2023

• (20 March 2023)

  Workshop paper on "Ontology-driven Self-supervision for Adverse Childhood Experiences Identification using Social Media Datasets" now published!

  Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. In this paper, Jinge and colleages present an ontology-driven self-supervised approach (derive concept embeddings using an auto-encoder from baseline NLP results) for producing a publicly available resource that would support large-scale machine learning (e.g., training transformer based large language models) on social media corpus. Jinge presented this paper in 2022 summer at the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare, which was with IJCAI 2022 in Vienna. Check - Paper, Github Repo

• (7 March 2023) npj Digital Medical’s editorial on automating clinical coding echoes our prospective
  
  Recently, npj Digital Medicine’s editor Dr Kvedar and colleagues have published a great editorial on automating clinical coding (link), pointing out the main challenges including technological and implementation levels; clinical documents are redundant and complex, code sets like the ICD-10 are rapidly evolving, training sets are not comprehensive of codes; capturing the logic and rules of coding decisions. Great to see our prospectives on the automated coding research challenges and future directions were echoed in the editorial!

• (21 February 2023) New paper - The impact of inconsistent human annotations on AI driven clinical decision making now published by npj Digital Medicine
  
  Annotation inconsistencies commonly occur when even highly experienced clinical experts annotate the same phenomenon (e.g., medical image, diagnostics, or prognostic status), due to inherent expert bias, judgements, and slips, among other factors. While their existence is relatively well-known, the implications of such inconsistencies are largely understudied in real-world settings.
  
  Aneeta Sylolypavan did her MSc with us addressing this hugely important research question using real-world ICU datasets with annotated data from 11 ICU consultants. The results suggest that (a) there may not always be a “super expert” in acute clinical settings; and (b) standard consensus seeking (such as majority vote) consistently leads to suboptimal models. Further analysis, however, suggests that assessing annotation learnability and using only ‘learnable’ annotated datasets for determining consensus achieves optimal models in most cases.
  
  Read the paper from here

• (23 January 2023)   KnowLab is funded by NIHR/HDR UK to use health data science + machine learning for addressing the NHS winter pressure - Using rare disease phenotype models to identify people at risk of COVID-19 related adverse outcome
  
  KnowLab is proud to be part of 16 projects funded by HDR UK and funded by NIHR which will use data-driven approaches to pin-point pressures in the health care system, understand their causes and develop ways to overcome or avoid them. Particularly, we will use machine learning and rare disease phenotype models to uncover much-needed information on the added risks of severe COVID-19 in people who are clinically more vulnerable and come from disadvantaged socioeconomic backgrounds. This can then inform policy responses to provide better management and treatment for these most vulnerable groups who might have been overlooked. The team are well placed to derive quick actionable findings for the winter pressures as they have been working with CVD-COVID-UK/COVID-IMPACT on rare diseases since October 2021.
  
  HDR UK Press Release on the funded projects | HDR UK News on this project | Herald Scotland News

2022

• (21 December 2022) Our UK’s clinical NLP landscaping (a survey) paper is now published with npj Digital Medicine
  
  Aiming to survey the landscape of Clinical NLP in the UK, we used a relatively extraordinary approach - start with finding all relevant funded projects and extract their interlinked information. Then, conducted community analysis and literature review. We described WHO (key players of funders, universities, companies, researchers), WHAT (techs, applications, disease areas, clinical questions, datasets), WHERE (the community developments, tech trends & maturity), GAPS (barriers to unleash the full power of NLP in health). While on the community level we focused on the UK, analyses and discussions on the research, tech and developments were beyond the country boundary. In particular, we compared tech, data, regulatory similarities and differences of the US and the UK. This is one of the key outputs of HDR UK funded National Text Analytics Project.
  
  Read it at DOI:10.1038/s41746-022-00730-6

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