1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson’s Dyskinesia 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Michael A. Lones School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh, UK +44 (0)131 4518434 m.lones@hw.ac.uk Jane E. Alty Department of Neurology Leeds General Infirmary Leeds, UK +44 (0)113 3928118 Jane.Alty@hyms.ac.uk Jeremy Cosgrove Department of Neurology Leeds General Infirmary Leeds, UK +44 (0)113 3928118 jeremycosgrove@nhs.net Philippa Duggan-Carter Department of Neurology Leeds General Infirmary Leeds, UK +44 (0)113 2432799 philippa.duggan-carter@nhs.net Stuart Jamieson Department of Neurology Leeds General Infirmary Leeds, UK +44 (0)113 3882067 stuart.jamieson1@nhs.net Rebecca F. Naylor Department of Electronic Engineering University of York York, UK +44 (0)1904 322351 becky.naylor@york.ac.uk Andrew J. Turner Department of Electronic Engineering University of York York, UK +44 (0)1904 322351 andrew.turner@york.ac.uk Stephen L. Smith Department of Electronic Engineering University of York York, UK +44 (0)1904 322351 stephen.smith@york.ac.uk 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Stephen Smith stephen.smith@york.ac.uk 4. the abstract of the paper(s); Parkinson’s disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient’s movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; A, D, G 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); (A) The result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention. The system, as described in the paper, has been protected under three filed patent applications as "apparatus", recognising the invention of combining an algorithm (IRCGP), in conjunction with IMU sensors (accelerometers), to monitoring a specific medical condition (Parkinson's levodopa-induced dyskinesia) : Title Country Application No Publication No Monitoring Device China [PCT] 2013800477226 104619252 A Monitoring Device European [PCT] 13741818.2 2872041 Monitoring Device US [PCT] 14/414,591 20150208955 The algorithm (IRCGP) has been protected under the following patent applications, of which two (USA and Australia) have been granted: Title Country Application No Publication No Signal Processing Method and Apparatus Australia [PCT] 2012208360 2012/098388 Signal Processing Method and Apparatus Brazil [PCT]    112013018412-4 2012/098388 Signal Processing Method and Apparatus Canada [PCT]    2825082 2012/098388 Signal Processing Method and Apparatus China [PCT]     2012800141456 103430192 Signal Processing Method and Apparatus European [PCT]  12702610.2 2666120 Signal Processing Method and Apparatus Singapore [PCT] 10201600232R 10201600232R Signal Processing Method and Apparatus US [PCT] 13/980,320 2014-0155784 (D) The result is publishable in its own right as a new scientific result independent of the fact that the result was mechanically created. The work was published in the medical literature, in a journal which focuses on new contributions to the clinical healthcare environment. This indicates that the work has been accepted as a new scientific result based upon its relevance to the application domain, independent of our use of a machine learning approach to achieve it. The device has now been CE Marked (signifying regulatory compliance with European directives) and is in routine clinical use in three NHS (state) hospitals in the UK (Leeds, Harrogate and Scarborough) and the Ruijin Hospital, Shanghai, China (a renowned general hospital affiliated to the School of Medicine, Shanghai Jiao Tong University). (G) The result solves a problem of indisputable difficulty in its field. Parkinson’s disease is a common neurodegenerative disease caused by the loss of dopamine-producing neurons required for motor function. Although there is no cure, the quality of life of patients can be greatly improved by administering dopamine replacement drugs in appropriate doses and at appropriate times throughout the day. However, it is critical to keep a patient’s medication as low as possible whilst still treating the symptoms of the disease. Too much medication leads to unpleasant motor complications, such as dyskinesia, but also reduces the effectiveness of medication over time. Choosing an appropriate dosage currently requires routine close clinical observation, which is time-consuming, expensive, and relies on the availability of trained clinicians. The effectiveness of this observation then rests on their ability to correctly measure motor symptoms, yet current ways of doing this are subjective, and even patients find it difficult to give a reliable estimate of the severity and frequency of their symptoms (ref 28 in paper). Another factor contributing to the difficulty of the problem is that the underlying causes of the disease and its clinical signs are poorly understood (ref 26 in paper). In our work, we have successfully developed a system that automates the process of monitoring the side-effects of medication and provides clinicians with an accurate portrayal of a patient’s specific response to medication over the course of a day, giving them the information needed to optimise a patient’s medication regime. In particular, using a genetic programming approach, we were able to develop an accurate predictive model of dyskinesia (the primary physiological indication of excess medication), using this to provide a reliable, objective measure of a patient’s response to medication. 7. a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Lones MA, Alty JE, Cosgrove J, Duggan-Carter P, Jamieson S, Naylor RF, Turner AJ, Smith SL. A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson’s Dyskinesia. Journal of medical systems. 2017 Nov 1;41(11):176. 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Prize money, if any, is to be divided equally among the co-authors. 9. a statement stating why the authors expect that their entry would be the "best" We believe our entry has particular merit as it reports the successful application of a novel representation of GP (Implicit context Cartesian Genetic Programming) to resolve a challenging and life-affecting clinical condition (Parkinson's dyskinesia). A published health economic assessment has shown that not only will the introduction of the technology significantly improve the quality of life of people with Parkinson's dyskinesia, but also has the potential to save the UK's National Health Service over £84m per year. Filby A, Lewis L, Taylor M, Smith SL, Dettmar PW, Jamieson SD, Alty JE. Cost effectiveness analysis of a device to monitor levodopa-induced dyskinesia in parkinson’s patients. Value in Health. 2015 Nov 1;18(7):A358. 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GP - specifically, Implicit Context Representation Cartesian Genetic Programming (IRCGP), a form of Cartesian Genetic Programming (CGP) that overcomes problems associated positional dependence. 11. The date of publication of each paper.  If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. Published online: 25 September 2017