Learning from data with structured missingness

Missing data are an unavoidable complication in many machine learning tasks. When data are ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such ‘structured missingness’ raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here we outline the current literature and propose a set of grand challenges in learning from data with structured missingness

TELO-SCOPE study: a randomised, double-blind, placebo-controlled, phase 2 trial of danazol for short telomere related pulmonary fibrosis

Introduction: Recent discoveries have identified shortened telomeres and related mutations in people with pulmonary fibrosis (PF). There is evidence to suggest that androgens, including danazol, may be effective in lengthening telomeres in peripheral blood cells. This study aims to assess the safety and efficacy of danazol in adults and children with PF associated with telomere shortening. Methods and analysis: A multi- centre, double- blind, placebo- controlled, randomised trial of danazol will be conducted in subjects aged \u3e5 years with PF associated with age- adjusted telomere length ≤10th centile measured by flow fluorescence in situ hybridisation; or in children, a diagnosis of dyskeratosis congenita. Adult participants will receive danazol 800 mg daily in two divided doses or identical placebo capsules orally for 12 months, in addition to standard of care (including pirfenidone or nintedanib). Paediatric participants will receive danazol 2 mg/kg/day orally in two divided doses or identical placebo for 6 months. If no side effects are encountered, the dose will be escalated to 4 mg/kg/day (maximum 800 mg daily) orally in two divided doses for a further 6 months. The primary outcome is change in absolute telomere length in base pairs, measured using the telomere shortest length assay (TeSLA), at 12 months in the intention to treat population. Ethics and dissemination: Ethics approval has been granted in Australia by the Metro South Human Research Ethics Committee (HREC/2020/QMS/66385). The study will be conducted and reported according to Standard Protocol Items: Recommendations for Interventional Trials guidelines. Results will be published in peer- reviewed journals and presented at international and national conferences. Trial registration numbers: NCT04638517; Australian New Zealand Clinical Trials Registry (ACTRN12620001363976p)

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings