output
20192026
most citedUsing smartphones and wearable devices to monitor behavioural changes during COVID-19

204 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

10 papers

cs.CL2026★ 4 cited

Unlocking Electronic Health Records: A Hybrid Graph RAG Approach to Safe Clinical AI for Patient QA

Samuel Thio, Matthew Lewis, Spiros Denaxas +1

Electronic health record (EHR) systems present clinicians with vast repositories of clinical information, creating a significant cognitive burden where critical details are easily…

cs.LG2023

Development of a Knowledge Graph Embeddings Model for Pain

Jaya Chaturvedi, Tao Wang, Sumithra Velupillai +2

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand t…

q-bio.QM2022★ 45 cited

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Shaoxiong Sun, Amos A. Folarin, Yuezhou Zhang +28

A number of challenges exist for the analysis of mHealth data: maintaining participant engagement over extended time periods and therefore understanding what constitutes an accepta…

q-bio.QM2022★ 21 cited

Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +27

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of dai…

stat.ML2021★ 78 cited

Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +21

The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluet…

cs.HC2021★ 10 cited

Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder

Judith Dineley, Grace Lavelle, Daniel Leightley +22

The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced…

stat.AP2020★ 123 cited

The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +19

Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography, is not suitable for long-term, continu…

q-bio.QM2020★ 204 cited

Using smartphones and wearable devices to monitor behavioural changes during COVID-19

Shaoxiong Sun, Amos Folarin, Yatharth Ranjan +23

We aimed to explore the utility of the recently developed open-source mobile health platform RADAR-base as a toolbox to rapidly test the effect and response to NPIs aimed at limiti…

cs.IR2020★ 64 cited

The side effect profile of Clozapine in real world data of three large mental hospitals

Ehtesham Iqbal, Risha Govind, Alvin Romero +8

Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing…

cs.CL2019★ 16 cited

Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study

Honghan Wu, Karen Hodgson, Sue Dyson +6

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to co…