Hospitalists

Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographical disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating...

Insights into suspended sediment and microplastic budget of a lowland river: integrating in-situ measurements, Sentinel-2 imagery, and machine learning.

The calculations on fluvial microplastic load (MPL) provide dynamic and actionable metrics for understanding microplastic (MP) particle emissions to the downstream environment. However, most of the concentration data reported in the literature do not reflect the total amount of transported MPs. This study aims to quantify the MPLs in the Middle and Lower Tisza River, Hungary by combining multiscal...

DeVisE: Behavioral Testing of Medical Large Language Models

Large language models (LLMs) are increasingly used in clinical decision support, yet current evaluation methods often fail to distinguish genuine me...

A deep learning model could screen for coronary heart disease from a "pseudo-normal" electrocardiogram.

BACKGROUND: This study aimed to develop a deep learning model (DLM) for rapid screening of coronary heart disease (CHD) using "pseudo-normal" electroc...

Beyond RAG: Reinforced Reasoning Augmented Generation for Clinical Notes

Clinical note generation aims to automatically produce free-text summaries of a patient's condition and diagnostic process, with discharge instructi...

Cluster discharge resonance neuron model and its application in machinery multi-dimensional fault vibration signals.

Through the analysis of multidimensional vibration signals of machinery, existing faults in mechanical equipment can be timely identified to ensure no...

A transformer-based framework for temporal health event prediction with graph-enhanced representations.

OBJECTIVE: Deep learning approaches have demonstrated significant potential in predicting temporal health events in recent years. However, existing me...

A multi-objective optimization model integrating machine learning and time-frequency analysis for supporting nitrogen and phosphorus pollution reduction in Guangzhou city, China.

The unbridled discharge of nitrogen and phosphorus (NP) pollutants is believed to have surpassed ecosystem resilience limits for many regions, which i...

Neuronal Waveform Classification in Multielectrode Recordings Using Machine Learning Techniques and Multidimensional Analysis.

Extracellular recordings of neuronal spikes are crucial for studying brain activity. These signals are typically classified based on firing patterns a...

Applying machine learning with MobileNetV2 model for rapid screening of vaginal discharge samples in vaginitis diagnosis.

Vaginitis is a prevalent gynecological condition that impacts women's quality of life, with most women likely to experience it at least once. Traditio...

Interpretable phenotyping of Heart Failure patients with Dutch discharge letters

Objective: Heart failure (HF) patients present with diverse phenotypes affecting treatment and prognosis. This study evaluates models for phenotypin...

ER-REASON: A Benchmark Dataset for LLM-Based Clinical Reasoning in the Emergency Room

Large language models (LLMs) have been extensively evaluated on medical question answering tasks based on licensing exams. However, real-world evalu...

Prediction of coronary heart disease based on klotho levels using machine learning.

The diagnostic accuracy for coronary heart disease (CHD) needs to be improved. Some studies have indicated that klotho protein levels upon admission c...

Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions.

In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress...

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patie...

Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks

Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction...

Early Diagnosis of Atrial Fibrillation Recurrence: A Large Tabular Model Approach with Structured and Unstructured Clinical Data

BACKGROUND: Atrial fibrillation (AF), the most common arrhythmia, is linked to high morbidity and mortality. In a fast-evolving AF rhythm control tr...

Predicting 1-Year Survival Using Machine Learning in Very Old Patients Before ICU Admission.

Discussions about the benefits of admitting very old individuals to intensive care unit (ICU) remain challenging. We hypothesized that data-driven alg...

Detecting Adverse Drug Events in Clinical Notes Using Large Language Models.

Monitoring adverse drug events (ADEs) is critical for pharmacovigilance and patient safety. However, identifying ADEs remains challenging, as suspecte...

Bridging AI and Medical Expertise: ChatGPT's Success on the Medical Specialization Residency Admission Exam in Spain.

The growing use of Artificial Intelligence (AI) in healthcare, particularly focusing on the potential of generative AI models like ChatGPT-4 is a tren...