Performance Comparison of Junior Residents and ChatGPT in the Objective Structured Clinical Examination (OSCE) for Medical History Taking and Documentation of Medical Records: Development and Usability Study.

Background: This study explores the cutting-edge abilities of large language models (LLMs) such as ChatGPT in medical history taking and medical record documentation, with a focus on their practical effectiveness in clinical settings-an area vital for the progress of medical artificial intelligence.

Objective: Our aim was to assess the capability of ChatGPT versions 3.5 and 4.

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

Introduction: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies.

A Retrospective Comparison of Diagnostic Tools of Bullous Pemphigoid for Elderly Patients.

Background: Older patients who are predisposed to bullous pemphigoid (BP) may exhibit reluctance to undergo skin biopsy due to potential complications.

Objectives: This study aimed to conduct a comparative evaluation among histology, direct immunofluorescence (DIF), and indirect immunofluorescence (IIF) to determine the optimal diagnostic tool in elderly patients.

Methods: A retrospective study was conducted on 841 patients suspected of having BP.

Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection.

Background: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies.

Objective: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database.

A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.

Background: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging.

Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits.

The increasing prevalence of overcrowding in Emergency Departments (EDs) threatens the effective delivery of urgent healthcare. Mitigation strategies include the deployment of monitoring systems capable of tracking and managing patient disposition to facilitate appropriate and timely care, which subsequently reduces patient revisits, optimizes resource allocation, and enhances patient outcomes. This study used ∼ 250,000 emergency department visit records from Taipei Medical University-Shuang Ho Hospital to develop a natural language processing model using BlueBERT, a biomedical domain-specific pre-trained language model, to predict patient disposition status and unplanned readmissions.

Applying machine learning to assess the morphology of sculpted teeth.

Background/purpose: Producing tooth crowns through dental technology is a basic function of dentistry. The morphology of tooth crowns is the most important parameter for evaluating its acceptability. The procedures were divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation.

In vivo vascularized scaffold with different shear-exposed models for lymphatic tissue regeneration.

Current clinical treatments on lymphedema provide promising results, but also result in donor site morbidities. The establishment of a microenvironment optimized for lymphangiogenesis can be an alternative way to enhance lymphatic tissue formation. Hemodynamic flow stimuli have been confirmed to have an influential effect on angiogenesis in tissue engineering, but not on lymphatic vessel formation.

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail.

Using Text Mining and Data Visualization Approaches for Investigating Mental Illness from the Perspective of Traditional Chinese Medicine.

Anxiety and depressive disorders are the most prevalent mental disorders, and due to the COVID-19 pandemic, more people are suffering from anxiety and depressive disorders, and a considerable fraction of COVID-19 survivors have a variety of persistent neuropsychiatric problems after the initial infection. Traditional Chinese Medicine (TCM) offers a different perspective on mental disorders from Western biomedicine. Effective management of mental disorders has become an increasing concern in recent decades due to the high social and economic costs involved.

Clinical narrative-aware deep neural network for emergency department critical outcome prediction.

Since early identification of potential critical patients in the Emergency Department (ED) can lower mortality and morbidity, this study seeks to develop a machine learning model capable of predicting possible critical outcomes based on the history and vital signs routinely collected at triage. We compare emergency physicians and the predictive performance of the machine learning model. Predictors including patients' chief complaints, present illness, past medical history, vital signs, and demographic data of adult patients (aged ≥ 18 years) visiting the ED at Shuang-Ho Hospital in New Taipei City, Taiwan, are extracted from the hospital's electronic health records.

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation.

A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively.

Linguistic Pattern-Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases-Mail Database: Algorithm Development Study.

Background: Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)-mail is the most comprehensive international expert-curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the reports makes analysis for further application difficult.

Deep Multi-Objective Learning from Low-Dose CT for Automatic Lung-RADS Report Generation.

Radiology report generation through chest radiography interpretation is a time-consuming task that involves the interpretation of images by expert radiologists. It is common for fatigue-induced diagnostic error to occur, and especially difficult in areas of the world where radiologists are not available or lack diagnostic expertise. In this research, we proposed a multi-objective deep learning model called CT2Rep (Computed Tomography to Report) for generating lung radiology reports by extracting semantic features from lung CT scans.

Creation of a rat lymphedema model using extensive lymph node dissection and circumferential soft tissue resection: Is this a reliable model?

Introduction: The medical demand for lymphedema treatment is huge since the disease mechanism remains unclear, and management are difficult. Our purpose was to develop a reliable lymphedema model mimicking the clinical scenario and allows a microsurgical approach.

Materials And Methods: Male Lewis rats weighing 400 to 450 g were used to create lymphedema with groin and popliteal lymph node dissection and creation of 5 mm circumferential skin defect (n = 6).

Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach.

We aimed to develop and validate a model for predicting mortality in patients with angina across the spectrum of dysglycemia. A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality served as the primary endpoint.

Social Media Mining for an Analysis of Nutrition and Dietary Health in Taiwan.

Dining is an essential part of human life. In order to pursue a healthier self, more and more people enjoy homemade cuisines. Consequently, the amount of recipe websites has increased significantly.

LBERT: Lexically aware Transformer-based Bidirectional Encoder Representation model for learning universal bio-entity relations.

Motivation: Natural Language Processing techniques are constantly being advanced to accommodate the influx of data as well as to provide exhaustive and structured knowledge dissemination. Within the biomedical domain, relation detection between bio-entities known as the Bio-Entity Relation Extraction (BRE) task has a critical function in knowledge structuring. Although recent advances in deep learning-based biomedical domain embedding have improved BRE predictive analytics, these works are often task selective or use external knowledge-based pre-/post-processing.

Refined distributed emotion vector representation for social media sentiment analysis.

As user-generated content increasingly proliferates through social networking sites, our lives are bombarded with ever more information, which has in turn has inspired the rapid evolution of new technologies and tools to process these vast amounts of data. Semantic and sentiment analysis of these social multimedia have become key research topics in many areas in society, e.g.

Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Objective: In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies.

Materials And Methods: In this study, we propose a clinical text representation infused with medical knowledge (MK).

LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task.

Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs.