Clinical Trials

Quantifying the severity of patient safety events via statistical natural language processing

Medical errors are one of the leading causes of death in the United States. Several public databases have been built to record patient safety events across healthcare systems to better understand and improve safety hazards. These reports typically include both structured fields (e.g., event type, device, manufacturer) and unstructured data elements (free text narrative of what happened). The struc...

The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs

Medical Multimodal Large Language Models (Medical MLLMs) have achieved remarkable progress in specialized medical tasks; however, research into their safety has lagged, posing potential risks for real-world deployment. In this paper, we first establish a multidimensional evaluation framework to systematically benchmark the safety of current SOTA Medical MLLMs. Our empirical analysis reveals pervas...

A medically grounded LLM agent–based tool to detect patient safety events in medical records

Large language models (LLMs) have shown incredible promise in medicine. While LLMs may be particularly useful in areas requiring extensive review of c...

Ocrelizumab versus Natalizumab in Relapsing-Remitting Multiple Sclerosis: A Registry-Linked Electronic Health Records Study

Ocrelizumab and natalizumab are commonly prescribed high-effectiveness disease-modifying therapies (DMTs) for relapsing-remitting multiple sclerosis (...

Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project

Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD...

Using the Minnesota Multiphasic Personality Inventory-2 restructured form to predict functioning after treatment for borderline personality disorder: A machine learning approach.

Insight into predictors of functioning after treatment for borderline personality disorder (BPD) is limited, despite growing recognition that more foc...

Effect of artificial intelligence driven therapeutic lifestyle changes (AI-TLC) intervention on health behavior and health among obesity pregnant women in China: a randomized controlled trial protocol.

INTRODUCTION: Obesity has reached epidemic proportions globally, posing significant challenges to public health and economic stability. In China, the ...

Leveraging big data in health care and public health for AI driven talent development in rural areas.

INTRODUCTION: This study proposes a novel Transformer-based approach to enhance talent attraction and retention strategies in rural public health syst...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weigh...

Leveraging Generative AI for Drug Safety and Pharmacovigilance.

Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has bee...

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and c...

Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to devel...

[Risk prediction of Reduning Injection batches by near-infrared spectroscopy combined with multiple machine learning algorithms].

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting ...

Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.

Background & Objectives Non-pharmacological interventions (NPI) were crucial in curbing the initial COVID-19 pandemic waves, but compliance was diffic...

Residual XGBoost regression-Based individual moving range control chart for Gross Domestic Product growth monitoring.

Accurate and reliable Gross Domestic Product (GDP) forecasting is indispensable for informed economic policymaking and risk management. Autocorrelatio...

MLLM-as-a-Judge for Image Safety without Human Labeling

Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated con...

xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability

Reinforcement learning (RL) has shown great promise in simulated environments, such as games, where failures have minimal consequences. However, the...

[Expert consensus on ethical requirements for artificial intelligence (AI) processing medical data].

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently...

Three mechanistically different variability and noise sources in the trial-to-trial fluctuations of responses to brain stimulation

Motor-evoked potentials (MEPs) are among the few directly observable responses to external brain stimulation and serve a variety of applications, of...

Deliberative Alignment: Reasoning Enables Safer Language Models

As large-scale language models increasingly impact safety-critical domains, ensuring their reliable adherence to well-defined principles remains a f...