Data science for health-care: Patient condition recognition

>Magister Scientiae - MScThe emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) have elicited increased interest in many areas of our daily lives. These include health, agriculture, aviation, manufacturing, cities management and many others. In the health sector, portable vital sign monitoring devices are being developed using the IoT technology to collect patients’ vital signs in real-time. The vital sign data acquired by wearable devices is quantitative and machine learning techniques can be applied to find hidden patterns in the dataset and help the medical practitioner with decision making. There are about 30000 diseases known to man and no human being can possibly remember all of them, their relations to other diseases, their symptoms and whether the symptoms exhibited by the patients are early warnings of a fatal disease. In light of this, Medical Decision Support Systems (MDSS) can provide assistance in making these crucial assessments. In most decision support systems factors a ect each other; they can be contradictory, competitive, and complementary. All these factors contribute to the overall decision and have di erent degrees of influence [85]. However, while there is more need for automated processes to improve the health-care sector, most of MDSS and the associated devices are still under clinical trials. This thesis revisits cyber physical health systems (CPHS) with the objective of designing and implementing a data analytics platform that provides patient condition monitoring services in terms of patient prioritisation and disease identification [1]. Di erent machine learning algorithms are investigated by the platform as potential candidate for achieving patient prioritisation. These include multiple linear regression, multiple logistic regression, classification and regression decision trees, single hidden layer neural networks and deep neural networks. Graph theory concepts are used to design and implement disease identification. The data analytics platform analyses data from biomedical sensors and other descriptive data provided by the patients (this can be recent data or historical data) stored in a cloud which can be private local health Information organisation (LHIO) or belonging to a regional health information organisation (RHIO). Users of the data analytics platform consisting of medical practitioners and patients are assumed to interact with the platform through cities’ pharmacies , rural E-Health kiosks end user applications

Understanding blood pressure dynamics in the South African population: a latent variables approach to the analysis and comparison of data from multiple surveys

Background: The 2015 edition of the Global Burden of Diseases Study identified elevated systolic blood pressure─ defined as systolic blood pressure greater than the minimum risk category of 110–115 mm Hg ─ as the largest single contributor to the global burden of disease, responsible for 211.8 million disability adjusted life years lost, up 8.8% in the last decade. Middle‐income countries are currently bearing the highest share of this burden, and, because of the rapid demographic transition towards larger and older populations, the burden is bound to increase rapidly in the coming years, unless age‐specific values of blood pressure are substantially reduced to compensate for the unfavourable demographic changes. Achieving this more favourable blood pressure distribution in populations undergoing rapid changes in their socioeconomic structure requires knowledge of the mechanisms underlying temporal variations of blood pressure and the relationships of such variations with socioeconomic variables.However, evidence on these mechanisms and reliable information on the temporal trends of blood pressure themselves are scant outside high‐income countries. Given the large gain in health that would result in low‐ and middle‐income countries if an optimal blood pressure were to be achieved in large sectors of the population, there is little doubt that temporal trends in the distribution of blood pressure in these populations and their possible determinants are an open and important area for investigation. Objectives: Objectives of the study were: 1. To assess the level of quality and comparability of blood pressure data collected in a series of large‐scale surveys carried out between 1998 and 2015 in South Africa, a middle‐income country undergoing rapid demographic and epidemiological transition; 2. To explore the possibility of applying a series of latent variables techniques to improve the comparability of data from the different sources and to minimise the effect of measurement