Machine learning in asthma research: moving towards a more integrated approach

Expert Rev Respir Med. 2021 Feb 23. doi: 10.1080/17476348.2021.1894133. Online ahead of print.ABSTRACTINTRODUCTION: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favoured the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterised by a shift in the way studies are conducted and data are analysed. Motivated by the assumptions that 'data will speak for themselves', hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterise asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.AREAS COVERED: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.EXPERT OPINION: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalise on the potential of "big data", we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.PM...
Source: Expert Review of Respiratory Medicine - Category: Respiratory Medicine Authors: Source Type: research