Machine learning, AI and robotics in health

Centre for Health Technology
eHealth EPIC technology robots
A wide range of digital health technologies can prevent ill-health, promote well-being and support the delivery of health and social care. At CHT, we are investigating the development and use of humanoid and companion robots, and other digital devices, such as voice activated assistants, to transform the provision of health and care, from assistive living and health monitoring to cognitive training and social engagement.
Enabled by artificial intelligence (AI) and machine learning, these technologies are able to interact with and respond to people, adapting to their behaviour, emotions and needs to better support their health and wellbeing.
There are also numerous applications of AI/ML, either used in conjunction with other technologies, such as assistive music technology, or to leverage real world data to provide insights as to which patients can benefit from, for example, home-based technologies for earlier management of health risks, to increase the wellbeing of patients and lower potential future costs to the NHS.
Whether applied to specific groups (such as childbearing women with Epilepsy) or local populations as a whole, we have a strong record of applying AI and statistics in health and biomedical informatics to identify the complex interactions between socioeconomic, cultural and environmental factors that contribute to individual- and population-level health outcomes.

Our current projects

Previous successes

Who's involved

Publications

2024

Courtman M, Kim D, Wit H, Wang H, Sun L, Ifeachor E, Mullin S, Thurston M. (2024) Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety. J Imaging Inform Med. doi: 10.1007/s10278-023-00932-8. https://doi.org/10.1007/s10278-023-00932-8
Oralbayeva N, Aly A, Sandygulova A, Belpaeme (2024) T Data-Driven Communicative Behaviour Generation: A Survey, ACM Transactions on Human-Robot Interaction, 10.1145/3609235. https://doi.org/10.1145/3609235
Zogaan, WA, Mehrbakhsh Nilashi, Hossein Ahmadi, Rabab Ali Abumalloh, Mesfer Alrizq, Hamad Abosaq, Abdullah Alghamdi (2024). A Combined Method of Optimized Learning Vector Quantization and Neuro-Fuzzy Techniques for Predicting Unified Parkinson's Disease Rating Scale Using Vocal Features, MethodsX, 5 January, 102553. https://doi.org/10.1016/j.mex.2024.102553