and representation error on the estimation of cross‐sectional relationships and temporal trends; 3. To estimate changes in the distribution of blood pressure and derived quantities ‐‐‐ such as prevalence of uncontrolled hypertension ‐‐‐ in the South African adult population between 1998 and 2015, taking into account between‐surveys differences and measurement and representation error that could lead to artefactual conclusions; 4. To estimate the extent to which the estimated changes in the blood pressure distribution during the study period could be explained by concurrent changes in the distribution of a series of biological, behavioural and socioeconomic risk factors. Methods: A series of techniques within the general framework of structural equation modelling were applied to jointly analyse the data and estimate the temporal trends and relationships of interest. Results: The average systolic and diastolic blood pressure of South African adult women has progressively decreased since 2003‐2004, reversing the previous rising trend. Among men, the reversal happened only for the systolic blood pressure, while the average diastolic blood pressure continued rising, although at a lower pace than previously.In both genders, this pattern resulted in a reduction of the prevalence of uncontrolled hypertension between 2003‐2004 and 2014‐2015, by 8 percentage points among women and by 4.5 percentage points among men. This consistent and rapid decrease cannot be explained by changes in the age structure of the population, smoking and alcohol consumption habits, distribution of body mass index or urbanization. The diffusion of antihypertensive treatment and, among women, cohort effects and rapidly increasing educational level partly explain the recent trend, but a substantial part of the observed decrease remains unexplained by the factors available in our analyses. Large seasonal variations in both systolic and diastolic blood pressure are present in the South African population, and their magnitude is greater among population strata with low socioeconomic status. From a methodological point of view, there were two further results of this study. First, estimates of blood pressure and related quantities from the eight large‐scale population surveys carried out in South Africa between 1998 and 2015are not directly comparable, because of methodological differences and overall data quality. Second, structural equation modelling (and, within this general framework, multiple group modelling, normal‐censored regression, mixture analysis with skew‐normal distributions and the use of additional parameters and phantom variables) represent a viable and advantageous alternative to current methods of comparative analysis of blood pressure data. Conclusions: Encouraging signs regarding the future development of the burden of diseases related to elevated blood pressure in the South African population emerge from this study. Age‐specific prevalence of uncontrolled hypertension seems to be decreasing, especially among women, and this decrease is accompanied by declining mortality for cardiovascular disease, particularly for stroke, recorded in burden of mortality studies. The reasons of this decrease are largely unexplained and warrant further investigation. However, among the possible drivers analysed in this study, increased accessibility and efficacy of antihypertensive treatment are likely to be playing a role in the observed decrease in blood pressure. The growing obesity epidemic, on the contrary, is likely to be limiting the achievable benefits. Both of these factors can be targeted to maintain and improve the current decline in population values of blood pressure and prevalence of hypertension. The large seasonal variations of blood pressure and their unequal distribution across socioeconomic strata also suggest that interventions to reduce exposure to low temperatures might have public health benefit. From the point of view of the epidemiological investigation, the results of this study suggest that the current methods for the analysis of survey data on blood pressure and the measurement protocols for future data collections should be improved to increase between‐surveys comparabilityand gather more reliable information on temporal changes in BP and gain better understanding of their drivers. Specifically, analytical methods should take explicitly into account known sources of measurement and representation error to reduce their biasing effects, especially when inter‐survey comparisons are involved. Protocols for future studies should routinely include collection of auxiliary information and/or explicit validation of devices and procedures in the specific population

