A5052287559- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare and Education
- Cardiac, Anesthesia and Surgical Outcomes
- Clinical Reasoning and Diagnostic Skills
- Vestibular and auditory disorders
- Interpreting and Communication in Healthcare
- Acute Kidney Injury Research
- Delphi Technique in Research
- Computational and Text Analysis Methods
- Palliative Care and End-of-Life Issues
- Ultrasound in Clinical Applications
- COVID-19 and healthcare impacts
- Trauma, Hemostasis, Coagulopathy, Resuscitation
- Topic Modeling
- Health disparities and outcomes
- Sepsis Diagnosis and Treatment
- Inflammatory Bowel Disease
- Colorectal Cancer Screening and Detection
- Autopsy Techniques and Outcomes
- Travel-related health issues
- Patient-Provider Communication in Healthcare
- Salivary Gland Tumors Diagnosis and Treatment
- Data-Driven Disease Surveillance
- Diagnosis and Treatment of Venous Diseases
- Takotsubo Cardiomyopathy and Associated Phenomena
Draper Laboratory
2025
University of California, San Francisco
2023-2025
University of Cambridge
2019-2024
Cambridge University Hospitals NHS Foundation Trust
2022-2023
University of Glasgow
2023
Addenbrooke's Hospital
2021
University of Birmingham
2020
Cambridge School
2019
Institute for Clinical Evaluative Sciences
2017
Hospital for Sick Children
2017
The introduction of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4; OpenAI), has generated significant interest in health care, yet studies evaluating their performance a clinical setting are lacking. Determination acuity, measure patient's illness severity and level required medical attention, is one the foundational elements reasoning emergency medicine.
The release of GPT-4 and other large language models (LLMs) has the potential to transform healthcare. However, existing research evaluating LLM performance on real-world clinical notes is limited. Here, we conduct a highly-powered study determine whether LLMs can provide recommendations for three tasks (admission status, radiological investigation(s) request antibiotic prescription status) using from Emergency Department. We randomly selected 10,000 Department visits evaluate accuracy...
Abstract Importance Large language models (LLMs) possess a range of capabilities which may be applied to the clinical domain, including text summarization. As ambient artificial intelligence scribes and other LLM-based tools begin deployed within healthcare settings, rigorous evaluations accuracy these technologies are urgently needed. Objective To investigate performance GPT-4 GPT-3.5-turbo in generating Emergency Department (ED) discharge summaries evaluate prevalence type errors across...
Existing research on social determinants of health (SDoH) predominantly focuses physician notes and structured data within electronic medical records. This study posits that work are an untapped, potentially rich source for SDoH information. We hypothesize clinical recorded by workers, whose role is to ameliorate economic factors, might provide a complementary information compared notes, which primarily concentrate diagnoses treatments. aimed use word frequency analysis topic modeling...
Takotsubo syndrome mimics an acute myocardial infarction, typically in the aftermath of mental or physical stress.The mechanism by which emotional processing context stress leads to significant cardiac injury is poorly understood, so a full exploration brain structure and function takotsubo patients merits investigation.Twenty-five (<5 days) 25 control subjects were recruited into this observational cross-sectional study. Surface-based morphometry was carried out on magnetic resonance...
Abstract Importance: Tumor necrosis factor inhibitors (TNFi) are widely used for auto-immune conditions. Despite their efficacy, many patients switch TNFis due to lack of cost-related reasons, or adverse events. Understanding why switches occur is important, but requires extensive chart review. Objective: To determine whether large language models (LLMs) can automatically perform review, accurately identifying TNFi switching trajectories and reasons in a real-world cohort. Design:...
The incidence of acute kidney injury (AKI) and its impact on chronic disease (CKD) following pediatric nonkidney solid organ transplantation is unknown. We aimed to determine the AKI CKD examine their relationship among children who received a heart, lung, liver, or multiorgan transplant at Hospital for Sick Children between 2002 2011. was assessed in first year posttransplant. Among 303 children, perioperative (within week) occurred 67% after week 36%, with highest lung recipients....
Abstract This paper evaluates the performance of Chat Generative Pre-trained Transformer (ChatGPT; GPT-3.5) in accurately identifying higher acuity patients a real-world clinical context. Using dataset 10,000 pairs patient Emergency Department (ED) visits with varying levels, we demonstrate that GPT-3.5 can successfully determine based on history sections extracted from ED physician notes. The model achieves an accuracy 84% and F1 score 0.83, improved for more disparate scores. Among 500...
Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women the United States using contraceptives, understanding factors that drive selection and switching is of significant interest. However, many related to medication are often only captured unstructured clinical notes can be difficult extract. Here, we evaluate zero-shot abilities recently developed large language model, GPT-4 (via HIPAA-compliant Microsoft Azure API), identify...
There is a paucity of research examining patient experiences cochlear implants. We sought to use natural language processing methods explore and concerns in the online implant (CI) community.Cross-sectional study posts on Reddit r/CochlearImplants forum from 1 March 2015 11 November 2021. Natural using BERTopic automated topic modelling technique was employed cluster into semantically similar topics. Topic categorisation manually validated by two independent reviewers Cohen's kappa...
Sepsis is a major cause of morbidity and mortality worldwide. In the updated, 2016 Sepsis-3 criteria, sepsis defined as life-threatening organ dysfunction caused by dysregulated host response to infection, where can be represented an increase in Sequential Organ Failure Assessment (SOFA) score 2 points or more. We sought apply criteria characterise septic cohort Amsterdam University Medical Centres database (Amsterdam UMCdb).
Large language models (LLMs), such as OpenAI's Generative Pre-Trained Transformer-4 (GPT-4), can generate, audit, and process data without domain-specific training. LLMs have many potential health care applications but require validation testing before deployment. Much of the current LLM research in has focused on supporting clinical decision making.1 Charting augmentation is another area for application, with a lower risk patient harm than that directly influence medical making. In...
Abstract Objective We aimed to investigate the impact of social circumstances on cancer therapy selection using natural language processing derive insights from worker documentation. Materials and Methods developed employed a Bidirectional Encoder Representations Transformers (BERT) based approach, hierarchical multi-step BERT model (BERT-MS), predict prescription targeted patients solely documentation by clinical workers. Our corpus included free-text work notes, combined with medication...
To determine the frequency of incidental findings found on magnetic resonance imaging scans internal auditory meatus performed to investigate audiovestibular symptoms, and how best manage these when found.A retrospective review was conducted during a three-month period in radiology department at UK district general hospital.A total 109 were reviewed. Of these, 92.7 per cent showed no retrocochlear pathology, 0.9 vestibular schwannoma, 6.4 revealed vascular loops, 2.8 that warranted further...
Objective: To explore the Nijmegen Questionnaire (NQ) and its relationship to vestibular function tests symptoms in patients with dizziness; compare patient characteristics between those a positive score clinically diagnosed hyperventilation syndrome (HVS). Study Design: Retrospective case series. Setting: Tertiary neurotology referral center. Patients: Patients seen at assessment were grouped according (≥24) or negative (<24) scores; secondary analysis was performed on by clinical...
People living with HIV-1 (PLWH) have elevated constitutive expression of type 1 interferons (IFN). However, it is unclear whether this affects downstream innate immune responses.
There has been conflicting public messaging from government and state officials about recommended health behaviours during the COVID-19 pandemic. We examined whether differences in political affiliation influences public's interest infection prevention measures United States. State-specific data on search four key (Quarantine, Social distancing, Hand washing Masks) were obtained Google Trends for period 1 January 2020 to 12 December 2020. Political was ascertained based U.S. Presidential...
Recent advances in generative models, including large language models (LLMs), vision (VLMs), and diffusion have accelerated the field of natural image processing medicine marked a significant paradigm shift how biomedical can be developed deployed. While these are highly adaptable to new tasks, scaling evaluating their usage presents challenges not addressed previous frameworks. In particular, ability produce useful outputs with little no specialized training data ("zero-" or "few-shot"...
Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment inflammatory bowel (IBD). However, manual extraction of these PROs from the free-text clinical notes is burdensome. We aimed to improve data curation information electronic health record, making it more available for research quality improvement. This study compare traditional natural language processing (tNLP) large models (LLMs) extracting three IBD (abdominal pain, diarrhea, fecal blood) across two institutions.
Importance: High quality discharge summaries are associated with improved patient outcomes but contribute to clinical documentation burden. Large language models (LLMs) provide an opportunity support physicians by drafting summary narratives. Objective: To determine whether LLM-generated narratives of comparable and safety those physicians. Design: Cross-sectional study. Setting: University California, San Francisco. Participants: 100 randomly selected Inpatient Hospital Medicine encounters...