Application Deadline: 30 August 2023 Details This project has a specific focus in managing the single greatest threat to global health, the increasing burden from infections caused by bacteria that are resistant to antibiotics (antimicrobial resistance, AMR). Doctors (humans) can’t reliably know which antibiotic to administer in an emergency. In fact, based on our earlier research they get it wrong about 20% of the time. A serious bacterial infection will look the same whether the bacteria causing the infection are resistant to certain antibiotics or not, and the first antibiotic must be selected on very limited information and be given the first hour of admission to hospital if there is a risk they have developed an infection that is spreading through their body. Understandably, this ‘high stakes’ uncertainty promotes the use of ‘broad-spectrum’ antibiotics which should be held in reserve for known drug-resistant infections. Natural language processing (NLP) has the potential to safely unlock successful antimicrobial stewardship for AMR at the first dose. In earlier work, we used quantitative and categorical data from electronic health records (EHRs) from patients who needed emergency hospital admission to see which antibiotics were given in the emergency room, how often a patient was prescribed an antibiotic that their bacterial infection was resistant to (under-prescribing), and how often a broad-spectrum antibiotic was used when another antibiotic alternative would have been equally effective (over-prescribing). We trained a machine learning algorithm that was allowed to under-prescribe at the same rate as doctors (about 20% of the time), that could also reduce the use of broad-spectrum antibiotics by about 40% by anticipation of which patients were unlikely to have an AMR infection. This powerful proof-of-concept work shows the huge potential for AI in personalised medicine and antimicrobial stewardship at the first and most important dose. Taking the next steps in AI for AMR. We know that a lot of important information is held in free text clinician notes that aren’t reflected in the data we used to build the model, and want to understand what valuable information contained in the free text data would help improve prediction accuracy. This project aims to analyse free-text clinician notes to retrieve valuable information that can improve the prescribing of antibiotics by more accurately predicting an individual patient’s risk of having an antibiotic-resistant infection. We are seeking a motivated student to undertake a 4 year funded PhD, in collaboration with Shionogi, a pharmaceutical company with offices in London. Eligiblity The successful candidate will hold a bachelor’s degree (or above) in Computer Science, Physics, Mathematics, Psychology or related discipline and have proven experience in computational linguistics, natural language processing, machine learning. Previous experience of applying AI methods to the medical domain is a strong advantage. Furthermore, the candidate will have strong programming skills, expertise in machine learning approaches and be excited be the challenges of interdisciplinary research between medicine and computer science. We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled and neuro-diverse candidates. We also welcome applications for part-time study. The University of Birmingham works closely with University Hospitals Birmingham NHS Foundation Trust (UHB), which is the single-largest Acute NHS Trust in the UK, and serves the healthcare needs of over 1.2m people in the second-largest city in the UK. PIONEER, the Health Data Research Hub for Acute Care, alone includes >1.2m patient episodes per year with >10yrs longitudinal health data. This experienced collaboration means we are uniquely positioned to develop, model and then later embed AI-supported antimicrobial stewardship within a clinical trial and electronic prescribing systems. The student will be located at the Institute of Microbiology and Infection (IMI) of the University of Birmingham, the largest academic research institute in the field of microbiology and infectious diseases in the United Kingdom. The IMI is part of the School of Medical and Dental Sciences, defining the future of health and medicine through the provision of innovative education and exceptional research. Throughout the PhD project, regular meetings with industry partner colleagues at Shionogi will be held to monitor progression and support the student in their research. About Shionogi Established in Japan 140 years ago, Shionogi has a history of drug discovery and scientific rigour in addressing some of the toughest challenges in healthcare. Shionogi’s work in antimicrobial resistance (AMR) is a key part of our contribution to the UN Sustainable Development Goals (SDGs) - we invest the highest proportion of our pharmaceutical revenues in relevant anti-infectives R&D compared to other large pharmaceutical companies. Shionogi announced the first-ever licence agreement for an antibiotic to treat serious bacterial infections between a pharmaceutical company and a non-profit organisation driven by public health priorities. Working with the Global Antibiotic Research and Development Partnership (GARDP) and the Clinton Health Access Initiative (CHAI), the agreement aims to provide 135 countries with access. At Shionogi, our belief is that sustainable growth hinges not only on new drug creation, but also on consolidating our strengths in areas of strategic focus. Through external partnerships, we seek to bring benefits to more patients through collaboration in areas where it would be difficult for us to go it alone. Globally, the number of our partners, including partnerships across a range of industries, including academia, enables us to accelerate innovation to better help societies manage some of the most important public health threats and to take on areas where the unmet clinical need is greatest. Funding Notes The position offered is for three and a half years full-time study. The current (2023-24) value of the award is stipend; £18,622 pa; tuition fee: £4,712 pa. Awards are usually incremented on 1 October each following year. The package includes a Macbook Air and funding for additional training and conference attendance. References Moran E, Robinson E, Green C, Keeling M, Collyer B. Towards personalized guidelines: using machine-learning algorithms to guide antimicrobial selection. J Antimicrob Chemother. 2020. doi:10.1093/jac/dkaa222 Cavallaro M, Moran E, Collyer B, McCarthy ND, Green C, Keeling MJ. Informing antimicrobial stewardship with explainable AI. bioRxiv. 2022. doi:10.1101/2022.08.12.22278678
https://www.findaphd.com/phds/project/natural-language-processing-of-electronic-health-records-to-improve-empirical-prescribing-in-acute-admissions/?p159899 With best regards, Mark Lee Professor of Artificial Intelligence School of Computer Science University of Birmingham www.cs.bham.ac.uk/~mgl<http://www.cs.bham.ac.uk/~mgl>
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