Bio: Hi, my name’s Jake Parker and I’m currently studying Maths with Finance at the University of Plymouth. Further to my degree, I regularly participate in Digdata virtual experience, which are problems set by real companies, fostering my industrial awareness as I got chance to work on real world datasets, therefore I am eager to bring these analysis skills to my new role in healthcare.

I am excited to join a fast-paced, innovative department such as MFT Critical Care Research and Innovation. Within the role, I intend to improve my skills in R and Python and develop my technical knowledge and application of skills utilising the facilities and familiarising myself with a state-of-the-art data lab.

I am passionate about the technological revolution taking place currently as industries are beginning to realise the power of data and I am committed to lifelong learning in order to grow my career in the field of big data. I am lucky to be central to this shift as my new role is concerned with optimising the way patients are cared for, which can improve their healthcare and ultimately their quality of life.

I am passionate about contributing to this mission in a meaningful way to support my career and future development as a digital innovator and problem solver.

 

Project: ICU patient vital signs waveform data with Drs Anthony Wilson and Alex Parker at Manchester University NHS Foundation Trust.

The critical care data science team at Manchester Royal Infirmary, in collaboration with researchers at the University of Manchester, have developed software to extract continuous vital signs waveforms from patients monitored in our 52 bed ICU. Currently the data is stored in raw format (pairs of amplitude time measurements) but this project would seek to clean and analyse this data. The first stage would be to reconstruct ECG and/or arterial blood pressure waveforms from the raw data. The second stage would be to classify intervals of waveforms as “valid” or “invalid” using established criterion. The third stage would be to derive numeric values such as heart rate and blood pressure from this data.