2022

• (2 May 2022) Our work Quantifying Health Inequalities Induced by Data and AI Models has been accepted by IJCAI-ECAI2022 ‘AI for Good track’. This work introduced a generic allocation-deterioration framework for detecting and quantifying AI induced inequality. Extensive experiments were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets of HiRID and MIMIC III; (b) induced by AI models trained for two resource allocation scenarios. The results showed that compared to men, women had up to 33% poorer deterioration in markers of prognosis when admitted to HiRID ICUs. All four AI models assessed were shown to induce significant inequalities (2.45% to 43.2%) for non-White compared to White patients. The models exacerbated data embedded inequalities significantly in 3 out of 8 assessments, one of which was >9 times worse. preprint, slides, recording, repo.

• (26 April 2022) Study led by Isabel Straw, Investigating for bias in healthcare algorithms - a sex-stratified analysis of supervised machine learning models in liver disease prediction, demonstrates a previously unobserved sex disparity present in published machine learning models. It suggests “To ensure sex-based inequalities do not manifest in medical AI, an evaluation of demographic performance disparities must be integrated into model development.” The work has been published on BMJ Health & Care Informatics.

• (22 April 2022) Dr Honghan Wu joined the editorial board of BMC Digital Health. BMC Digital Health considers research on all aspects of the development and implementation of digital technology in both medicine and public health, such as mobile health applications, virtual healthcare and wearable technology, as well as the role of social media and other communications technology in digital health.

• (25 March 2022) Study led by Huayu, Increased COVID-19 mortality rate in rare disease patients - a retrospective cohort study in participants of the Genomics England 100,000 Genomes project, has shown rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. This work has now been accepted by Orphanet Journal of Rare Diseases.

• (20 March 2022) Clinical coding is the task of transforming medical information in a patient’s health records into structured codes like ICD-10 for diagnosis, which is cognitive, time-consuming task and error-prone. In this preprint, titled Automated Clinical Coding - What, Why, and Where We Are? , Hang introduces the idea of automated clinical coding and summarises its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK.

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