Mr James Kinross

Speaker Biography

James Kinross is a Senior Lecturer in Colorectal Surgery and a Consultant Surgeon at Imperial College London. James is the PI on the PanSurg project (www.pansurg.org), which delivered education and primary research on the implications of Covid19 on surgical services. As part of this work, his team are building a secure continually updating web-source processing pipeline for REaltime DAta Synthesis and Analysis (REDASA) of Scientific Literature on COVID-19 in collaboration with Amazon Web Services. The objective is to dynamically assimilate and synthesise the corpus of knowledge on COVID-19 from traditional academic literature, websites and social media for real time clinical use. His clinical interest is in minimally invasive surgery for colorectal cancer. He was trained in Northwest London, and he was an NIHR Clinical Lecturer in Surgery and an Ethicon Laparoscopic Fellow in Colorectal Surgery. He was awarded a Royal College of Surgeons of England training fellowship during his PhD on the gut microbiome and he was funded by the Academy of Medical Sciences as an early stage lecturer. He is a visiting Professor at the Royal College of Surgeons of Ireland. He performs translational research into computational and systems biology in surgery and the gut microbiome. He is also funded by the NIHR to perform intra-operative mass spectrometry (known as Real-time Electrospray Ionisation Mass Spectrometry or REIMS) for improving precision in the surgical treatment of colorectal cancer.

 

Talk Abstract

The scale and quality of the global scientific response to COVID-19 has unquestionably saved lives. However, COVID-19 has also triggered an unprecedented “infodemic”; the velocity and volume of data production has overwhelmed many key stakeholders such as clinicians and policy makers who have been unable to process structured and unstructured data for evidence-based decision making. Current solutions aiming to alleviate this multi-disciplinary data synthesis challenge and minimise the deleterious impact of misinformation, are unable to capture heterogeneous web data in “real-time” for the production of contemporaneous answers and are not based on the identification and interpretation of high quality information in response to a freetext query. To realise an infrastructure that can addresses these shortcomings, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a webcrawl methodology. REDASA is now one of the world’s largest and most contemporaneous COVID-19 evidence sources consisting of 104,000 documents and counting. This data pipeline converges with a novel curation methodology that adopts a “human in the loop” methodology for the characterisation of quality, relevance and key evidence across a range of scientific literature sources. By capturing curator’s critical appraisal methodology as discrete labels and rating information, REDASA has rapidly developed a foundational data science dataset representing 10% of the papers written worldwide on COVID-19 in under 2 weeks.  This dataset can act as ground-truth for future implementation of live, automated systematic review.