Hospitalists

Predicting Return Home After Moderate-to-Severe Traumatic Brain Injury.

BACKGROUND AND OBJECTIVES: Days alive and at home (DAH) is a validated outcome measure that captures health care transitions between time spent at home vs various nonhome care settings, offering a more nuanced patient-centered understanding of recovery. We aimed to (1) characterize long-term recovery trajectories for adults with moderate-to-severe traumatic brain injury (msTBI) using DAH and (2) d...

Seizure forecasting with multiple timescales and features.

OBJECTIVE: Forecasting epileptic seizures is a difficult task. Studies of seizure prediction have investigated many different EEG features, but none of them have been useful enough to be applied in clinical practice beyond trials. Moreover, most of these features have been applied to short-term intracranial EEG (iEEG) recordings, limiting the possibility of reliable statistical evaluation. This pa...

In-hospital outcomes of intravascular lithotripsy compared to rotational atherectomy and cutting/scoring balloon angioplasty.

BACKGROUND: Intravascular lithotripsy (IVL) emerged for the treatment of coronary artery calcification with encouraging safety and effectiveness rates...

An integrative clinical and bioinformatic analysis identifies MicroRNAs as biomarkers of ischemic stroke severity.

Identifying reliable circulating biomarkers is crucial for improving the diagnosis and risk stratification of patients with ischemic stroke. In this s...

IKDP: Implicit Knowledge Enhanced Disease Prediction via heterogeneous admission sequence graphs.

Despite significant advances in deep learning for electronic health record (EHR) modeling, accurately representing complex disease relationships and a...

Derivation and validation of a clinical prediction score for ICU utilization at trauma intake.

BACKGROUND: Delayed admission to the intensive care unit (ICU) after trauma can lead to tripling of in-hospital mortality. Accurate ICU resource predi...

Performance of Machine Learning Models in Predicting Outcomes After ACDF: A Systematic Review and Meta-Analysis of 443 000 Patients.

Study DesignSystematic review and meta-analysis.ObjectiveDirect head-to-head comparison of machine learning models aiming to predict outcomes in Anter...

Identifying Key Predictors of Appropriate Discharge Destinations for Older Inpatients in Acute Care: Scoping Review.

BACKGROUND: Postacute care (PAC) services are important to ensure functional recovery and provide adequate care for geriatric inpatients in acute care...

The efficiency paradox of discharge masking head loss in run-of-river hydropower generation.

While climate impacts on hydropower output are well-documented, plant efficiency, the critical ratio of electrical energy generated to hydraulic energ...

Dual Ontology-enhanced Clinical Decision Learning for First-admission Mortality Prediction.

Time-series based deep learning methods have significantly improved performance of predictive healthcare tasks on electronic health records (EHR) data...

Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly wh...

A Study on the Effectiveness of Al-Assisted Patient Health Education Using Voice Cloning and ChatGPT: A Prospective Randomized Controlled Trial.

BACKGROUND: Traditional patient education often lacks personalization and engagement, potentially limiting knowledge acquisition and treatment adheren...

Development of a machine learning model to predict intensive care unit bed demand for adult elective surgical patients at a large United Kingdom National Health Service Trust.

BACKGROUND: Elective surgical admissions form a growing share of demand for ICU beds, a constrained resource. Capacity planning for these admissions i...

Lactate to albumin ratio predicts one year mortality in ICU patients receiving invasive mechanical ventilation.

The lactate-to-albumin ratio (LAR), a composite biomarker reflecting both inflammatory burden and nutritional status, has been associated with adverse...

A Deep Learning Model for Dynamic Prediction of Acute Kidney Injury in Heart Failure Patientss.

BACKGROUND: This study aimed to develop and validate a dynamic prediction model for acute kidney injury (AKI) in heart failure (HF) patients. METHODS:...

Optimizing wastewater treatment through combined deep learning and deep reinforcement learning: Recent advances and future prospects.

Wastewater treatment plants (WWTPs) are critical components of urban infrastructure, and enhancing their performance while reducing carbon emissions i...

Dynamic Ensemble Selection for Early Detection of Deep Vein Thrombosis in Fracture Patients.

Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machi...

ChatGPT-guided translation of pediatric emergency department discharge instructions.

BACKGROUND: Language barriers in pediatric emergency medicine discharge instructions can impact patient safety, leading to poorer post-discharge outco...

Development of a new tool for prediction of hospital length of stay and intensive care needs in trauma patients using Machine Learning.

BACKGROUND: Trauma is a major global health burden leading to significant morbidity, disability, and mortality. Predictive models in trauma care tradi...

Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.

BACKGROUND AND OBJECTIVES: Identifying surgical candidates who are prone to poor outcomes is crucial for adapting treatment and ensuring optimal outco...