Siri Lise van der Meijden

Navigating fairness in artificial intelligence-based prediction models: theoretical constructs and practical applications

Siri Lise van der Meijden, Yuqing Wang, Madelena Y. Ng, M. Sesmu Arbous et autres

Artificial intelligence (AI)-based prediction models, including risk scoring systems and decision support systems, are being increasingly adopted in health care. Addressing AI fairness is essential to fighting health disparities and ensuring equitable model performance and patient outcomes. However, numerous and conflicting definitions …

nl, us (code pays fourni par la source)

Interpretable machine learning for identifying ICU readmission risk in subgroups with probabilistic rules

Lincen Yang, Siri Lise van der Meijden, M. Sesmu Arbous, Matthijs van Leeuwen

OBJECTIVE: Estimating readmission risk for intensive care unit (ICU) patients is critical for clinicians to optimize resource allocation and prevent premature discharges. Machine learning models currently applied to this task either lack interpretability or cannot identify patient subgroups with distinctive readmission risks …

nl, Afrique du Sud (code pays fourni par la source)

C-reactive protein in the first 30 postoperative days and its discriminative value as a marker for postoperative infections, a multicentre cohort study

Anna M van Boekel, Siri Lise van der Meijden, Bart F. Geerts, Harry van Goor et autres

OBJECTIVE: To assess the association of C-reactive protein (CRP) with postoperative infections for eight different types of surgery using big data. DESIGN: A multicentre cohort study with longitudinally collected data from electronic health records, collected from 1 January 2011 to 22 September …

nl, Afrique du Sud (code pays fourni par la source)

Navigating Fairness in AI-based Prediction Models: Theoretical Constructs and Practical Applications

Siri Lise van der Meijden, Yuqing Wang, M. Sesmu Arbous, Bart F. Geerts et autres

Artificial Intelligence (AI)-based prediction models, including risk scoring systems and decision support systems, are increasingly adopted in healthcare. Addressing AI fairness is essential to fighting health disparities and achieving equitable performance and patient outcomes. Numerous and conflicting definitions of fairness complicate this …

nl, us (code pays fourni par la source)

Systematic evaluation of machine learning models for postoperative surgical site infection prediction

Anna M van Boekel, Siri Lise van der Meijden, M. Sesmu Arbous, Rob G. H. H. Nelissen et autres

BACKGROUND: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools. OBJECTIVE: The aim …

nl (code pays fourni par la source)

Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data

Siri Lise van der Meijden, Anna M van Boekel, Laurens Schinkelshoek, Harry van Goor et autres

Background: Postoperative infections significantly impact patient outcomes and costs, exacerbated by late diagnoses, yet early reliable predictors are scarce. Existing artificial intelligence (AI) models for postoperative infection prediction often lack external validation or perform poorly in local settings when validated. We aimed …

nl, Afrique du Sud, be (code pays fourni par la source)

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review

Siri Lise van der Meijden, Anna M van Boekel, Harry van Goor, Rob G. H. H. Nelissen et autres

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. …

nl, us (code pays fourni par la source)

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review (Preprint)

Siri Lise van der Meijden, Anna M van Boekel, Harry van Goor, Rob G. H. H. Nelissen et autres

BACKGROUND Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. …

Possibilities and challenges for artificial intelligence and machine learning in perioperative care

Siri Lise van der Meijden, M. Sesmu Arbous, Bart F. Geerts

nl (code pays fourni par la source)

Identifying and Predicting Postoperative Infections Based on Readily Available Electronic Health Record Data

Siri Lise van der Meijden, Anna M van Boekel, Laurens Schinkelshoek, Harry van Goor et autres

Identification of postoperative infections based on retrospective patient data is currently done using manual chart review. We used a validated, automated labelling method based on registrations and treatments to develop a high-quality prediction model (AUC 0.81) for postoperative infections.

nl (code pays fourni par la source)

537. First Week Post-Operative C-Reactive Protein Kinetics Show Different Patterns of Association with Infection Depending on the Type of Surgery

Anna M van Boekel, Siri Lise van der Meijden, Bart F. Geerts, M. Sesmu Arbous et autres

Abstract Background C-reactive protein (CRP) is a nonspecific marker of inflammation and due to surgery alone CRP levels are increased post-operatively. Although multiple studies investigated the potential of post-operative CRP levels to predict post-operative infection, its discriminative capacity remained unclear. We aimed …

nl (code pays fourni par la source)

Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study

Siri Lise van der Meijden, Anne de Hond, Patrick Thoral, Ewout W. Steyerberg et autres

BACKGROUND: Artificial intelligence-based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools. OBJECTIVE: We aimed to investigate physicians' perspectives and their current …

nl (code pays fourni par la source)