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Turning Patients' Open-Ended Narratives of Chronic Pain Into Quantitative Measures: Natural Language Processing Study.

BACKGROUND: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience. OBJECTIVE: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives. METHODS: Patients' narratives were collected during recorded…

Data Extraction and Curation from Radiology Reports for Pancreatic Cyst Surveillance Using Large Language Models.

BACKGROUND: Manual curation of radiographic features in pancreatic cyst registries for data abstraction and longitudinal evaluation is time-consuming and limits widespread implementation. We examined the feasibility and accuracy of using large language models (LLMs) to extract clinical variables from radiology reports. STUDY DESIGN: A single-center retrospective study included patients under surveillance for pancreatic cysts. Nine radiographic elements used to monitor cyst progression were included: cyst size, main pancreatic duct (MPD) size (continuous variable), number of lesions, MPD dilat…

Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.

BACKGROUND: Bicuspid aortic valve (BAV) is the most common congenital heart defect but often evades timely diagnosis due to variable clinical presentations. Prior to October 2024, no specific diagnosis code existed for BAV, limiting retrospective identification. OBJECTIVES: The purpose of this study was to develop and validate a natural language processing (NLP) system for automated extraction of heart valve morphology from echocardiographic reports, with focus on BAV detection. METHODS: We developed a rule-based NLP system using MedSpaCy to analyze echocardiographic reports from the Veteran…

Language dysfunction and related amyloid-β in Alzheimer's disease mediated by brain functional connectivity changes.

BACKGROUND: Language dysfunction occurs early in Alzheimer's disease (AD) and whether amyloid-β pathology is related to language dysfunction remains unclear. Functional connectivity (FC) in language networks is critical for language function. We hypothesize that altered FCs in fronto-temporal regions (core language areas) mediate the association between amyloid-β burden and language impairment. METHODS: A total of 110 individuals were recruited including 44 cognitively unimpaired individuals, 24 mild cognitive impairment patients and 42 AD patients. The acoustic features and semantic content …

Understanding the regulatory grammar of sepsis-causing Staphylococcus aureus bacteria using contexualised DNA language models.

To efficiently and accurately understand, detect, and treat sepsis, we require a deep understanding of how its underlying biological systems work. Conventional methods struggle to provide a detailed view of both the functional and regulatory elements orchestrating a system-wide response to pathogenic infection. Our approach was to use a streamlined process: First, we used multi-omics integration to create a functional-omics signature at the single molecule level, employing sparse Partial Least Squares Discriminant Analysis (sPLSDA). Then, we turned to genome Natural Language Processing (genome…

Large language models' capabilities in responding to tuberculosis medical questions: testing ChatGPT, Gemini, and Copilot.

This study aims to evaluate the capability of Large Language Models (LLMs) in responding to questions related to tuberculosis. Three large language models (ChatGPT, Gemini, and Copilot) were selected based on public accessibility criteria and their ability to respond to medical questions. Questions were designed across four main domains (diagnosis, treatment, prevention and control, and disease management). The responses were subsequently evaluated using DISCERN-AI and NLAT-AI assessment tools. ChatGPT achieved higher scores (4 out of 5) across all domains, while Gemini demonstrated superior p…

Обнаружение новых листьев с использованием большой языковой модели: прикладные программы в массивах данных с множественным склерозом в реальном мире.

ЦЕЛЬ: Нейроимация обычно используется для выявления новых видов подстрекательской деятельности при множественном склерозе (МС). Крупная языковая модель классификации описей магнитного резонанса в электронной медицинской записи (ЭПЧ) в качестве дискретных данных может принести значительные выгоды для исследований в области НР. Цели нынешнего исследования заключались в разработке такой оперативной иллюстрации его исследовательской деятельности на основе общего клинического сценария: мониторинг реакции на разрушающую клетки B (BCDT). МЕТОДЫ: Институциональная экосистема, которая надежно увязывает

Совпадение в аннотации оригинала
OBJECTIVE: Neuroimaging is routinely utilized to identify new inflammatory activity in multiple sclerosis (MS). A large language model to classify narrative magnetic resonance imaging reports in the electronic health record (EHR) as discrete data could provide significant benefits for MS research. The objectives of the current study were to develop such a pr…

Evolution of publicly available large language models for complex decision-making in breast cancer care.

PURPOSE: This study investigated the concordance of five different publicly available Large Language Models (LLM) with the recommendations of a multidisciplinary tumor board regarding treatment recommendations for complex breast cancer patient profiles. METHODS: Five LLM, including three versions of ChatGPT (version 4 and 3.5, with data access until September 3021 and January 2022), Llama2, and Bard were prompted to produce treatment recommendations for 20 complex breast cancer patient profiles. LLM recommendations were compared to the recommendations of a multidisciplinary tumor board (gold …

Naming fMRI-guided white matter language tract volumes influence naming decline after temporal lobe resection.

OBJECTIVE: The aim of this study was to explore the relation of language functional MRI (fMRI)-guided tractography with postsurgical naming decline in people with temporal lobe epilepsy (TLE). METHODS: Twenty patients with unilateral TLE (9 left) were studied with auditory and picture naming functional MRI tasks. Activation maxima in the left posterobasal temporal lobe were used as seed regions for whole-brain fibre tractography. Clinical naming performance was assessed preoperatively, 4 months, and 12 months following temporal lobe resection. Volumes of white matter language tracts in both h…

Hippocampal activations obtained during language fMRI tasks: A complementary tool for predicting postoperative memory prognosis.

