AIMC Topic: Retrospective Studies

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Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis.

Scientific reports
Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile t...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...

First nomogram for predicting interstitial lung disease and pulmonary arterial hypertension in SLE: a machine learning approach.

Respiratory research
BACKGROUND: Interstitial lung disease (ILD) and pulmonary arterial hypertension (PAH) are severe, life-threatening complications of systemic lupus erythematosus (SLE). Early identification of high-risk patients remains challenging due to the lack of ...

Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study.

BMC palliative care
BACKGROUND: Developing an accurate predictive model for palliative care phases is crucial for improving cancer patient management, enabling healthcare providers to identify those in need of specific care plans and streamlining decision-making process...

Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study.

Journal of orthopaedic surgery and research
OBJECTIVES: To develop and validate an interpretable machine learning model based on clinicoradiological features and radiomic features based on magnetic resonance imaging (MRI) to predict the failure of conservative treatment in lateral epicondyliti...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

BMC pediatrics
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.

Machine learning analysis of factors contributing to hypotension after lumbosacral epidural anaesthesia in dogs undergoing abdominal surgery.

Scientific reports
The incidence of hypotension after a lumbosacral epidural in dogs depends on the volume of local anaesthetic administered. So far, there are no reports comparing both methods used to calculate this volume-body weight (BW) and occipito-coccygeal lengt...

Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.

Scientific reports
In recent years, the incidence of refractory Mycoplasma pneumoniae pneumonia (RMPP) has significantly risen, posing severe pulmonary and extrapulmonary complications, making early identification a challenge for clinicians. In this retrospective singl...

Relationship between medication regimen complexity and pharmacist engagement in fluid stewardship.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
PURPOSE: The medication regimen complexity intensive care unit (MRC-ICU) score has previously been associated with pharmacist workload and fluid overload. The purpose of this study was to determine the relationship of MRC-ICU score with pharmacist-dr...

Radiomics-based machine learning model for diagnosing internal abdominal hernias: a retrospective study.

Scientific reports
Intraperitoneal hernia is an acute abdominal disease, with complex imaging features and variable clinical manifestations that challenge surgeons and emergency physicians in early disease assessment and streamlined diagnosis and treatment procedures. ...