What's your project?
I'm raising £11,521 to enable me to undertake a Master's Degree in Data Science and Machine Learning at University College London.
I will use what I learn to help re-design how we use technology in healthcare, to improve healthcare for patients and for doctors.
I will also share my learnings with the medical community through blogs and videos (I've already started doing so at MLmedics.com and want to build on this throughout the Master's).
Who are you?
My name is Chris Lovejoy, I'm 26 years old and was the first in my family to go to university to study medicine. I grew up in a small town in Shropshire, UK.
What's your Story
While working as a junior doctor for the last 2 years, I became increasingly frustrated by the technology being used in healthcare. We were working on busy wards and A+E departments - in high-pressure situations with lots of sick patients - but the computers and technology we were using was often poor. This would slow us down, and make it harder for us to provide high-quality care.
For example, when a patient has had blood tests, it is useful to look back at previous blood tests for comparison, to understand the trend. However, at one hospital I worked at, the system would only show very recent results and it could take extremely long to load more than a few earlier results.
It seemed crazy to me that, given the vital importance of good health, we would be reading key information on a computer running Windows XP, on software designed in the early 2000s.
Instead of just remaining frustrated, I decided to do something about it: I decided to undertake a degree in data science and machine learning. I chose these areas because I believe they have the greatest potential to improve how we provide healthcare.
At the moment, doctors and other healthcare professionals have little say in the technology systems that we use; we just have to work with what we're given. I passionately believe that if we want to improve the technology in healthcare, this should be led by medical professionals. Working in healthcare provides insights into how the systems should be improved - we just need to develop the technological know-how to do so.
There are very few doctors with formal education in machine learning; I will be one of a small number in our generation. Throughout my degree I plan to create resources to help more healthcare professionals understand the area, and to inspire them to do so. I have already started doing so at MLmedics.com.
While many may not be familiar with the term 'machine learning', it is part of the broader area of 'artificial intelligence'. Machine learning could be used in medicine to improve hospital workflows, to facilitate better research into diseases such as cancer, and ultimately free up more time for doctors to spend with patients.
I want to support the implementation of this technology in a safe, effective and patient-centred way.
Where will the money go?
Costs (click to see where the figures came from):
- Tuition fees: £13,340
- Living expenses: £13,520 (the lower estimate of UCL's living cost range, as I plan to live modestly)
- Stripe fees (2.4% applied to successful projects): £270
TOTAL = £27,130
Existing financial support:
- Personal savings: £5,000
- Student Finance England: £10,609
TOTAL = £15,609
DEFICIT = £27,130 - £15,609 = £11,521
I want to give back as much as possible to those who donate, so have a look at some of the rewards below.
I'm also open to further suggestions, so drop a comment or email me at email@example.com.
What work have you done so far in the area?
During a rotation in geriatric medicine, I saw the sometimes-devastating consequences of unnecessary hospital admissions in the elderly population. While searching for a solution, I came across the award-winning tech-enabled homecare provider Cera Care and am now Project Manager of a predictive analytics project within the company. We are developing a machine learning model to predict clinical deteriorations in patients’ own homes and enable earlier interventions to prevent hospital admission. I designed a clinical labelling system to enable supervised topic modelling, based on findings with analysis tools that I created in Python and Keras.
Alongside this, I've undertaken scientific research exploring how we can use machine learning in healthcare. I am first author on four peer-reviewed papers so far, with several more in the write-up and submission stages.
I've also worked on a number of other projects; I developed a tool to analyse epigenetic modifications for predicting drug treatment response, created a device to recognise medications to reduce medication errors and am currently building a database of social prescribing services for patients and GPs.
For more details, you can access my CV here.
Me working as a doctor in a busy A+E department
Running courses to teach healthcare professionals about machine learning
Find us here (We will be sharing updates)
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Help us succeed!
You don't need to give money to help us succeed! Please share this project with anyone you think would support us – on Twitter, Facebook, LinkedIn, by email, telephone, in a chat over the fence or on your blog.
Also, feel free to reach out at firstname.lastname@example.org with any questions or suggestions - I'd love to hear from you.