Hospital-Based Medicine

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 is of great concern to research and governmental communities. In this research, a multi-objective optimization model was developed based on integrating advanced optimization, time-frequency analysis, and machine learning approaches into a general mode...

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the rate of hospital visits and hospitalizations. In this study, patients in a local hospital of third-grade class-A hospital were used as cases. Artificial...

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...

Driving Knowledge to Action: Building a Better Future With Artificial Intelligence-Enabled Multidisciplinary Oncology.

Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and trea...

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases

Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large langua...

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...

Opportunities and challenges in lung cancer care in the era of large language models and vision language models.

Lung cancer remains the leading cause of cancer-related deaths globally. Over the past decade, the development of artificial intelligence (AI) has sig...

Infi-Med: Low-Resource Medical MLLMs with Robust Reasoning Evaluation

Multimodal large language models (MLLMs) have demonstrated promising prospects in healthcare, particularly for addressing complex medical tasks, sup...

Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemble

Despite the growing clinical adoption of large language models (LLMs), current approaches heavily rely on single model architectures. To overcome ri...

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...

Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning

Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system genera...

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...

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial fo...

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...

[Technological innovation and future development of quantitative research on acupuncture manipulation techniques].

The quantitative research on acupuncture manipulation techniques aims to transform traditional empirical operations into measurable parameters through...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study ai...

New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance.

Transitional care may play a vital role in the sustainability of Europe's future healthcare system, offering solutions for relocating patient care fro...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strat...

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...

Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV

Intensive care unit (ICU) is a crucial hospital department that handles life-threatening cases. Nowadays machine learning (ML) is being leveraged in...