BEng, MEng, PhD
Personalised prescription based on individual-patient data using deep neural networks
My research interests and specialists are applications of machine learning and statistical analysis in medical and biological science, including personalised treatment and diagnosis for mental healthcare, prediction models, cell phenotypic analysis, biomedical signal processing, MCDA in healthcare, smart sensors, wearable sensors, sensor integration and data fusion algorithms, visual SLAM and scene understanding, NLP algorithms.
My recent research is focused on using machine learning, mainly deep neural networks to guide personalised treatment for individuals with dementia or depression. By applying machine learning and statistical models to both RCT and real-world longitudinal data (EHRs or clinical notes), we can predict post-treatment cognitive or depression scores, dropout risks, probabilities of experiencing side effects, etc., at the individual patient level. Furthermore, we can combine patients' and clinicians' preferences with treatment predictions for patient-centred decision-making using MCDA.
I have also been developing deep neural networks for cell morphological alteration detection and drug repurposing. We can distinguish various treatment conditions in neuronal cell culture or co-culture of neurons and microglia by analysing fluorescence images painted with multiple stains and bright-field images using both end-to-end deep learning models and extracted cell features. Given untreated neuronal images, we have developed several deep learning based generative models for artificial cell staining and generation.
Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.
Liu Q. et al, (2022), BMC Psychiatry, 22
Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.
Liu Q. et al, (2022), BMC Med, 20