Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background: Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system.

Methods: Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology.

Evaluation of the success of predicted dental changes with clear-aligner treatment: A pilot study.

Objective: This pilot study concerned evaluation of the success of predicted dental changes in patients presenting with Class I malocclusions who were submitted to treatment aligners, using the superimposition.

Methods: The digital models were superimposed and analyzed using 3DSlicer 5.0 software.

Influence of pain duration and severity on oral health-related quality of life and patient satisfaction during adult treatment with clear aligners.

Background: This study aims to compare the impact of pain on quality of life and patient satisfaction during treatment with aligners.

Methods: Ninety-four subjects in active treatment were invited to answer self-reported questions concerning pain severity and duration, occurrence of other signs and symptoms, and level of satisfaction with their treatment. Also, the OHIP-14 questionnaire was applied to assess patients' OHRQoL.

Automated Feature Selection from Medical Literature.

We propose an automated approach to rank the most salient variables related to a certain clinical phenomenon from scientific literature. Our solution is an automated approach to improve the efficiency of the collection of different health-related measures from a population, and to accelerate the discovery of novel associations and dependencies between health-related concepts.