Prescriptions

Pancreatic cancer risk prediction using deep sequential modeling of longitudinal diagnostic and medication records

Pancreatic ductal adenocarcinoma (PDAC) is a rare, aggressive cancer often diagnosed late with low survival rates, due to the lack of population-wide screening programs and the high cost of currently available early detection methods. To facilitate earlier treatment, we developed an AI-based tool that predicts the risk of pancreatic cancer diagnosis within 6, 12 and 36 months of assessment, using ...

Tuberculosis disease severity assessment using clinical variables and radiology enabled by artificial intelligence

Radiology can define tuberculosis (TB) severity and may guide duration of treatment, however the optimal radiological metric to use and which clinical variables to combine it with in the real-world is unclear. We systematically associated baseline chest X-rays (CXR) metrics with TB treatment outcome using real-world data from diverse TB clinical settings. We used logistic regression to associate 1...

Supervised Machine-Learning Classification of Treatment-Resistant Depression in U.S. Claims Data

Accurate identification of individuals with treatment-resistant depression (TRD) is important to facilitate timely access to appropriate care. However...

Analytical approaches for medication reconciliation-related topics: a scoping review

This scoping review examines literature related to analytical methods for medication reconciliation in the digital era, particularly using artificial ...

A mechanistic neural network model predicts both potency and toxicity of antimicrobial combination therapies

Antimicrobial resistance poses a major global threat due to the diminishing efficacy of current treatments and limited new therapies. Combination ther...

Is Multimodal Better? A Systematic Review of Multimodal versus Unimodal Machine Learning in Clinical Decision-Making

Machine learning has demonstrated success in clinical decision-making, yet the added value of multimodal approaches over unimodal models remains uncle...

Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use - A Scoping Review

Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains ...

Biomedical Text Normalization through Generative Modeling

Around 80% of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and in...

A Pilot Study Comparing Speech Characteristics in People with Parkinson’s Disease and Controls Dancing Weekly Over 5-years

Parkinson’s Disease (PD) is a neurodegenerative disorder that affects motor and non-motor functions. Speech impairments, such as reduced variability i...

Leveraging Feature Transfer to Predict Medication Resistance and Secondary-Clinical Outcomes in Psychotic Disorders in Forensic Settings

Medication resistance in psychotic disorders represents a critical challenge in forensic psychiatry, where up to 50% of patients show poor treatment r...

Development and validation of a personalised antipsychotic selection tool for first-line treatment in severe mental illness

Guidance is lacking on choice of first-line antipsychotic for individuals with incident severe mental illness (SMI). Patients may try several before a...

Extracting TNFi Switching Reasons and Trajectories From Real-World Data Using Large Language Models

Tumor necrosis factor inhibitors (TNFi) are widely used for auto-immune conditions. Despite their efficacy, many patients switch TNFis due to lack of ...

Bridging AI and Healthcare: A Scoping Review of Retrieval-Augmented Generation—Ethics, Bias, Transparency, Improvements, and Applications

Retrieval-augmented generation (RAG) is an emerging artificial intelligence (AI) strategy that integrates encoded model knowledge with external data s...

Medication information extraction using local large language models

Medication information is crucial for clinical routine and research. However, a vast amount is stored in unstructured text, such as doctoral letters, ...

Comprehensive Cerebral Aneurysm Rupture Prediction: From Clustering to Deep Learning

Cerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the global population. Aneurysms can develop due to variou...

PED-X-Bench: A Benchmark of Adult-to-Pediatric Extrapolation Decisions in FDA Drug Labels

Pediatric trials are ethically and logistically difficult, so the U.S. FDA often extrapolates adult data to children when justified. Yet no public res...

Changes in psychiatric documentation and treatment in primary care with artificial intelligence scribe use

Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, the extent to which they may impact clinician pract...

Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation

Robust de-identification is necessary to preserve patient confidentiality and maintain public acceptance of electronic health record (EHR) research. M...

AI-Driven Pharmacovigilance and Molecular Profiling of Fluoroquinolone-Associated Cardiotoxicity in the UAE: A Geospatial and Machine Learning Analysis with Structural Modification Strategies (2018-2023)

Fluoroquinolones, while clinically indispensable, carry underappreciated cardiovascular risks, particularly QT prolongation and life-threatening arrhy...

Machine Learning-Enabled EEG Biomarkers Predict Divergent Antidepressant and Placebo Response in a Clinical Trial of Major Depression

Major depressive disorder (MDD) is a heterogeneous neuropsychiatric disorder with highly variable antidepressant outcomes. In randomized controlled tr...