Machine Learning and Statistical Analysis of Complex Mathematical Models: An Application to Epilepsy

The electroencephalogram (EEG) is a commonly used tool for studying the emergent electrical rhythms of the brain. It has wide utility in psychology, as well as bringing a useful diagnostic aid for neurological conditions such as epilepsy. It is of growing importance to better understand the emergence of these electrical rhythms and, in the case of diagnosis of neurological conditions, to find mechanistic differences between healthy individuals and those with a disease. Mathematical models are an important tool that offer the potential to reveal these otherwise hidden mechanisms. In particular Neural Mass Models (NMMs), which describe the macroscopic activity of large populations of neurons, are increasingly used to uncover large-scale mechanisms of brain rhythms in both health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite they are considered low-dimensional in comparison to micro-scale neural network models, with regards to understanding the relationship between parameters and dynamics NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. We need alternative methods to characterise the dynamics of NMMs in high dimensional parameter spaces. The primary aim of this thesis is to develop a method to explore and analyse the high dimensional parameter space of these mathematical models. We develop an approach based on statistics and machine learning methods called decision tree mapping (DTM). This method is used to analyse the parameter space of a mathematical model by studying all the parameters simultaneously. With this approach, the parameter space can efficiently be mapped in high dimension. We have used measures linked with this method to determine which parameters play a key role in the output of the model. This approach recursively splits the parameter space into smaller subspaces with an increasing homogeneity of dynamics. The concepts of decision tree learning, random forest, measures of importance, statistical tests and visual tools are introduced to explore and analyse the parameter space. We introduce formally the theoretical background and the methods with examples. The DTM approach is used in three distinct studies to: • Identify the role of parameters on the dynamic model. For example, which parameters have a role in the emergence of seizure dynamics? • Constrain the parameter space, such that regions of the parameter space which give implausible dynamic are removed. • Compare the parameter sets to fit different groups. How does the thalamocortical connectivity of people with and without epilepsy differ? We demonstrate that classical studies have not taken into account the complexity of the parameter space. DTM can easily be extended to other fields using mathematical models. We advocate the use of this method in the future to constrain high dimensional parameter spaces in order to enable more efficient, person-specific model calibration

Impact of STROBE Statement Publication on Quality of Observational Study Reporting: Interrupted Time Series versus Before-After Analysis

Background:In uncontrolled before-after studies, CONSORT was shown to improve the reporting of randomised trials. Before-after studies ignore underlying secular trends and may overestimate the impact of interventions. Our aim was to assess the impact of the 2007 STROBE statement publication on the quality of observational study reporting, using both uncontrolled before-after analyses and interrupted time series.Methods:For this quasi-experimental study, original articles reporting cohort, case-control, and cross-sectional studies published between 2004 and 2010 in the four dermatological journals having the highest 5-year impact factors (≥4) were selected. We compared the proportions of STROBE items (STROBE score) adequately reported in each article during three periods, two pre STROBE period (2004-2005 and 2006-2007) and one post STROBE period (2008-2010). Segmented regression analysis of interrupted time series was also performed.Results:Of the 456 included articles, 187 (41%) reported cohort studies, 166 (36.4%) cross-sectional studies, and 103 (22.6%) case-control studies. The median STROBE score was 57% (range, 18%-98%). Before-after analysis evidenced significant STROBE score increases between the two pre-STROBE periods and between the earliest pre-STROBE period and the post-STROBE period (median score2004-0548% versus median score2008-1058%, p<0.001) but not between the immediate pre-STROBE period and the post-STROBE period (median score2006-0758% versus median score2008-1058%, p = 0.42). In the pre STROBE period, the six-monthly mean STROBE score increased significantly, by 1.19% per six-month period (absolute increase 95%CI, 0.26% to 2.11%, p = 0.016). By segmented analysis, no significant changes in STROBE score trends occurred (-0.40%; 95%CI, -2.20 to 1.41; p = 0.64) in the post STROBE statement publication.Interpretation:The quality of reports increased over time but was not affected by STROBE. Our findings raise concerns about the relevance of uncontrolled before-after analysis for estimating the impact of guidelines

