Hospital-Based Medicine

Clinicosocial determinants of hospital stay following cervical decompression: A public healthcare perspective and machine learning model.

OBJECTIVE: Post-operative length of hospital stay (LOS) is a valuable measure for monitoring quality of care provision, patient recovery, and guiding hospital resource management. But the impact of patient ethnicity, socio-economic deprivation as measured by the indices of multiple deprivation (IMD), and pre-existing health conditions on LOS post-anterior cervical decompression and fusion (ACDF) i...

Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).

The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both bas...

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

INTRODUCTION: Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression sympto...

Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain.

Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine l...

Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate.

We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model tha...

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of th...

An Ethically Supported Framework for Determining Patient Notification and Informed Consent Practices When Using Artificial Intelligence in Health Care.

Artificial intelligence (AI) is increasingly being used in health care. Without an ethically supportable, standard approach to knowing when patients s...

A new machine learning model to predict the prognosis of cardiogenic brain infarction.

Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis ...

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was des...

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machi...

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 instituti...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machi...

Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits.

The increasing prevalence of overcrowding in Emergency Departments (EDs) threatens the effective delivery of urgent healthcare. Mitigation strategies ...

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.

This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific deliver...

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clin...

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in car...

Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.

AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostica...

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this ...

Interpretable machine learning predicts postpartum hemorrhage at time of admission.

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing.

 Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP'...