Broad Learning for Healthcare

A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital footprints recorded on smart sensors). Capability for modeling information from these heterogeneous data sources is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Our works in this thesis attempt to facilitate healthcare applications in the setting of broad learning which focuses on fusing heterogeneous data sources for a variety of synergistic knowledge discovery and machine learning tasks. We are generally interested in computer-aided diagnosis, precision medicine, and mobile health by creating accurate user profiles which include important biomarkers, brain connectivity patterns, and latent representations. In particular, our works involve four different data mining problems with application to the healthcare domain: multi-view feature selection, subgraph pattern mining, brain network embedding, and multi-view sequence prediction.Comment: PhD Thesis, University of Illinois at Chicago, March 201

Principles of sound assessment practice in Health Professions Education

In the broad sense, assessment is a systematic method of obtaining information or sampling data about teaching and learning in order to make specific inferences about characteristics which reflect student learning and skills acquisition. Assessment in Health Professions Education is vital as it impacts on healthcare delivery outcomes for patients and must be based on sound research evidence [1,2]. For any assessment method to be successfully used as an instrument of competency measurement, standard setting and blue printing must be done in advance [3]

Power and Energy Optimized Approach towards Sustainable Mobile Ad-hoc Networks and IoT

Investigating how real-time applications in sectors like healthcare, agriculture, construction, and manufacturing can enhance their effectiveness and sustainability through the use of autonomous sensor technologies, green computing, and big data analytics is part of the work with sustainable approaches for optimising performance of networks. This authoritative guide exposes the drawbacks of conventional technology and provides techniques and tactics for addressing Quality of Service (QOS) issues and enhancing network performance. It brings together a broad team of subject-matter specialists. Several in-depth chapters cover topics like blockchain-assisted secure data sharing, intelligent management of ad hoc networks, smart 5G Internet of Things scenarios, and the use of artificial intelligence (AI), machine learning (ML), and learning techniques (DL) techniques in smart healthcare, smart factory, and smart agriculture

Capacity to consent to healthcare in adults with intellectual disabilities

Section A explores capacity to consent to healthcare in adults with an intellectual disability in a broad context. It examines the legal understanding of capacity to consent as defined by the Mental Capacity Act (2005), before going on to use decision-making theory as a framework for exploring the psychological understanding of capacity to consent. It then examines the empirical literature on what influences capacity to consent to healthcare interventions and research in people with an intellectual disability, highlighting what further research is needed. Section B reports an empirical study, which follows up on some of the further research suggested by Section A. Background: Capacity to consent has been identified as one of the significant barriers to healthcare faced by people with intellectual disabilities. In order to improve understanding, the literature has attempted to investigate factors that influence capacity to consent to healthcare. Materials and Method: This study had 32 participants with learning disabilities, 22 carers and 3 nurse participants. It examined the correlations between verbal ability, decision-making opportunities and previous health experience, with capacity to consent to healthcare in people with learning disabilities, before exploring a regression model to show how the factors interacted. Results: Previous health experience and verbal ability significantly positively correlated with capacity to consent, whilst the correlation with decision-making opportunities was almost significant. However, the regression model showed that only verbal ability was a significant predictor. Conclusion: The study reveals the importance of looking at how factors that influence capacity to consent to healthcare interact with each other, rather than just acting individually. Further research is required to expand this model to include other variables. Section C provides a critical appraisal for the whole project, exploring what was learnt and what could have been improved on, as well as considering the implications for clinical practice and further research

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients.

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition;emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; andrecognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time