2025

• (3 February 2025) Paper published on Lancet Digital Health - Prevalence and demographics of 331 rare diseases and associated COVID-19-related mortality among 58 million individuals- a nationwide retrospective observational study - at DOI:10.1016/S2589-7500(24)00253-X
  
  This study of over 58 million people has identified eight rare diseases that carry significantly increased risks for COVID-19-related mortality in fully vaccinated individuals. This important research calls for better inclusion of rare diseases in public health strategies, including future pandemic planning, vaccination policies, and NHS service provision. We uncovered eight rare diseases linked with an increased risk of dying from COVID-19 and found that people with rare diseases were nearly five times more like to die from COVID-19 than the general population. The conditions carrying the highest risk were infertility disorders and kidney diseases.
  
  The rarity of some diseases can contribute to a lack of comprehensive data due to lengthy waits for diagnosis. And as the study period spans two ‘waves’ of the pandemic, it’s difficult to untangle the effects of different COVID-19 variants, as well as overlapping symptoms in multiple illnesses.
  
  However, this study takes advantage of one of our health system’s unique strengths, having population-wide granular data coverage in EHRs and diversity in representation, highlighting signfificant health inequalities faced by specific ethnic groups in which some rare diseases were disproportionately common. It’s also the first time an analysis has been performed on so many rare diseases, and the impact of COVID-19 on the people with such conditions, a landmark moment for rare disease research.
  
  Living with a rare disease can be extremely challenging, from a difficult diagnosis to relentless appointments to manage health and wellbeing. But these findings fill a critical knowledge gap, emphasising the power of electronic health record data to bring to light the prevalence and outcomes of rare diseases.
  
  Read it at DOI:10.1016/S2589-7500(24)00253-X.

• (22 January 2025) Paper published on International Journal of Medical Informatics - Deep Learning Based Prediction of Depression and Anxiety in Patients with Type 2 Diabetes Mellitus Using Regional Electronic Health Records - at DOI:10.1016/j.ijmedinf.2025.105801
  
  The study aimed to develop the Regional EHR for Depression and Anxiety Prediction Model (REDAPM), which integrates both structured and unstructured electronic health record (EHR) data to predict the onset of depression and anxiety among individuals with Type 2 Diabetes Mellitus (T2DM). REDAPM showed superior performance over baseline models, achieving high ROC-AUC and PR-AUC scores in both internal and external validation datasets, indicating its effectiveness in predicting mental health conditions. The REDAPM’s ability to leverage regional EHR data and its demonstrated clinical utility positions it as a promising tool for healthcare professionals and informaticians, offering a novel contribution to the field of medical informatics, especially in predicting mental health within the diabetic population.
  
  Read it at DOI:10.1016/j.ijmedinf.2025.105801.

2024

• (19 December 2024) Paper published on npj Digital Medicine - Optimising the paradigms of human AI collaborative clinical coding - at DOI:10.1038/s41746-024-01363-7
  
  Clinical coding is the process of assigning standardised codes (eg hashtag#ICD-10 for diagnosis or procedures) for an interaction with the health service (a visit to GP or a hospital stay). Such ‘coded’ information is widely used for patient care, auditing and research. However, currently this is mainly a manual process done by clinical coders, which is expensive, time-consuming, and error-prone (quantified in the paper). Yue Gao, a visiting PhD student at KnowLab during 2023-2024, developed a deep-learning based human-AI collaborative paradigm to facilitate the clinical coding process. The system, called CliniCoCo, was developed using real-world hashtag#EHR datasets from two hospitals in China and further evaluated by human coders in a third Chinese hospital. CliniCoCo was able to reduce coding time by 40% and enable professional coders achieving a F1 score more than 0.93.
  
  Read it at DOI:10.1038/s41746-024-01363-7.

• (28 October 2024) Invited talk at KnowLab - The world of non-clinical safety, artificial intelligence, and foundation models
  
  Speaker - Dr Arijit Patra
  
  Title - The world of non-clinical safety, artificial intelligence, and foundation models
  
  Abstract - There has been a significant amount of interest in artificial intelligence in the pharmaceutical industry, particularly with respect to drug discovery. While drug discovery has been established as a domain where algorithmic involvement can create substantial efficiency gains, other aspects of the pharmaceutical development process such as non-clinical and clinical safety are of critical importance towards creating patient value. This talk will explore the drug development process, the concepts of non-clinical safety with a particular focus on toxicologic pathology, and recent insights on building ML pipelines and foundation models therein.
  
  Bio - Dr Arijit Patra is a Senior Principal Scientist at UCB Biopharma UK. He holds a PhD in machine learning for healthcare imaging from the University of Oxford, where he was a Rhodes Scholar (India & Exeter, 2016). Prior to that, he completed a dual degree in Mechanical Engineering from the Indian Institute of Technology (IIT) at Kharagpur, India. He has also been associated with AstraZeneca, Shell, Microsoft Research and CSIR-South Africa at various points in his career and has been actively involved in the AI4SG (AI for Social Good) community. He has authored several publications around machine learning and medical imaging and is a reviewer for multiple peer reviewed venues such as NeurIPS, ICML, MICCAI and several journals. Arijit has been appointed as a Rising Leaders fellow of the Aspen Institute UK, and as an International Strategy Forum fellow in 2024.
  
  Date/Time - 13.30-14.30 14th November 2024

• (10 October 2024) Tutorial on Foundation Models For Medical Imaging - with MICCAI 2024, Morocco
  
  Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers.
  
  As part of MICCAI2024, Yunsoo, Jinge and Honghan worked with colleagues from IBM, ÉTS Montréal, and Stanford University to organize a tutorial on Foundation Models For Medical Imaging. Yunsoo and the rest of KnowLab team took the lead on “the multimodal LLMs in medicine” section.
  
  Tutorial website at https://sites.google.com/view/miccai-2024-tutorial/home.