Globally, there has been a significant increase in the number of people using prescription drugs for reasons other than why they were prescribed, sometimes combining them with other substances such as alcohol to sleep better or stimulants to perform better. A team of computer scientists and emergency physicians from Emory, Oregon, and Pennsylvania universities in the United States used AI to analyze drug misuse and the emotions users felt during times of use. The study, "Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use," was published in the journal Health Data Science. In 2021, more than 108,000 people in the U.S. died from drug overdoses, a number that is up 20% from 2020, many of these deaths were caused by the ingestion of prescription drugs, often mixed with other substances. In France, more than 10,000 people die each year as a result of medication misuse.

This book is a must-know for those working as health data scientists.

DECODE is a 30-month research project funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) Programme. This project is led by Loughborough University (PI: Dr Gyuchan Thomas Jun, Reader in Socio-technical System Design) jointly with Leicestershire Partnership NHS Trust (joint PI: Dr Satheesh Gangadharan, Consultant Psychiatrist). Overall, the project team consists of fifteen co-investigators with expertise in the field of intellectual disabilities, neuropsychiatry, epidemiology, health data science, machine learning, data visualisation, human factors, qualitative research and ethics from eight institutions. The co-investigators include Dr Georgina Cosma (AI and data science) and Dr Panos Balatsoukas (UX design) at Loughborough University, Dr Francesco Zaccardi (epidemiology), Dr Michelle O'Reilly (qualitative research) and Prof Kamlesh Khunti (primary care) at the University of Leicester, Ashley Akbari (data science) and Prof Simon Ellwood-Thompson (health informatics) at Swansea University, Dr Vasa Curcin (AI) at King's College London, Prof Rohit Shankar (neuropsychiatry) at the University of Plymouth, Dr Reza Kiani (intellectual disabilities) at Leicestershire Partnership NHS Trust, Dr Neil Sinclair (ethics) at the University of Nottingham, Dr Chris Knifton (nursing) at De Montfort University, and Gillian Huddleston (PPI lead). The DECODE project aims to improve the health and wellbeing of people with intellectual disabilities (also known as learning disabilities) by developing actionable insights to support a model of effective care coordination using machine learning aided analysis of multiple long-term conditions in people with intellectual disabilities.

Original aim: Our first milestone will be to publish a draft framework on a preprint server such as medRxiv at month 12 of the project. This will be updated based on relevant changes in direction to HDR UK priorities, or new insights gained after its deployment in use-case scenarios for work packages 2 and 3. Current progress: The TRIPOD-AI protocol has been recently published in BMJ Open1. An additional article has recently been published in the Journal of Clinical Oncology2 on the critical need for better reporting of machine learning methods in health data science for risk prediction. Original aim: We anticipate pre-registering this research project by month 9 of the project. A manuscript will then be submitted to a preprint server such as medRxiv, by month 24, to report the utility and pitfalls of synthetic data generation models to support reproducible machine learning in wearable sensor data and electronic health records.

Desired Qualifications: • Ph.D. in the disciplines listed above.