2023

Alsayat A, Ahmadi H (2023). A Hybrid Method Using Ensembles of Neural Network and Text Mining for Learner Satisfaction Analysis from Big Datasets in Online Learning Platform. Neural Processing Letters, 55 (3267–3303). https://doi.org/10.1007/s11063-022-11009-y
Arji G, Ahmadi H, Avazpoor P, Hemmat M, (2023). Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review. Informatics in Medicine Unlocked, 38 101199. https://doi.org/10.1016/j.imu.2023.101199
Burnett, B, Zhou, SM, Brophy, S, Davies P, Ellis P, et al (2023). Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review. Diagnostics 13, 301. https://doi.org/10.3390/diagnostics13020301
Cullinan MF, Scott R, Linogao J, Bradwell H, Cooper L & McGinn C (2023) Development and Demonstration of a Wireless Ultraviolet Sensing Network for Dose Monitoring and Operator Safety in Room Disinfection Applications. Sensors 23, (5) 2493-249. https://doi.org/10.3390/s23052493
Giorgi, I., Minutolo, A., Tirotto, F. et al. (2023). I am Robot, Your Health Adviser for Older Adults: Do You Trust My Advice? Int J of Soc Robotics (2023). https://doi.org/10.1007/s12369-023-01019-8
Longo E, Burnett B, Bauermeister S, Zhou SM (2023). Identifying dynamic patterns of polypharmacy for patients with dementia from primary care electronic health records: A machine learning driven longitudinal study. Ageing and Disease, April, 14(2): 548~559. https://doi.org/10.14336/AD.2022.0829
Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alghamdi A, Alrizq M, Alyami S, Nayer FK (2023). Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. Heliyon. 2023 Apr 5;9(4):e15258. https://doi.org/10.1016/j.heliyon.2023.e15258
Nilashi M, Ali R, Samad S, Minaei-Bidgoli B, Thi HH, Alghamdi OA, Ismail MY, Ahmadi H (2023). The Impact of Multi-Criteria Ratings in Social Networking Sites on the Performance of Online Recommendation Agents. Telematics and Informatics. January; 76: 101919. https://doi.org/10.1016/j.tele.2022.101919
Nilashi, M, Rabab Ali, Sarminah Samad, Behrouz Minaei-Bidgoli, Ha Hang Thi, OA Alghamdi, Muhammed Yousoof Ismail, Hossein Ahmadi (2023). The Impact of Multi-Criteria Ratings in Social Networking Sites on the Performance of Online Recommendation Agents. Telematics and Informatics Volume 76, January, 101919. https://doi.org/10.1016/j.tele.2022.101919
Omisade O, Gegov A, Zhou SM, Good A, Sengar S, Liu B, Adedeji T, and Toptan C (2023). Explainable Artificial Intelligence and Mobile Health for Treating Eating Disorders in Young Adults with Autism Spectrum Disorder based on the Theory of Change: A mixed method protocol. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_3
Qayyum, A., Loizou, M., et al. (2023). SEGCROP: Segmentation-based dynamic cropping of endoscopic videos to Address label leakage in surgical tool detection. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023). https://doi.org/10.1109/isbi53787.2023.10230822
Rodríguez-Espíndola O, Ahmadi H, Gastélum-Chavira D, Ahumada-Valenzuela O, Chowdhury S, Kumar Dey P, Albores P. (2023), Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation, Socio-Economic Planning Sciences, Volume 89, 101669. https://doi.org/10.1016/j.seps.2023.101669
Roesler O, Bagheri E, Aly A (2023). Toward Understanding the Effects of Socially Aware Robot Behavior. Interaction Studies 23 (3):355-359. https://doi.org/10.1075/is.22029.roe
Zhou SM, Baines, R, Roberts E, Hannon P, Ashby S, Chatterjee A, Sen A, Laugharne R, Shankar R (2023). Analysing Patient-Generated Data to Understand Behaviours and Characteristics of Childbearing Women with Epilepsy, Seizure - European Journal of Epilepsy, 108:24-32. https://doi.org/10.1016/j.seizure.2023.04.008
Zhou, SM, Baines R, Roberts E, Hannon P, Ashby S, Chatterjee A, Sen A, Laugharne R, Shankar, R (2023). Analysing Patient-Generated Data to Understand Behaviours and Characteristics of Childbearing Women with Epilepsy. Seizure - European Journal of Epilepsy, 108(2023) 24-32. https://doi.org/10.1016/j.seizure.2023.04.008

