List of Titles

Machine learning in mental health: a scoping review of methods and applications

- Shatte, Adrian, Hutchinson, Delyse, Teague, Samantha

Authors: Shatte, Adrian , Hutchinson, Delyse , Teague, Samantha
Date: 2019
Type: Text , Journal article
Relation: Psychological Medicine Vol. 49, no. 9 (2019), p. 1426-1448
Full Text: false
Reviewed:
Description: This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health, and (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Effect of navigation problems, assessment location, and a practice test on driving assessment performance for people with alzheimer's disease

- Unsworth, Carolyn, Russell, Kay, Lovell, Robin, Woodward, Michael, Browne, Matthew

Authors: Unsworth, Carolyn , Russell, Kay , Lovell, Robin , Woodward, Michael , Browne, Matthew
Date: 2019
Type: Text , Journal article
Relation: Journal of Alzheimer's Disease Vol. 67, no. 3 (2019), p. 1035-1043
Full Text: false
Reviewed:
Description: Background: People with Alzheimer's disease may be required to undertake clinical and on-road assessments to determine fitness to drive. The manner in which on-road assessments are conducted with drivers who do and do not have navigational problems may affect the outcome. Objectives: Investigate the effect of 1) navigational difficulties, 2) location of assessment (un/familiar area) and assessment order, and 3) undertaking a second assessment (practice), on passing an on-road driving assessment. Methods: Forty-three drivers undertook an Occupational Therapy-Driver Assessment Off Road Assessment (OT-DORA) Battery which included the Drive Home Maze Test (DHMT). Participants with/without a history of navigational problems were randomly allocated into three groups: 1) Unfamiliar/then familiar area assessment; 2) Unfamiliar/unfamiliar; 3) familiar/unfamiliar. An on-road assessment protocol was used including over 100 expected behaviors at nominated points along the directed route. For familiar area assessments, the driver self-navigated from their home to shops and services. A pass/fail decision was made for each assessment. Results: A generalized linear mixed effects model showed neither location, nor practice affected passing the on-road assessment. Participants with navigational problems were six times less likely to pass regardless of route familiarity and direction method, and the DHMT was a significant negative predictor of passing. Conclusion: Drivers with Alzheimer's disease who have navigational problems and are slow to complete the DHMT are unlikely to pass an on-road assessment. However, navigation and maze completion skills may be a proxy for an underlying cognitive skill underpinning driving performance. © 2019 - IOS Press and the authors. All rights reserved.

Investigating what works to support family carers of people with dementia: a rapid realist review

- Parkinson, Mark, Carr, Susan, Rushmer, Rosemary, Abley, Clare

Authors: Parkinson, Mark , Carr, Susan , Rushmer, Rosemary , Abley, Clare
Date: 2017
Type: Text , Journal article
Relation: Journal of Public Health Vol. 39, no. 4 (2017), p. e290-e301
Full Text: false
Reviewed:
Description: Advances in longevity and medicine mean that many more people in the UK survive life-threatening diseases but are instead susceptible to life-limiting diseases such as dementia. Within the next 10 years those affected by dementia in the UK is set to rise to over 1 million, making reliance on family care of people with dementia (PWD) essential. A central challenge is how to improve family carer support to offset the demands made by dementia care which can jeopardise carers' own health. This review investigates 'what works to support family carers of PWD'. Rapid realist review of a comprehensive range of databases. Five key themes emerged: (1) extending social assets, (2) strengthening key psychological resources, (3) maintaining physical health status, (4) safeguarding quality of life and (5) ensuring timely availability of key external resources. It is hypothesized that these five factors combine and interact to provide critical biopsychosocial and service support that bolsters carer 'resilience' and supports the maintenance and sustenance of family care of PWD. 'Resilience-building' is central to 'what works to support family carers of PWD'. The resulting model and Programme Theories respond to the burgeoning need for a coherent approach to carer support.

Stage-aware brain graph learning for Alzheimer's disease

- Peng, Ciyuan, Liu, Mujie, Meng, Chenxuan, Xue, Sha, Keogh, Kathleen, Xia, Feng

Authors: Peng, Ciyuan , Liu, Mujie , Meng, Chenxuan , Xue, Sha , Keogh, Kathleen , Xia, Feng
Date: 2024
Type: Text , Conference paper
Relation: 2nd IEEE Conference on Artificial Intelligence, CAI 2024, Singapore, 25-27 June 2024, Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024 p. 1346-1349
Full Text: false
Reviewed:
Description: Current machine learning-based Alzheimer's disease (AD) diagnosis methods fail to explore the distinctive brain patterns across different AD stages, lacking the ability to trace the trajectory of AD progression. This limitation can lead to an oversight of the pathological mechanisms of AD and suboptimal performance in AD diagnosis. To overcome this challenge, this paper proposes a novel stage-aware brain graph learning model. Particularly, we analyze the different brain patterns of each AD stage in terms of stage-specific brain graphs. We design a Stage Feature-enhanced Graph Contrastive Learning method, named SF-GCL, utilizing specific features within each AD stage to perform graph augmentation, thereby effectively capturing differences between stages. Significantly, this study unveils the specific brain patterns corresponding to each AD stage, showing great potential in tracing the trajectory of brain degeneration. Experimental results on a real-world dataset demonstrate the superiority of our model. © 2024 IEEE.

Showing items 1 - 4 of 4