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
Derive insights from health data using knowledge graph technologies
This article describes how
knowledge graph technologies can help with health data science, particularly on free-text electronic health records.
Why bother using free-text clinical notes for research or patient care?
Alright, in general, we know a significant proportion of the world’s data is in an unstructured format like news articles, Tweets and blogs. Some say 80% of our data is unstructured, while others estimate even more.