Health AI researcher · London, UK

Building clinically useful AI from speech and longitudinal health data.

I am a PhD candidate in Health Informatics at King's College London. My research brings together machine learning, digital biomarkers, epidemiology and medical statistics to support more accessible and interpretable healthcare.

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Bio

Research at the intersection of AI and population health.

My doctoral work combines evidence synthesis, diagnostic-accuracy assessment and multilingual deep learning for automatic speech-based depression screening. I also designed and built a working iOS prototype that translates this research into a mobile health application.

Alongside my PhD, I lead an analytical work package within the eLIXIR-BiSL programme, working with more than 30,000 repeated observations from over 18,000 infants across linked NHS datasets. Before research, I worked in data analytics and governance at Deloitte Consulting and Bank of Jilin.

I use Python, PyTorch, R, SQL and Swift, and enjoy collaborating across clinical, academic and data teams to turn complex methods into clear, responsible and reproducible outputs.

Research

Current themes

Multilingual speech AI

Developing and evaluating deep-learning models for depression screening from English and Chinese clinical speech, with an emphasis on cross-language generalisability and clinically meaningful evaluation.

Digital biomarkers & mobile health

Translating research models into usable tools, including a Swift/iOS prototype that connects speech capture with automated depression-screening outputs.

Longitudinal health data

Analysing linked NHS maternity, neonatal and Health Visitor records using reproducible pipelines, mixed-effects models and clinically interpretable trajectories.

Publications

Published work

All publications listed on my Google Scholar

First-author publications

3 publications

  1. 2025
    Development of an AI-based Mobile App for Automatic Depression Screening Using Speech in English and Chinese

    Lidan Liu, Florence Tydeman, Wanqing Xie, and Yanzhong Wang

    47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

  2. 2024
    Diagnostic accuracy of deep learning using speech samples in depression: a systematic review and meta-analysis

    Lidan Liu, Lu Liu, Hatem A. Wafa, Florence Tydeman, Wanqing Xie, and Yanzhong Wang

    Journal of the American Medical Informatics Association, 31(10), 2394-2404

  3. 2024
    Multilingual Depression Detection Based on Speech Signals and Deep Learning

    Lidan Liu, Florence Tydeman, Wanqing Xie, and Yanzhong Wang

    IEEE BigDataService, 115-116

Co-authored publications

3 publications

  1. 2026
    Global Burden of Polycystic Ovary Syndrome: Socioeconomic Disparities, Reproductive Impacts, and Projections to 2050

    Chenzhen Du, Liling Deng, Junlin Ran, Yanzhong Wang, Dongfeng Tang, Lu Liu, Lidan Liu, Canlong Wang, Salma Ayis, Lu Chen, Yi Li, Hongyan Wang, Bo Deng, Xiaoyan Jiang, and Wuquan Deng

    Tropical Medicine & International Health, 31(6), 669-715

  2. 2026
    Sequence-to-Graph Learning for Depression Detection with Clinical Interview Video Recording

    Mingmei Cheng, Yongqi Lin, Yaonan Zhu, Xiaoyuan He, Chaofan Li, Baoliang Zhang, Xiaofeng Liu, Lidan Liu, Chenyang Xu, Chuan Shi, Yanzhong Wang, and Wanqing Xie

    IEEE Journal of Biomedical and Health Informatics, 1-13 (advance online publication)

  3. 2025
    Long-term outcomes of depression up to 10-years after stroke in the South London Stroke Register: a population-based study

    Lu Liu, Iain J. Marshall, Xianqi Li, Ajay Bhalla, Lidan Liu, Ruonan Pei, Charles D. A. Wolfe, Matthew D. L. O'Connell, and Yanzhong Wang

    The Lancet Regional Health - Europe, 54, 101324

Manuscripts under review

Current submissions

  1. Submitted

    Transportability of pretrained speech representations for depression assessment across English and Mandarin cohorts

    Lidan Liu, Lu Liu, Lei Lu, Wanqing Xie, and Yanzhong Wang

    Submitted to PLOS Digital Health

  2. Submitted

    Shrinkage-guided synthetic speech augmentation for imbalanced depression detection

    Lidan Liu, Lei Lu, Wanqing Xie, and Yanzhong Wang

    Submitted to IEEE ICASSP 2027

Background

Education & experience

2022 - present

PhD Candidate, Health Informatics

King's College London · Department of Population Health Sciences

2025 - present

Research Assistant, eLIXIR-BiSL

King's College London · NHS South London Trusts

2019 - 2020

MSc, Advanced Computing

King's College London · Department of Informatics

2015 - 2019

BEng, Software Engineering

Jilin University

Community

Research group & collaborators

A collaborative community spanning population health, medical statistics and health AI.

Lidan Liu with research colleagues at King's College London
Research colleagues together after a group event in London
Members of the research community at a social gathering
Research colleagues on a group trip in the snow
Research colleagues at an end-of-year gathering

People & research interests

Ordered by total Google Scholar citations · checked August 2026

  1. 01

    Yanzhong Wang

    Google Scholar

    Biostatistics · Statistical epidemiology · Predictive models · Stroke · Multimorbidity

    63,845citations

  2. 02

    Lucilla Poston

    Google Scholar

    Maternal and fetal health · Maternal nutrition and obesity · Gestational diabetes · Life-course data linkage

    56,131citations

  3. 03

    Laura Magee

    Google Scholar

    Medical complications of pregnancy · Clinical trials · Epidemiology · Reproductive toxicology

    52,360citations

  4. 04

    Wanqing Xie

    Google Scholar

    AI in healthcare · Health informatics · Machine learning · Medical statistics · Non-linear analysis

    4,984citations

  5. 05

    Lu Liu

    Google Scholar

    Post-stroke depression · Longitudinal stroke outcomes · Population health epidemiology

    605citations

  6. 06

    Canlong Wang

    Google Scholar

    Real-world evidence · Trial emulation · Epidemiology · Sports medicine

    522citations

  7. 07

    Davide Ferrari

    Google Scholar

    Machine learning · Artificial intelligence

    348citations

  8. 08

    Xianqi Li

    Google Scholar

    Epidemiology · Stroke outcomes · Population health

    258citations

  9. 09

    Tianyi Liu

    Google Scholar

    Cardiovascular disease · Machine learning · Electronic health records · Risk prediction

    249citations

  10. 10

    Dongfeng Tang

    Google Scholar

    Chronic diseases · Frailty · Population health

    99citations

Contact

Let's connect.

I welcome conversations about health AI, digital biomarkers, longitudinal health data and interdisciplinary research opportunities.