Hospital-Based Medicine

Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models

Clinical phenotyping is the process of extracting patient’s observable symptoms and traits to better understand their disease condition. Suicide phenotyping focuses more on behavioral and cognitive characteristics, such as suicide ideation, attempt, and self-injury, to identify suicide risks and improve interventions. In this study, we leveraged the latest reasoning models, namely 4o, o1, and o3-m...

Automated Detection of Faciobrachial Dystonic Seizures Related Events in LGI1 Autoimmune Encephalitis Patients with Wearables

To evaluate the potential of wrist-worn wearable devices to detect and quantify Faciobrachial Dystonic Seizures (FBDS) and related events associated with leucine-rich glioma Inactivated-1 (LGI1)-IgG autoimmune encephalitis (LGI1 AIE). Seven patients and four control subjects were monitored with Empatica E4 wristbands in both hospital and ambulatory environments. The analysis focused on the pre- an...

Pose AI prediction of neurological status in the Neuroscience Intensive Care Unit

The neurological exam is pivotal in assessing patients with neurological conditions but has severe limitations: it can vary between examiners, it may ...

Perceptions and Insights: A Qualitative Assessment of an AI-Assisted Psychiatric Triage System Implemented in an Outpatient Hospital Setting

The Canadian healthcare system is approaching a breaking point. With mental health being a leading cause of disability, innovative solutions are neces...

Concurrent prediction of in-hospital mortality and length of stay using single-task, multi-class, and multi-task machine learning

Accurate predictions of discharge timing and in-hospital mortality could improve hospital efficiency, but clinician estimates are often inconsistent a...

Impact of Iron Deficiency on Clinical Outcomes in Congestive Heart Failure: A Retrospective Analysis of Risk Stratification and Mortality

Iron deficiency frequently coexists with congestive heart failure, thereby increasing morbidity and mortality. Although guidelines typically define ir...

Transport-based transfer learning on Electronic Health Records: Application to detection of treatment disparities

Electronic Health Records (EHRs) sampled from different populations can introduce unwanted bi-ases, limit individual-level data sharing, and make the ...

Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major ...

An AI-driven machine learning approach identifies risk factors associated with 30-day mortality following total aortic arch replacement combined with stent elephant implantation

During emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavorable outcomes throughout their hospital stay. The com...

Unmet Needs in Acute Hepatic Porphyria Diagnosis: A Comparative Big Data Analysis of an AI-based Human-in-the-Loop Screening Versus Standard of Care

Acute Hepatic Porphyria (AHP) is a rare genetic disease characterized by unpredictable life-threatening attacks. There is no reliable biochemical scre...

Predicting 30-Day Mortality and Readmission Using Hospital Discharge Summaries: A Comparative Analysis of Machine Learning Models, Large Language Models, and Physicians

This study evaluates the comparative performance of trained machine learning models, commercial off-the-shelf (COTS) large language models (LLMs), and...

Predictive Modeling of Heart Failure Readmissions

Federal programs to mitigate hospital readmission of patients with heart failure (HF) monetarily encourage hospitals through the use of penalties. The...

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...

Comparative Evaluation of Time Series Forecasting Approaches for Facility-Level Antibiotic Resistance Outcomes in the Veterans Health Administration

Antibiotic resistance is a critical public health threat, particularly in hospital settings where vulnerable populations face heightened risks of infe...

Screening for anemia using multi-modal machine learning models on smartphones: protocol for a comparative accuracy study in rural India

Anemia, or low blood hemoglobin (Hb), affects one third of the world population, and is particularly prevalent in women and children in lower resource...

Limited Echocardiogram Acquisition by Clinicians Aided with Deep Learning: A Randomized Controlled Trial

Deep learning (DL) programs can aid in the acquisition of echocardiograms by medical professionals not previously trained in sonography, potentially a...

The use of Artificial Intelligence in the out of hospital care settings: A Scoping Review

Out of hospital services face significant challenges, including growing patient demand, workforce limitations, and evolving care pathways. Artificial ...

A Bilingual On-premise AI agent for Clinical Drafting: Seamless EHR integration in the Y-KNOT Project

Large Language Models (LLMs) have shown promise in reducing clinical documentation burden, yet their real-world implementation faces significant chall...

Towards Inpatient Discharge Summary Automation via Large Language Models: A Multidimensional Evaluation with a HIPAA-Compliant Instance of GPT-4o and Clinical Expert Assessment

Large language models (LLMs) have demonstrated potential to automate clinical documentation tasks that may reduce clinician burden, such as generation...

Optimized BERT-based NLP outperforms Zero-Shot Methods for Automated Symptom Detection in Clinical Practice

Large Language Nodels (LLMs) have raised broad expectations for clinical use, particularly in the processing of complex medical narratives. However, i...