AIMC Topic: Retrospective Studies

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Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study.

Scientific reports
Perioperative stroke significantly impacts postoperative outcomes. Current risk stratification methods for perioperative stroke prediction lack accuracy and practicality. We aimed to develop a machine learning (ML) model that improves both accuracy a...

Prognostic value of combining nutritional inflammatory index trajectories and tumor characteristics in cervical cancer.

BMC women's health
OBJECTIVE: This investigation seeks to examine how varying longitudinal patterns in nutritional inflammatory index (NII) correlate with clinical outcomes in cervical cancer patients, while developing predictive models for prognosis.

Machine learning-based management of hypertensive disorders in pregnancy: analysis of differences in key risk factors between gestational hypertension and pre-eclampsia and construction of a pre-eclampsia prediction model.

European journal of medical research
OBJECTIVES: It remains debated whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectra of the same disease. Currently, comparative studies of risk factors for GH and PE in the same population are limite...

Impact of COVID-19 isolation measures on ICU microbial resistance dynamics: simulation-based statistical modeling analysis.

Antimicrobial resistance and infection control
BACKGROUND: The transmission of antibiotic-resistant bacteria in intensive care units (ICUs) poses a significant challenge to infection control and patient safety. While direct patient-to-patient transmission is well documented, the relative contribu...

XGBoost-based analysis of maternal and biochemical factors associated with spontaneous preterm birth: a retrospective cohort study.

BMC pregnancy and childbirth
BACKGROUND: Spontaneous preterm birth (sPTB) remains a major cause of neonatal morbidity and early risk assessment was poor. This study aimed to evaluate the association and predictive potential of serum biomarkers and maternal factors with sPTB.

Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients.

Scientific reports
Liver cancer has a high incidence and mortality rate globally, particularly in patients requiring intensive care unit (ICU) admission. Early prediction of in-hospital mortality for these patients is crucial, yet lacking reliable tools. This study aim...

Pilot case control evaluation of artificial intelligence assisted orthodontic monitoring and pediatric patient perception.

Scientific reports
Artificial Intelligence (AI) has become a key tool in the modernization of the healthcare industry, aiding dentists in performing their work more efficiently and effectively. The aim of this study was to evaluate orthodontic monitoring and patient pe...

MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.

Scientific reports
Pediatric viral encephalitis is an acute central nervous system infection caused by various viruses, with diverse clinical manifestations and challenges in early diagnosis. The traditional diagnostic methods lack sufficient sensitivity and specificit...

Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.

Scientific reports
About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE...

AI-driven pre-screening for colorectal cancer using complete blood counts: toward broader population impact.

International journal of colorectal disease
PURPOSE: Early colorectal cancer (CRC) detection is crucial for effective treatment; however, traditional screening methods face challenges. Colonoscopy, though highly effective, has limited availability, and fecal immunochemical tests (FIT) are more...