2022

Ahmadi N, Nilashi M, Minaei-Bidgoli B, Farooque M, Samad S, Aljehane NO, Zogaan WA, Ahmadi H (2022). Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network. Scientific Programming, Feb 28, 2022. https://doi.org/10.1155/2022/4439189
Almowil Z, Zhou SM, Croxall J and Brophy S. (2022). Concept libraries for repeatable and reusable research: a qualitative study exploring the needs of users. JMIR Human Factors, 2022 Mar 15;9(1):e31021. https://doi.org/10.2196/31021
Duell J, Seisenberger M, Aarts G, Zhou SM, Fan X (2022). Towards a Shapley Value Graph Framework for Medical Peer-influence. arXiv:2112.14624 [cs.AI], 2022. https://doi.org/10.48550/arXiv.2112.14624
Giorgi I, Tirotto FA, Hagen O, Aider F, Gianni M, Palomino M & Masala GL (2022). Friendly But Faulty: A Pilot Study on the Perceived Trust of Older Adults in a Social Robot. IEEE Access 10, 92084-92096. https://doi.org/10.1109/access.2022.3202942
Haynes C, Palomino MA, Stuart L, Viira D, Hannon F, Crossingham G & Tantam K (2022) Automatic Classification of National Health Service Feedback. Mathematics 10, (6). https://doi.org/10.3390/math10060983
Huo L, Bai L, Zhou SM (2022). Automatically Generating Natural Language Descriptions of Images by A Deep Hierarchical Framework. IEEE Transactions on Cybernetics, 52(8),7441-7452. https://doi.org/10.1109/tcyb.2020.3041595
Khan A, Milne-Ives M, Meinert E, Iyawa GE, Jones RB & Josephraj AN (2022). A Scoping Review of Digital Twins in the Context of the Covid-19 Pandemic. Biomedical Engineering and Computational Biology 13:11795972221102115. https://doi.org/10.1177/11795972221102115
Milne-Ives M, Fraser L, Khan A, Walker D, van Velthoven MH, May J, Wolfe I, Harding T & Meinert E (2022). Life.course digital T.wins – I.ntelligent M.onitoring for E.arly and continuous intervention and prevention (LifeTIME): Proposal for a proof-of-concept study. JMIR Research Protocols 11 (5). https://doi.org/10.2196/35738
Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E. (2022). Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digit Health. 15;1(8): e0000079. https://doi.org/10.1371/journal.pdig.0000079
Miranda E & Siegelwax BN (2022). Teaching Qubits to Sing: Mission Impossible? International Journal of Unconventional Computing 17, (4) 303-331. https://www.oldcitypublishing.com/journals/ijuc-home/ijuc-issue-contents/ijuc-volume-17-number-4-2022/
Miranda E, Venkatesh S, Martin-Guerrero JD, Hernani-Morales C, Lamata L & Solano E (2022) An approach to interfacing the brain with quantum computers: practical steps and caveats. International Journal of Unconventional Computing 17, (3). http://www.oldcitypublishing.com/wp-content/uploads/2022/04/IJUCv17n3p159-171Miranda.pdf
Miranda ER, Martín-Guerrero JD, Venkatesh S, Hernani-Morales C, Lamata L & Solano E (2022) Quantum Brain Networks: A Perspective. Electronics 11, (10). https://doi.org/10.3390/electronics11101528
Palomino MA & Aider F (2022). Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis. Applied Sciences 12, (17) 8765-8765. https://doi.org/10.3390/app12178765
Sankaran G, Palomino MA, Knahl M & Siestrup G (2022). A Modeling Approach for Measuring the Performance of a Human-AI Collaborative Process. 1212Applied Sciences 12, (22) 11642-11642. https://doi.org/10.3390/app122211642
Thurston MDV, Kim DH, Wit HK (2022). Neural Network Detection of Pacemakers for MRI Safety. J Digit Imaging. Dec;35(6):1673-1680. https://doi.org/10.1007/s10278-022-00663-2
Venkatesh S, Miranda ER & Braund E (2022) SSVEP-based Brain-computer Interface for Music using a Low-density EEG System. Assistive Technology. https://doi.org/10.1080/10400435.2022.2084182
Venkatesh S, Moffat D & Miranda ER (2022). You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection. Applied Sciences 12 (7). https://doi.org/10.3390/app12073293
Zhou SM, R. A. Lyons, M. A Rahman, A. Holborow, S. Brophy (2022). Predicting hospital readmission for campylobacteriosis from electronic health records: A machine learning and text mining perspective. Journal of Personalized Medicine, 2022, 12(1), 86. https://doi.org/10.3390/jpm12010086

