Publications by authors named "M Schrempf"
Introduction: Suspected appendicitis is the most common indication for non-obstetric surgery during pregnancy. Diagnosis and management of these patients can be challenging. Atypical clinical presentation has been described before, but the current literature consists mostly of small case series.
View Article and Find Full Text PDFArticle Synopsis
- - The study focuses on developing and validating machine learning models to predict major adverse cardiovascular events (MACE) by evaluating their reliability and interpretability across different populations, utilizing data from Brazil and the USA.
- - Eight machine learning algorithms were trained using a balanced dataset and assessed for their predictive performance based on accuracy and ROC curve metrics, with emphasis on Random Forest, which outperformed the others in both internal and external validations.
- - Findings indicate that while Random Forest was the most effective model, Shapley values offered more consistent insights for understanding feature importance compared to LIME during exploratory analyses.
Objective: Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium.
View Article and Find Full Text PDFIntroduction: Endometriosis is a common condition affecting 5 to 10% of women of childbearing age. The true incidence of endometriosis of the appendix is currently unknown. Since symptoms often overlap with those of acute appendicitis, endometriosis of the appendix presents a diagnostic challenge in the emergency department.
View Article and Find Full Text PDFMachine Learning-Based Prediction of Malnutrition in Surgical In-Patients: A Validation Pilot Study.
Stud Health Technol Inform
April 2024
Background: Malnutrition in hospitalised patients can lead to serious complications, worse patient outcomes and longer hospital stays. State-of-the-art screening methods rely on scores, which need additional manual assessments causing higher workload.
Objectives: The aim of this prospective study was to validate a machine learning (ML)-based approach for an automated prediction of malnutrition in hospitalised patients.