Multivariate modeling to identify patterns in clinical data: the example of chest pain

<p>Abstract</p> <p>Background</p> <p>In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.</p> <p>Methods</p> <p>We conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. A total of 1199 patients above age 35 presenting with chest pain were included in the study. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient's chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.</p> <p>Results</p> <p>A multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: „chest wall syndrome“, „vital threat“, „stomach and bowel pain“, „angina pectoris“, „chest infection syndrome“, and „ self-limiting chest pain“. MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.</p> <p>Conclusions</p> <p>Chest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level.</p

Dual-tasking and gait in people with Mild Cognitive Impairment. The effect of working memory

<p>Abstract</p> <p>Background</p> <p>Cognition and mobility in older adults are closely associated and they decline together with aging. Studies evaluating associations between cognitive factors and gait performance in people with Mild Cognitive Impairment (MCI) are scarce. In this study, our aim was to determine whether specific cognitive factors have a more identifiable effect on gait velocity during dual-tasking in people with MCI.</p> <p>Methods</p> <p>Fifty-five participants, mean age 77.7 (SD = 5.9), 45% women, with MCI were evaluated for global cognition, working memory, executive function, and attention. Gait Velocity (GV) was measured under a single-task condition (single GV) and under two dual-task conditions: 1) while counting backwards (counting GV), 2) while naming animals (verbal GV). Multivariable linear regression analysis was used to examine associations with an alpha-level of 0.05.</p> <p>Results</p> <p>Participants experienced a reduction in GV while engaging in dual-task challenges (p < 0.005). Low executive function and working memory performances were associated with slow single GV (p = 0.038), slow counting GV (p = 0.017), and slow verbal GV (p = 0.031). After adjustments, working memory was the only cognitive factor which remained significantly associated with a slow GV.</p> <p>Conclusion</p> <p>In older adults with MCI, low working memory performance was associated with slow GV. Dual-task conditions showed the strongest associations with gait slowing. Our findings suggest that cortical control of gait is associated with decline in working memory in people with MCI.</p

Prevalence of physical and verbal aggressive behaviours and associated factors among older adults in long-term care facilities

BACKGROUND: Verbal and physical aggressive behaviours are among the most disturbing and distressing behaviours displayed by older patients in long-term care facilities. Aggressive behaviour (AB) is often the reason for using physical or chemical restraints with nursing home residents and is a major concern for caregivers. AB is associated with increased health care costs due to staff turnover and absenteeism. METHODS: The goals of this secondary analysis of a cross-sectional study are to determine the prevalence of verbal and physical aggressive behaviours and to identify associated factors among older adults in long-term care facilities in the Quebec City area (n = 2 332). RESULTS: The same percentage of older adults displayed physical aggressive behaviour (21.2%) or verbal aggressive behaviour (21.5%), whereas 11.2% displayed both types of aggressive behaviour. Factors associated with aggressive behaviour (both verbal and physical) were male gender, neuroleptic drug use, mild and severe cognitive impairment, insomnia, psychological distress, and physical restraints. Factors associated with physical aggressive behaviour were older age, male gender, neuroleptic drug use, mild or severe cognitive impairment, insomnia and psychological distress. Finally, factors associated with verbal aggressive behaviour were benzodiazepine and neuroleptic drug use, functional dependency, mild or severe cognitive impairment and insomnia. CONCLUSION: Cognitive impairment severity is the most significant predisposing factor for aggressive behaviour among older adults in long-term care facilities in the Quebec City area. Physical and chemical restraints were also significantly associated with AB. Based on these results, we suggest that caregivers should provide care to older adults with AB using approaches such as the progressively lowered stress threshold model and reactance theory which stress the importance of paying attention to the severity of cognitive impairment and avoiding the use of chemical or physical restraints

Automatic Neural Network Architecture Optimization

Deep learning has recently become a very hot topic in Computer Science. It has invaded many applications in Computer Science achieving exceptional performances compared to other existing methods. However, neural networks have a strong memory limitation which is considered to be one of its main challenges. This is why remarkable research focus is recently directed towards model compression. This thesis studies a divide-and-conquer approach that transforms an existing trained neural network into another network with less number of parameters with the target of decrasing its memory footprint. It takes into account the resulting loss in performance. It is based on existing layer transformation techniques like Canonical Polyadic (CP) and SVD affine transformations. Given an artificial neural network, trained on a certain dataset, an agent optimizes the architecture of the neural network in a bottom-up man- ner. It cuts the network in sub-networks of length 1. It optimizes each sub-network using layer transformations. Then it chooses the most- promising sub-networks to construct sub-networks of length 2. This process is repeated until it constructs an artificial neural network that covers the functionalities of the original neural network. This thesis offers an extensive analysis of the proposed approach. We tested this tech- nique with different known neural network architectures with popular datasets. We could outperform recent techniques in both the compression rate and network perfor- mance on LeNet5 with MNIST. We could compress ResNet-20 to 25% of their original size achieving performance comparable with networks in the literature with double this size