2021

Almowil Z, Zhou SM and Brophy S. (2021). Concept Libraries for Automatic Electronic Health Record Based Phenotyping: A Review. International Journal of Population Data Science, Jun 16;6(1):1362. https://doi.org/10.23889/ijpds.v5i1.1362
Alobaidi H, Clarke N, Li F & Abdulrahman Alruban (2021) Real-World Smartphone-based Gait Recognition. Computers and Security 113. https://doi.org/10.1016/j.cose.2021.102557
Alotaibi, F.G., Clarke, N. and Furnell, S.M. (2021). A novel approach for improving information security management and awareness for home environments. Information and Computer Security, Vol. 29( No. 1)), pp. 25-48. https://doi.org/10.1108/ICS-05-2020-0073
de Pennington N, Mole G, Lim E, Milne-Ives M, Normando E, Xue K, Meinert E. (2021). Safety and Acceptability of a Natural Language Artificial Intelligence Assistant to Deliver Clinical Follow-up to Cataract Surgery Patients: Proposal. JMIR Res Protoc. Jul 28;10(7):e27227. https://doi.org/10.2196/27227
Eke CS, Jammeh E, Li X, Carroll C, Pearson S & Ifeachor E (2021). Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines. IEEE Journal of Biomedical and Health Informatics 25, (1) 218-226. https://doi.org/10.1109/jbhi.2020.2984355
Fernández-Gutiérrez F, Kennedy JI, Cooksey R, Atkinson M, Choy E, Brophy S, Huo L, Zhou SM (2021). Mining primary care electronic health records for automatic disease phenotyping: A transparent machine learning framework. Diagnostics, 2021, 11, 1908. https://doi.org/10.3390/diagnostics11101908
Kim DH, Wit H, Thurston M, Long M, Maskell GF, Strugnell MJ, Shetty D, Smith IM, Hollings NP (2021). An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs. Br J Radiol. 1;94(1122):20201407. https://doi.org/10.1259/bjr.20201407
Miranda ER (2021) Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity. Springer Nature. https://doi.org/10.1007/978-3-030-72116-9
Morgan K, Zhou SM, R. Hill, R. Lyons, S. Paranjothy and S. Brophy (2021). Identifying prenatal and postnatal determinants of infant growth: A structural equation modelling based cohort analysis. International Journal of Environmental Research and Public Health 18, 10265. https://doi.org/10.3390/ijerph181910265
Raj ANJ, Zhu H, Khan A, Zhuang Z, Yang Z, Mahesh VGV & Karthik G (2021). ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans. Peer J Computer Science 7. https://doi.org/10.7717/peerj-cs.349
Surodina S, Lam C, Grbich S, Milne-Ives M, van Velthoven M & Meinert E (2021). Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study. JMIRx Med 2, (2). https://doi.org/10.2196/25560
Surodina S, Lam C, Grbich S, Milne-Ives M, van Velthoven M, Meinert E (2021) Authors’ Response to Peer Reviews of “Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study” JMIRX MED Vol.2 Iss.2 10.2196/28917. https://doi.org/10.2196/28917
Surodina S, Lam C, Grbich S, Milne-Ives M, van Velthoven M, Meinert E (2021) Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study JMIRX MED Vol.2 Iss.2 10.2196/25560. https://doi.org/10.2196/25560
Tsang G, Zhou SM, Xie X. (2021). Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records. In IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-13. https://doi.org/10.1109/JTEHM.2020.3040236
West C, Woldman W, Oak K, McLean B & Shankar R (2021). A Review of Network and Computer Analysis of Epileptiform Discharge Free EEG to Characterize and Detect Epilepsy. Clinical EEG and Neuroscience 53, (1) 74-78. https://doi.org/10.1177/15500594211008285
Zhou, SM, F. Chiclana, R. I. John, J. M. Garibaldi, and L. Huo (2021), Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Application in Integrated Diagnosis. IEEE Transactions on Fuzzy Systems,.,., vol.29((, no.8):):, pp. 2112-2121. https://doi.org/10.1109/tfuzz.2020.2992909

