Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.

Background: Radiotherapy is the mainstay of treatment for head and neck cancer (HNC) but may induce various side effects on surrounding normal tissues. To reach an optimal balance between tumour control and toxicity prevention, normal tissue complication probability (NTCP) models have been reported to predict the risk of radiation-induced side effects in patients with HNC. However, the quality of study design, conduct, and analysis (i.

Plug-and-play use of tree-based methods: consequences for clinical prediction modeling.

Objectives: Tree-based models, such as random forest and XGBoost, are increasingly being used for clinical prediction, but certain aspects of their behavior are often overlooked. This article aims to illustrate these aspects and discuss the implications of plug-and-play use of tree-based models for clinical prediction. We focus on their ability to learn smooth, monotonic (ie, consistent predictor effect where an increase in predictor leads to an increase in predicted risk), and additive predictor-outcome associations (ie, each predictor independently and additively contributes to the outcome) and how they behave when making predictions outside the range of observed data (extrapolation).

Updating methods for artificial intelligence-based clinical prediction models: a scoping review.

Objectives: To give an overview of methods for updating artificial intelligence (AI)-based clinical prediction models based on new data.

Study Design And Setting: We comprehensively searched Scopus and Embase up to August 2022 for articles that addressed developments, descriptions, or evaluations of prediction model updating methods. We specifically focused on articles in the medical domain involving AI-based prediction models that were updated based on new data, excluding regression-based updating methods as these have been extensively discussed elsewhere.

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.

Importance: The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow.

Objectives: To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle.

Modest performance of text mining to extract health outcomes may be almost sufficient for high-quality prognostic model development.

Background: Across medicine, prognostic models are used to estimate patient risk of certain future health outcomes (e.g., cardiovascular or mortality risk).

Cross-institution natural language processing for reliable clinical association studies: a methodological exploration.

Objectives: Natural language processing (NLP) of clinical notes in electronic medical records is increasingly used to extract otherwise sparsely available patient characteristics, to assess their association with relevant health outcomes. Manual data curation is resource intensive and NLP methods make these studies more feasible. However, the methodology of using NLP methods reliably in clinical research is understudied.

Predicting sepsis-related mortality and ICU admissions from telephone triage information of patients presenting to out-of-hours GP cooperatives with acute infections: A cohort study of linked routine care databases.

Background: General practitioners (GPs) often assess patients with acute infections. It is challenging for GPs to recognize patients needing immediate hospital referral for sepsis while avoiding unnecessary referrals. This study aimed to predict adverse sepsis-related outcomes from telephone triage information of patients presenting to out-of-hours GP cooperatives.

Dynamics in cardiac surgery: trends in population characteristics and the performance of the EuroSCORE II over time.

Objectives: The aim of this study was to investigate the performance of the EuroSCORE II over time and dynamics in values of predictors included in the model.

Methods: A cohort study was performed using data from the Netherlands Heart Registration. All cardiothoracic surgical procedures performed between 1 January 2013 and 31 December 2019 were included for analysis.

The relation between prediction model performance measures and patient selection outcomes for proton therapy in head and neck cancer.

Background: Normal-tissue complication probability (NTCP) models predict complication risk in patients receiving radiotherapy, considering radiation dose to healthy tissues, and are used to select patients for proton therapy, based on their expected reduction in risk after proton therapy versus photon radiotherapy (ΔNTCP). Recommended model evaluation measures include area under the receiver operating characteristic curve (AUC), overall calibration (CITL), and calibration slope (CS), whose precise relation to patient selection is still unclear. We investigated how each measure relates to patient selection outcomes.

Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods.

Background: Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate.

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review.

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy.