Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.

Purpose: To critically examine the current state of machine learning (ML) models including patient-reported outcome measure (PROM) scores in cancer research, by investigating the reporting quality of currently available studies and proposing areas of improvement for future use of ML in the field.

Methods: PubMed and Web of Science were systematically searched for publications of studies on patients with cancer applying ML models with PROM scores as either predictors or outcomes. The reporting quality of applied ML models was assessed utilizing an adapted version of the MI-CLAIM (Minimum Information about CLinical Artificial Intelligence Modelling) checklist.

Impact of repeated morning bright white light exposures on attention in a simulated office environment.

Attention is essential to the work. This study investigated the effects of two different light pulses on a simple attention task. In addition, the effects of subsequent exposure to constant but different illuminance levels on the continuation of the simple attention task and a subsequent complex attention task were examined.