Sleep walking into clinical data science
If I’ve learned one thing over the last five months in the Computational Oncology lab, it is that real world data is a whole different ball game.
As a fresh-out-of-undergrad master’s student, with a background in cognitive neuroscience and biological sciences, a data science project wasn’t entirely in my toolkit. My programming skills was a working knowledge of Java and online courses in Python and Machine Learning picked up over the summer. With MRes project choices, I found myself with a unique opportunity to gain experience in a lab, working with highly experienced researchers, clinicians and students. I figured a project in the Computational Oncology lab would be a challenge – but what better way to learn how to analyse large datasets or apply machine learning methods than immerse myself in a project.
For the past 5 months, I have been working under Dr Seema Dadhania on a project from the ongoing BrainWear clinical study. Specifically, I was tasked with analysing sleep in patients with High Grade Gliomas, a malignant primary brain tumour. Sleep disturbance is one of the most commonly experienced symptoms for patients with High Grade Gliomas. In fact, in a 2018 study by Garg et al, it was found that disrupted sleeping behaviours were three times more prevalent in patients with primary brain tumours than healthy controls, and was linked to decreased quality of life. Despite how pervasive and burdening sleep problems can be in brain tumour patients, research remains scarce and the studies that have been done use self-reported measures such as the EORTC-QLQ C30 and MDASI-BT, questionnaires which assess quality of life or severity of symptoms. These measures run the risk of subjectivity – patients may quantify difficulty with sleep in different ways which skews the translatability of findings to real life. With the collection of longitudinal accelerometer data, BrainWear gives the opportunity to objectively understand sleep patterns and changes that occur with treatment.