In medial temporal lobe epilepsy (MTLE), the benefits of surgery must be balanced against the risk of post-operative memory decline. Prediction of postoperative outcomes based on functional magnetic resonance imaging (fMRI) tasks is increasingly common but remains uncertain. The aim of this retrospective study was to determine whether hippocampal activations elicited by fMRI language tasks could enhance or refine memory fMRI in MTLE patients candidates to surgery. Forty-six patients were included: 30 right and 16 left MTLE, mostly with hippocampal sclerosis. Preoperative assessment included ne…

Performance of large language artificial intelligence models on solving restorative dentistry and endodontics student assessments.

OBJECTIVES: The advent of artificial intelligence (AI) and large language model (LLM)-based AI applications (LLMAs) has tremendous implications for our society. This study analyzed the performance of LLMAs on solving restorative dentistry and endodontics (RDE) student assessment questions. MATERIALS AND METHODS: 151 questions from a RDE question pool were prepared for prompting using LLMAs from OpenAI (ChatGPT-3.5,-4.0 and -4.0o) and Google (Gemini 1.0). Multiple-choice questions were sorted into four question subcategories, entered into LLMAs and answers recorded for analysis. P-value and ch…

Large Language Models Versus Expert Clinicians in Crisis Prediction Among Telemental Health Patients: Comparative Study.

BACKGROUND: Due to recent advances in artificial intelligence, large language models (LLMs) have emerged as a powerful tool for a variety of language-related tasks, including sentiment analysis, and summarization of provider-patient interactions. However, there is limited research on these models in the area of crisis prediction. OBJECTIVE: This study aimed to evaluate the performance of LLMs, specifically OpenAI's generative pretrained transformer 4 (GPT-4), in predicting current and future mental health crisis episodes using patient-provided information at intake among users of a national t…

Automatic inference of ICD-10 codes from German ophthalmologic physicians' letters using natural language processing.

Physicians' letters are the optimal source of diagnoses for registries. However, most registries demand for diagnosis codes such as ICD-10. … s. The inferred ICD-10 codes were evaluated for correctness by the senders. A total of 3332 natural language terms had been sent in (812 hospital one, 1473 hospital two, 1047 hospital three). A total of 526 non-diagnoses were excluded upfront. 2806 ICD-10 codes were inferred (771 hospital one, 1226 hospital two, 809 hospital three). In the first hospital, …

Understanding how CD19 expression levels impact the response to loncastuximab tesirine: a plain language summary.

WHAT IS THIS SUMMARY ABOUT?: In this article, we summarize results from a clinical study called LOTIS-2, in which researchers looked at patients with a type of blood cancer called diffuse large B-cell lymphoma, or DLBCL for short. Patients received the drug loncastuximab tesirine, or Lonca for short, which targets a marker on the surface of tumor cells called CD19.Patient information from the LOTIS-2 study, other studies of Lonca, and information from scientific publications was used to develop a quantitative systems pharmacology (QSP) model, which can predict how Lonca works in the body. The

Large language models outperform mental and medical health care professionals in identifying obsessive-compulsive disorder.

Despite the promising capacity of large language model (LLM)-powered chatbots to diagnose diseases, they have not been tested for obsessive-compulsive disorder (OCD). We assessed the diagnostic accuracy of LLMs in OCD using vignettes and found that LLMs outperformed medical and mental health professionals. This highlights the potential benefit of LLMs in assisting in the timely and accurate diagnosis of OCD, which usually entails a long delay in diagnosis and treatment.

Thoracic Aorta Measurement Extraction from Computed Tomography Radiology Reports Using Instruction Tuned Large Language Models.

Chest computed tomography (CT) is essential for diagnosing and monitoring thoracic aortic dilations and aneurysms, conditions that place pat … ts, and compared the performance of fine-tuned BERT-based models with instruction-tuned Llama large language models (LLMs). Applying the best-performing method to a real-world large chest CT radiology report database, we generated a comprehensive aortic measurement dataset that facilitates big data aortic disease research.

Bridging Language Barriers in COVID-19 Research: Descriptive Study of AccesoCovid.com's Reach and User Engagement.

BACKGROUND: The COVID-19 pandemic underscored the challenge of swiftly disseminating research findings to a global audience. Language barriers further exacerbated disparities in access to timely scientific information, particularly for non-English speaking communities. The majority of COVID-19 research was published in English, limiting accessibility for Spanish-speaking populations. OBJECTIVE: This paper aims to assess the reach and effectiveness of AccesoCovid.com, a platform designed to disseminate up-to-date COVID-19 research in both English and Spanish, addressing the language gap in sci…

Evaluation of Patient Education Materials From Large-Language Artificial Intelligence Models on Carpal Tunnel Release.

BACKGROUND: ChatGPT, an artificial intelligence technology, has the potential to be a useful patient aid, though the accuracy and appropriateness of its responses and recommendations on common hand surgical pathologies and procedures must be understood. Comparing the sources referenced and characteristics of responses from ChatGPT and an established search engine (Google) on carpal tunnel surgery will allow for an understanding of the utility of ChatGPT for patient education. METHODS: A Google search of "carpal tunnel release surgery" was performed and "frequently asked questions (FAQs)" were

Association between ethnicity, language and metformin prescriptions in U.S. community health center patients with diabetes.

BACKGROUND: Metformin treatment is a recommended first-line medication for patients with type 2 diabetes. Latino patients are subject to fac … atients were from non-Latino white, English-preferring Latino, and Spanish-preferring Latino ethnic/language groups. We modeled adjusted odds of metformin prescription and adjusted hazards (time-to-event) of metformin prescription after diabetes diagnosis and high hemoglobin A1c (HbA1c > 9) test results. RESULTS: English-preferring Latinos had similar odd …