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Length of Stay

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Efficacy of Pulse Methylprednisolone in Treatment of Acute Respiratory Distress Syndrome due to Malaria: A Randomized Controlled Clinical Trial.

The Journal of the Association of Physicians of India
: To study the efficacy of pulse methylprednisolone (MPS) therapy in patients with malaria-associated acute respiratory distress syndrome (ARDS). : The study was a randomized, single-blind, placebo-controlled trial with a total sample size of 44 pati...

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

PeerJ
BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the ...

Appropriate use of blood cultures in the emergency department through machine learning (ABC): study protocol for a randomised controlled non-inferiority trial.

BMJ open
INTRODUCTION: The liberal use of blood cultures in emergency departments (EDs) leads to low yields and high numbers of false-positive results. False-positive, contaminated cultures are associated with prolonged hospital stays, increased antibiotic us...

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

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
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 mea...

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BMC musculoskeletal disorders
BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to ...

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

Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and du...

Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations.

Surgery
BACKGROUND: With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their applic...

Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis.

Spine deformity
PURPOSE: Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery.

An artificial intelligence-designed predictive calculator of conversion from minimally invasive to open colectomy in colon cancer.

Updates in surgery
Minimally invasive surgery is safe and effective in colorectal cancer. Conversion to open surgery may be associated with adverse effects on treatment outcomes. This study aimed to assess risk factors of conversion from minimally invasive to open cole...

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

Computer assisted surgery (Abingdon, England)
BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study ...