Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking into consideration that stakeholders attempting to optimise the dementia care pathway might face multiple non-fungible budget constraints. Our new optimisation algorithm involved the searching of cost-weighting hyperparameters while constrained by total budgets. We then provided a proof of concept using both assessment time cost and financial budget cost. We showed that budget constraints could control the feature selection process in an intuitive and practical manner, while adjusting the hyperparameter increased the range of solutions selected by feature selection. We further showed that our budget-constrained cost optimisation framework could be implemented in a user-friendly graphical user interface sandbox tool to encourage non-technical users and stakeholders to adopt and to further explore and audit the model - a humans-in-the-loop approach. Overall, we suggest that setting budget constraints initially and then fine tuning the cost-weighting hyperparameters can be an effective way to perform feature selection where multiple cost constraints exist, which will in turn lead to more realistic optimising and redesigning of dementia diagnostic assessments. Clinical Relevance-By optimising diagnostic accuracy against various costs (e.g. assessment administration time and financial budget) predictive yet practical dementia diagnostic assessments can be redesigned to suit clinical use.
|Title of host publication||2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)|
|Number of pages||7|
|Publication status||Published (in print/issue) - Jul 2022|
|Event||The 44th International Engineering in Medicine and Biology Conference (EMBC) - Glasgow, United Kingdom|
Duration: 11 Jul 2022 → 15 Jul 2022
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||The 44th International Engineering in Medicine and Biology Conference (EMBC)|
|Period||11/07/22 → 15/07/22|
Bibliographical noteFunding Information:
This work was supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB) (Centre for Personalised Medicine, IVA 5036)), and additional support by Alzheimer’s Research UK (ARUK) NI Pump Priming (XD, ST, PLM, KW-L) and Ulster University Research Challenge Fund (XD, ST, PLM, KW-L). The views and opinions expressed in this paper do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB). See Acknowledgment for data support. NM+, XD, GP and KW-L+ are with the Intelligent Systems Research Centre, Ulster University, Magee campus, Derry Londonderry, Northern Ireland (NI), UK. DPF is with Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre and Centre for Pain Research, National University of Ireland, Galway, Ireland. ST is with Altnagelvin Area Hospital,
DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research Development, LLC.; Johnson Johnson Pharmaceutical Research Development LLC.; Lumosity; Lundbeck; Merck Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and
© 2022 IEEE.
- Process control
- Machine learning
- Feature extraction
- Medical diagnosis