 21 patients treated with a single restoration was selected. PFM and LDC crowns were fabricated for each selected abutment tooth, following traditional crown preparation.

TM-MC: a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine.

Background: In traditional medicine, there has been a great deal of research on the effects exhibited by medicinal materials. To study the effects, resources that can systematically describe the chemical compounds in medicinal materials are necessary. In recent years, numerous databases on medicinal materials and constituent compounds have been constructed.

Ontology-based reusable clinical document template production system.

Clinical documents embody professional clinical knowledge. This paper shows an effective clinical document template (CDT) production system that uses a clinical description entity (CDE) model, a CDE ontology, and a knowledge management system called STEP that manages ontology-based clinical description entities. The ontology represents CDEs and their inter-relations, and the STEP system stores and manages CDE ontology-based information regarding CDTs.