2020

Alotaibi F, Clarke N & Furnell S (2020). A novel approach for improving information security management and awareness for home environments. Information and Computer Security, 113, 102557. https://doi.org/10.1108/ICS-05-2020-0073
Babiloni C, Blinowska K, Bonanni L, Cichocki A, De Haan W, Del Percio C, Dubois B, Escudero J, Fernández A & Frisoni G (2020). What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons. Neurobiology of Aging 85, 58-73. https://doi.org/10.1016/j.neurobiolaging.2019.09.008
Barakabitze AA, Mkwawa I-H, Hines A, Sun L & Ifeachor E (2020). QoEMultiSDN: Management of Multimedia Services using MPTCP/SR in Softwarized and Virtualized Networks. IEEE Access 1-1. https://doi.org/10.1109/access.2020.3039953
Blesse C, Kharko A, Locher C, DesRoches CM, Mandl KD (2020) US primary care in 2029: A Delphi survey on the impact of machine learning PLOS ONE Vol.15 Iss.10 10.1371/journal.pone.0239947. https://doi.org/10.1371/journal.pone.0239947
Bradwell HL, Johnson CW, Lee J, Winnington R, Thill S & Jones RB (2020) Microbial contamination and efficacy of disinfection procedures of companion robots in care homes. PLOS ONE 15, (8) e0237069-e0237069. https://doi.org/10.1371/journal.pone.0237069
Bradwell HL, Winnington R, Thill S, Jones RB (2020). Longitudinal diary data: six months real-world implementation of affordable companion robots for older people in supported living. ACM/IEEE INT CONF ON HUMAN-ROBOT INTERACTION. https://doi.org/10.1145/3371382.3378256
de Cock C, Milne-Ives M, van Velthoven MH, Alturkistani A, Lam C & Meinert E (2020) Effectiveness of Conversational Agents (Virtual Assistants) in Health Care: Protocol for a Systematic Review. JMIR Research Protocols 9, (3) e16934-e16934. https://doi.org/10.2196/16934
Gianni, M. Uddin, M. S. (2020). Role and task allocation framework for Multi-Robot Collaboration with latent knowledge estimation. Engineering Reports. John Wiley & Sons Ltd. https://doi.org/10.1002/eng2.12225
Meinert E, Lam C, van Velthoven M (2020) Developing a blockchain-based supply chain system for advanced therapies: study protocol. JMIR Research Protocols Vol.9 Iss.12 10.2196/17005. https://doi.org/10.2196/17005
Milne-Ives M, de Cock C, Lim E, Shehadeh M, de Pennington N, Mole G & Meinert E (2020) The effectiveness of artificial intelligence conversational agents in healthcare: a systematic review. Journal of Medical Internet Research 22, (10), Iss.10 10.2196/20346. https://doi.org/10.2196/20346
Miranda E, Daly I, Nicolaou N, Williams D, Hwang F, Kirke A & Nasuto SJ (2020) Neural and physiological data from participants listening to affective music. Scientific Data 7, (1). https://doi.org/10.1038/s41597-020-0507-6
Nilashi M, Ahmadi H, Manaf AA, Rashid TA, Samad S, Shahmoradi L, Aljojo N, Akbari E. (2020) Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates. International Journal of Fuzzy Systems, 2, 1376–1388. https://doi.org/10.1007/s40815-020-00828-7
Nilashi M, Ahmadi H, Sheikhtaheri A, Naemi R, Alotaibi R, Alarood AA, Munshi A, Rashid TA, Zhao J. (2020). Remote Tracking of Parkinson's Disease Progression Using Ensembles of Deep Belief Network and Self-Organizing Map. Expert Systems with Applications, 159 113562. https://doi.org/10.1016/j.eswa.2020.113562
Nilashi M, Ahmadi N, Samad S, Shahmoradi L, Ahmadi H, Ibrahim O, Asadi S, Abdullah R, Abumalloh RA, Yadegari E. (2020). Disease Diagnosis Using Machine Learning Techniques: A Review and Classification, Journal of Soft Computing and Decision Support Systems, 7(1). https://www.jscdss.com/index.php/files/article/view/224
Tsang, G., Xie, X., Zhou, S.-M. (2020). Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges. IEEE Reviews in Biomedical Engineering, 13: 113-129. https://doi.org/10.1109/rbme.2019.2904488
Yunus J, Ifeachor E & Edworthy JE (2020). Improving Alarm Response in ICU/CCU. International Journal of Simulation Systems Science & Technology. http://doi.org/10.5013/IJSSST.a.16.04.01

Funders and collaborators