Use of machine learning technology in the diagnosis of Alzheimer’s disease

Alzheimer’s disease (AD) is thought to be the most common cause of dementia and it is estimated that only 1-in-4 people with Alzheimer’s are correctly diagnosed in a timely fashion. While no definitive cure is available, when the impairment is still mild the symptoms can be managed and treatment is most effective when it is started before significant downstream damage occurs, i.e., at the stage of mild cognitive impairment (MCI) or even earlier. AD is clinically diagnosed by physical and neurological examination, and through neuropsychological and cognitive tests. There is a need to develop better diagnostic tools, which is what this thesis addresses. Dublin City University School of Nursing and Human Sciences runs a memory clinic, Memory Works where subjects concerned about possible dementia come to seek clarity. Data collected at interview is recorded and one aim of the work in this thesis is to explore the use of machine learning techniques to generate a classifier that can assist in screening new individuals for different stages of AD. However, initial analysis of the features stored in the Memory Works database indicated that there is an insufficient number of instances available (about 120 at this time) to train a machine learning model to accurately predict AD stage on new test cases. The National Azheimers Cordinating Center (NACC) in the U.S collects data from National Institute for Aging (NIA)-funded Alzheimer’s Disease Centers (ADCs) and maintains a large database of standardized clinical and neuropathological research data from these ADCs. NACC data are freely available to researchers and we have been given access to 105,000 records from the NACC. We propose to use this dataset to test the hypothesis that a machine learning classifier can be generated to predict the dementia status for new, previously unseen subjects. We will also, by experiment, establish both the minimum number of instances required and the most important features from assessment interviews, to use for this prediction

Automating the integration of clinical studies into medical ontologies

A popular approach to knowledge extraction from clinical databases is to first define an ontology of the concepts one wishes to model and subsequently, use these concepts to test various hypotheses and make predictions about a person’s future health and wellbeing. The challenge for medical experts is in the time taken to map between their concepts/hypotheses and information contained within clinical studies. Presently, most of this work is performed manually. We have developed a method to generate links between Risk Factors in a medical ontology and the questions and result data in longitudinal studies. This can then be exploited to express complex queries based on domain concepts, to extract knowledge from external studies

Systematic review of interventions to improve patient uptake and completion of pulmonary rehabilitation in COPD

ABSTRACT Pulmonary rehabilitation is considered a key management strategy for chronic obstructive pulmonary disease (COPD), but its effectiveness is undermined by poor patient uptake and completion. The aim of this review was to identify, select and synthesise the available evidence on interventions for improving uptake and completion of pulmonary rehabilitation in COPD. Electronic databases and trial registers were searched for randomised trials evaluating the effect of an intervention compared with a concurrent control group on patient uptake and completion. The primary outcomes were the number of participants who attended a baseline assessment and at least one session of pulmonary rehabilitation (uptake), and the number of participants who received a discharge assessment (completion). Only one quasi-randomised study (n=115) (of 2468 records identified) met the review inclusion criteria and was assessed as having a high risk of bias. The point estimate of effect did, however, indicate greater programme completion and attendance rates in participants allocated to pulmonary rehabilitation plus a tablet computer (enabled with support for exercise training) compared with controls ( pulmonary rehabilitation only). There is insufficient evidence to guide clinical practice on interventions for improving patient uptake and completion of pulmonary rehabilitation in COPD. Despite increasing awareness of patient barriers to pulmonary rehabilitation, our review highlights the existing under-appreciation of interventional trials in this area. This knowledge gap should be viewed as an area of research priority due to its likely impact in undermining wider implementation of pulmonary rehabilitation and restricting patient access to a treatment considered the cornerstone of COPD

Mapping longitudinal studies to risk factors in an ontology for dementia

A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The In-MINDD project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalised dementia risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study (MAAS) dataset. As part of this overall goal, the research presented in this paper demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus, use information technology to provide more powerful query interfaces

An evaluation of a virtual COVID-19 ward to accelerate the supported discharge of patients from an acute hospital setting

open access articleBackground/Aims In response to high numbers of hospital admissions as a result of COVID-19, a virtual ward was implemented to achieve accelerated discharge from hospital without compromising patient safety. This study assessed the impact of this virtual ward for patients admitted to the acute hospital setting with COVID-19. Methods A community-based intervention using digital technology and a multi‑disciplinary team of specialist clinicians to monitor patients at home was established. An analysis was carried out within the service investigating the safety, health outcomes and resource use of the first 65 patients discharged from hospital into the virtual respiratory ward. Results Red days, where an urgent response was required, decreased from 33.8% of patients in their first 3 days at the virtual ward to 10.8% in their final 3 days (P=0.002). Four patients were readmitted to hospital, all for clotting disorders. There was one death, which was deemed unrelated to COVID-19. Length of stay was also reduced by 40.3% (P<0.001) and estimated overall savings were £68 052 (£1047 per patient). Conclusions The virtual ward appeared to assist with earlier discharges, had a low rate of clinically necessary re-admissions, and seemed to reduce costs without compromising patient safety. The authors believe that this intervention could be applied across other NHS trusts facing similar capacity issues as a result of COVID-19

Systematic review of interventions to improve patient uptake and completion of pulmonary rehabilitation in COPD

ABSTRACT Pulmonary rehabilitation is considered a key management strategy for chronic obstructive pulmonary disease (COPD), but its effectiveness is undermined by poor patient uptake and completion. The aim of this review was to identify, select and synthesise the available evidence on interventions for improving uptake and completion of pulmonary rehabilitation in COPD. Electronic databases and trial registers were searched for randomised trials evaluating the effect of an intervention compared with a concurrent control group on patient uptake and completion. The primary outcomes were the number of participants who attended a baseline assessment and at least one session of pulmonary rehabilitation (uptake), and the number of participants who received a discharge assessment (completion). Only one quasi-randomised study (n=115) (of 2468 records identified) met the review inclusion criteria and was assessed as having a high risk of bias. The point estimate of effect did, however, indicate greater programme completion and attendance rates in participants allocated to pulmonary rehabilitation plus a tablet computer (enabled with support for exercise training) compared with controls ( pulmonary rehabilitation only). There is insufficient evidence to guide clinical practice on interventions for improving patient uptake and completion of pulmonary rehabilitation in COPD. Despite increasing awareness of patient barriers to pulmonary rehabilitation, our review highlights the existing under-appreciation of interventional trials in this area. This knowledge gap should be viewed as an area of research priority due to its likely impact in undermining wider implementation of pulmonary rehabilitation and restricting patient access to a treatment considered the cornerstone of COPD