Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Big data and data repurposing – using existing data to answer new questions in vascular dementia research

Introduction: Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD. Methods: We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015). Results: We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach. Conclusions: There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use

UNDERSTANDING RISK FACTORS OF ELDERLY INPATIENT FALLS USING CONTEXTUAL MODEL

The field of Information Systems is about bridging the digital and information divide. Advances in the digital world enable information to be stored and structured in a manner that facilitates effective use of the information for future modelling purposes. Elderly inpatient falls are a common global phenomenon, and an inpatient fall incident can have severe consequences for the patient, caregivers and the healthcare provider. An inpatient fall can result from many causes and its risk can be increased through the combination of these causes. Many risk factors of elderly inpatient falls have been reported in various papers in the literature. However, a logical comprehensive categorisation of all these factors does not currently exist. The objective of this research in progress is to come up with a generic categorisation of the risk factors for elderly inpatient falls alongside the usage of a contextual model to illustrate the inherent interactions amongst these various factors. In addition, we found that the effect of the interaction amongst some risk factors is time dependent which also needs to be incorporated in the contextual model. Such comprehensive categorisation and contextual risk model will help health providers in the process of profiling of an elderly inpatient with respect to his/her fall risk. It is useful to experts in health informatics in formulating models to automate this process

The increased risk of mortality in elderly patients with epilepsy and dementias

Studies have shown that individuals with Alzheimer’s disease or related dementias have increased risk of developing seizures. Alzheimer’s disease is the most prevalent form of dementia affecting millions of individuals across the nation and as we this number continues the rise, we suspect that cases of seizures in elderly patients is on the rise as well. Although there are advanced neuroimaging techniques that expand our current understanding of neural processes and interplay between neurological diseases, we are still limited in our insight into the causes and progression of Alzheimer’s disease and related Dementias (ADRD) and epilepsy. We are aware that elderly patients with ADRD and epilepsy deteriorate neurologically but it also gives rise to a significant public health issue; patients with ADRD and seizures suffer from social, financial and health restrictions as well. Overall, there is scarce evidence identifying the impact of having comorbid seizures and ADRD. In this study, we performed a retrospective cohort study comparing the 5-years mortality risk of patients with both seizures and dementia to patients with other neurological conditions. Patient data was retrieved from Research Patient Data Registry query and a subsample was selected for medical records abstraction using an ICD-9 code for “epilepsy” (345.xx), or “convulsions” (780.3x), or “collapse” (780.2x) from 2006-2013 and one claim for ADRD (331.x) within 2006-2013. Our results indicated that the rate of mortality is higher among patients with a history of seizures and ADRD when compared to patients seen in neurology for other conditions. While previous studies have indicated the increased risk of seizure development in elderly patients with ADRD, they did not examine mortality rates. These results amplify the need to careful examination of elderly patients who are at risk and can improve the quality of care they receive