Descriptors of Posidonia oceanica meadows: Use and application

The conservation of the coastal marine environment requires the possession of information that enables the global quality of the environment to be evaluated reliably and relatively quickly. The use of biological indicators is often an appropriate method. Seagrasses in general, and Posidonia oceanica meadows in particular, are considered to be appropriate for biomonitoring because of their wide distribution, reasonable size, sedentary habit, easy collection and abundance and sensitivity to modifications of littoral zone. Reasoned management, on the scale of the whole Mediterranean basin, requires standardized methods of study, to be applied by both researchers and administrators, enabling comparable results to be obtained. This paper synthesises the existing methods applied to monitor P. oceanica meadows, identifies the most suitable techniques and suggests future research directions. From the results of a questionnaire, distributed to all the identified laboratories working on this topic, a list of the most commonly used descriptors was drawn up, together with the related research techniques (e.g. standardization, interest and limits, valuation of the results). It seems that the techniques used to study meadows are rather similar, but rarely identical, even though the various teams often refer to previously published works. This paper shows the interest of a practical guide that describes, in a standardized way, the most useful techniques enabling P. oceanica meadows to be used as an environmental descriptor. Indeed, it constitutes the first stage in the process. (c) 2005 Elsevier Ltd. All rights reserved.Peer reviewe

Value of hospital antimicrobial stewardship programs [ASPs]:a systematic review

Abstract Background Hospital antimicrobial stewardship programs (ASPs) aim to promote judicious use of antimicrobials to combat antimicrobial resistance. For ASPs to be developed, adopted, and implemented, an economic value assessment is essential. Few studies demonstrate the cost-effectiveness of ASPs. This systematic review aimed to evaluate the economic and clinical impact of ASPs. Methods An update to the Dik et al. systematic review (2000–2014) was conducted on EMBASE and Medline using PRISMA guidelines. The updated search was limited to primary research studies in English (30 September 2014–31 December 2017) that evaluated patient and/or economic outcomes after implementation of hospital ASPs including length of stay (LOS), antimicrobial use, and total (including operational and implementation) costs. Results One hundred forty-six studies meeting inclusion criteria were included. The majority of these studies were conducted within the last 5 years in North America (49%), Europe (25%), and Asia (14%), with few studies conducted in Africa (3%), South America (3%), and Australia (3%). Most studies were conducted in hospitals with 500–1000 beds and evaluated LOS and change in antibiotic expenditure, the majority of which showed a decrease in LOS (85%) and antibiotic expenditure (92%). The mean cost-savings varied by hospital size and region after implementation of ASPs. Average cost savings in US studies were $732 per patient (range:$2.50 to $2640), with similar trends exhibited in European studies. The key driver of cost savings was from reduction in LOS. Savings were higher among hospitals with comprehensive ASPs which included therapy review and antibiotic restrictions. Conclusions Our data indicates that hospital ASPs have significant value with beneficial clinical and economic impacts. More robust published data is required in terms of implementation, LOS, and overall costs so that decision-makers can make a stronger case for investing in ASPs, considering competing priorities. Such data on ASPs in lower- and middle-income countries is limited and requires urgent attention

The History, Relevance, and Applications of the Periodic System in Geochemistry

Geochemistry is a discipline in the earth sciences concerned with understanding the chemistry of the Earth and what that chemistry tells us about the processes that control the formation and evolution of Earth materials and the planet itself. The periodic table and the periodic system, as developed by Mendeleev and others in the nineteenth century, are as important in geochemistry as in other areas of chemistry. In fact, systemisation of the myriad of observations that geochemists make is perhaps even more important in this branch of chemistry, given the huge variability in the nature of Earth materials – from the Fe-rich core, through the silicate-dominated mantle and crust, to the volatile-rich ocean and atmosphere. This systemisation started in the eighteenth century, when geochemistry did not yet exist as a separate pursuit in itself. Mineralogy, one of the disciplines that eventually became geochemistry, was central to the discovery of the elements, and nineteenth-century mineralogists played a key role in this endeavour. Early “geochemists” continued this systemisation effort into the twentieth century, particularly highlighted in the career of V.M. Goldschmidt. The focus of the modern discipline of geochemistry has moved well beyond classification, in order to invert the information held in the properties of elements across the periodic table and their distribution across Earth and planetary materials, to learn about the physicochemical processes that shaped the Earth and other planets, on all scales. We illustrate this approach with key examples, those rooted in the patterns inherent in the periodic law as well as those that exploit concepts that only became familiar after Mendeleev, such as stable and radiogenic isotopes