(29 July 2022) Our collaboration study titled “Prediction of Five-Year Cardiovascular Disease Risk in People with Type 2 Diabetes Mellitus - Derivation in Nanjing, China and External Validation in Scotland, UK”, led by Cheng Wan from Nanjing Medical University, has been published by Global Heart. This study shows it is feasible to generate a risk prediction model using routinely collected Chinese hospital data. This indicates there is a great potential to make use of the large-scale and relatively easy accessible route data for identifying those at risk of CVD and help significantly improve CVD prevention in people with diabetes.
(16 June 2022) Our collaboration study titled “Spine-GFlow - A Hybrid Learning Framework for Robust Multi-tissue Segmentation in Lumbar MRI without Manual Annotation”, led by Dr Teng Zhang from Hong Kong University, has been accepted by Computerized Medical Imaging and Graphics. Results of this study show that our method, without requiring manual annotation, has achieved a segmentation performance comparable to a model trained with full supervision (mean Dice 0.914 vs 0.916).
(10 June 2022) Out work, titled COVID-19 trajectories among 57 million adults in England - a cohort study using electronic health records, is now out with Lancet Digital Health. Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.
(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.
This article describes how
knowledge graph technologies can help with health data science, particularly on free-text electronic health records.
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.