Intensivists

Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity Management.

BackgroundWhile machine learning (ML) models are increasingly used to predict outcomes in health care, their practical effect on health care operations, such as bed capacity management, remains underexplored. There is a variety of traditionally used evaluation metrics to analyze ML models; however, decision makers in health care settings require a deeper understanding of their implications for res...

Acute kidney injury prediction and prognostication using machine learning.

Acute Kidney Injury (AKI) is a major health concern with high costs and poor outcomes, partly due to late diagnosis. This paper reviews the application of artificial intelligence (AI) and machine learning (ML) to overcome these limitations by enabling earlier and more accurate prediction and prognostication of AKI. Many studies on AI and ML models used to predict and prognosticate AKI were include...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes.

Self-healing polyurethanes (PUs) exhibit an inherent trade-off between mechanical strength and self-healing efficiency. Although optimizing the feed r...

Widely targeted metabolomics and machine learning identify succinate as a key metabolite in sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive im...

Progress in constitutive modeling of arterial wall tissue mechanics: from theoretical frameworks to clinical application.

Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and the mechanical behavior of arterial wall tissue (AWT) is central to its in...

Lipidomics-driven insights into acupuncture mechanisms: From dynamic signaling networks to multi-dimensional regulations.

This review summarizes lipidomics as a key approach to dissect the multi-dimensional regulatory mechanisms of acupuncture, and reveals the role of lip...

Multi-view deep learning for mandibular landmark localization.

OBJECTIVES: Accurate localization of anatomical landmarks on the mandible is crucial for maxillofacial surgery and orthodontic treatment planning. Thi...

Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients.

OBJECTIVE: To develop and validate a machine learning model for postoperative sepsis in critically ill traumatic spinal injury (TSI) patients, a frequ...

Pharmacoepidemiologic characterization of cannabis use and symptomatology in rheumatology using natural language processing of electronic health record clinic notes.

Up to 70% of patients with autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus...

Artificial Intelligence and Machine Learning in Health Care: Current Capabilities, Emerging Potential, and Future Directions.

This article reviews how artificial intelligence and machine learning are reshaping health care. After a historical overview and glossary, we explore ...

Molecular crystal memristor-based edge AI platform for energy-efficient and real-time smart grid inspection.

Vast power grid infrastructure generates enormous volumes of inspection data from smart meters, unmanned aerial vehicle (UAV) patrols, and high-defini...

Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery.

OBJECTIVE: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. DES...

Pressure injury surveillance in the intensive care unit: Development, validation, and clinical application of a natural language processing algorithm.

INTRODUCTION: Pressure injuries (PIs) are one of the most common hospital-acquired complications in patients admitted to an intensive care unit (ICU)....

Development and validation of deep continual learning model to sequentially learn multiple clinical prediction tasks for ICU patients.

BACKGROUND: ICU patients often suffer from critical and complex condition, and multiple potential risks should be monitored to provide them comprehens...

Artificial intelligence-guided nutritional therapy in the ICU.

PURPOSE OF REVIEW: Critical care nutrition remains a high-stakes and error-prone domain, particularly given the complex metabolic demands and heteroge...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Authors reply: "How do experts classify sepsis cases for sepsis surveillance? Lessons learned from a Behavioural Artificial Intelligence Technology (BAIT) approach".

The Ideal Human Care in Green ICU: An integrated AI framework for future ICU care - Letter on Arabfard et al.

An explainable machine learning model for comorbidity risk stratification in patients with fractures admitted to the intensive care unit: a multicenter study.

BACKGROUND: Among traumatic-fracture patients admitted to intensive care units (ICUs), those with substantial chronic comorbidities recover more slowl...