Modifiable Risk in a Changing Climate: Linking household-level temperature, humidity, and air pollution to population health

Background: This dissertation comprises research conducted on two distinct projects. Project I focuses on the connection between household air pollution (HAP) from cooking with biomass fuels and blood pressure (BP); this research is situated in the context of a large randomized trial of a cookstove intervention in Ghana, West Africa. The setting of Project II, meanwhile, is the residential environment of New York City, where we explore temperature and humidity conditions in homes and relate these conditions to summertime heat wave risk and to the survival and transmission of respiratory viruses in the winter. Although these projects are quite distinct, each relates to the complex relationship between climate change and health. Reducing HAP to improve health (the focus of Project I) will simultaneously reduce climate change through a reduction in emissions of short-lived climate pollutants into the atmosphere. Meanwhile, furthering our understanding of heat and humidity levels inside urban residences (the focus of Project II) is crucial to our ability to protect health in light of projections for a changing climate. Domestic activities associated with heating, cooling, and cooking are thus very relevant both to human health and to climate change mitigation and adaptation. Objectives and Methods: Our overall objective for Project I was to investigate exposure- response relationships between HAP and BP in a cohort of pregnant women taking part in the Ghana Randomized Air Pollution and Health Study (GRAPHS). We first explored this association in a cross-sectional study (Chapter 1), in which we used 72-hour personal monitoring to ascertain levels of exposure among the GRAPHS women to carbon monoxide (CO), one of the pollutants emitted by traditional wood-fed cooking fires. These exposure data were collected at enrollment into the GRAPHS study, prior to the initiation of cooking with improved cookstoves. We investigated the association between these “baseline” CO exposure levels and the women’s blood pressure at enrollment into GRAPHS. A limitation of this study was that BP was only measured once. We followed this with a second study of 44 women drawn from the same cohort (Chapter 2), for whom we designed BP protocols using 24-hour ambulatory blood pressure monitoring (ABPM), the current gold standard for clinical diagnosis of hypertension. As we were not aware of any prior research in Africa that had employed ABPM, we also designed a parallel BP protocol using home blood pressure monitoring (HBPM) equipment for comparison with ABPM. The use of ABPM with concurrent personal CO monitoring enabled us to investigate hourly associations between CO exposure and changes in BP. We also evaluated BP in these women both before and after the cookstove intervention; this allowed us to investigate whether any changes in BP were associated with switching to an improved cookstove. Our objectives for Project II were to understand the distribution of temperature and humidity conditions in a range of New York City homes during the summer and winter seasons, to evaluate the impact of structural and behavioral factors (e.g. building size, use of air conditioning, and use of humidifiers) on these conditions, and to build models that could help predict indoor conditions from more readily available outdoor measurements. We conducted this research in two ways. We first analyzed a set of indoor temperature and humidity measurements that were collected in 285 New York City apartments during portions of summers 2003-2011 and used these data to simulate indoor conditions during two heat wave scenarios, one of which was more moderate and the other of which was more extreme (Chapter 3). Second, we designed and conducted a new study in which temperature and humidity were monitored in a set of 40 NYC apartments between 2013 and 2015 (Chapters 4-6). This second study enabled us extend our research into the winter season, and also to explore how factors such as air conditioning and humidifier use impacted indoor temperature and humidity. We also investigated relationships between the monitored conditions, self-reported perceptions of the indoor environment, and symptoms that were experienced among household members. Results: In the cross-sectional analysis of CO and BP in the GRAPHS cohort (Chapter 1), we found a significant positive association between CO exposure and diastolic blood pressure (DBP): on average, each 1 ppm increase in exposure to CO was associated with 0.43 mmHg higher DBP [0.01, 0.86]. A non-significant positive trend was also observed for systolic blood pressure (SBP). In our study of the acute relationship between CO exposure and BP (Chapter 2), we determined that peak CO exposure (defined as above the 90th percentile of the exposure distribution, or an average of 4.1ppm) in the two hours prior to BP measurement was associated with elevations in hourly systolic BP (4.3 mmHg [95% CI: 1.1, 7.4]) and diastolic BP (4.5 mmHg [95% CI: 1.9, 7.2]), as compared to BP following lower CO exposures. We also observed a non-significant trend toward lower BP following initiation of cooking with an improved cookstove. Lastly, we demonstrated that ABPM was a feasible and well-tolerated tool for BP assessment in a rural West African setting. For Project II in New York City, we first determined that there was a great deal of variability in indoor summer heat index (HI) between homes in association with similar outdoor conditions, and that this variability increased with increasing outdoor heat (Chapter 3). Our simulation of a moderate heat wave led us to conclude that the hottest 5% of the homes would reach peak indoor heat index (HI) values of 39°C. In a more extreme heat wave simulation, HI in the hottest 5% of homes reached a peak of 41oC and did not drop below 34oC for the entire nine- day simulated heat wave period. Our second indoor monitoring study yielded the following findings: in the summer season (Chapter 4), we found significant differences in indoor temperature and heat index according to the type of air conditioning (AC) in the home. Homes with central AC were the coolest, followed by homes with ductless AC, window AC, and no AC. Apartments on the top floor of a building were significantly hotter than other apartments regardless of the presence of AC. During the winter season (Chapter 5), median vapor pressure in our sample of apartments was 6.5mb. Comparing humidity levels in the apartments to a threshold of 10mb vapor pressure that has been proposed as protective against influenza virus transmission, levels of absolute humidity in the homes remained below this threshold for 86% of the winter: a total of over three months. Residential use of humidifiers was not associated with higher indoor humidity levels. Larger building size (above 100 units) was significantly associated with lower humidity, while the presence of a radiator heating system was non-significantly associated with higher humidity. Lastly, perceptions of indoor temperature and measured temperature were significantly associated in both the summer and the winter (Chapter 6), while sleep quality was inversely related to measured indoor temperature in the summer season only. Reports of heat- stress symptoms were associated with perceived, but not measured, temperature in the summer season. Conclusions: The work presented in this dissertation adds to a growing body of evidence on the importance of exposures in the domestic environment to health and well-being. The research reported here on household air pollution in Ghana documents an exposure-response relationship between air pollution from cookstoves and elevations in blood pressure, on both a chronic and an acute basis. As elevated BP is a known risk factor for cardiovascular disease (CVD), our research provides support for a plausible factor linking HAP exposure to CVD. Meanwhile, our research on temperature and humidity in New York City residences provides concrete data to supplement the very slim literature to date documenting these conditions in the home environment, where Americans spend over half their time. We conclude, first, that AC may not be fully protective against summertime heat risk, and second, that the levels of humidity we observed in residential environments are consistent with levels that have been shown to promote enhanced survival and transmission of respiratory viruses in experimental settings. We suggest that interventions that can reduce exposure to household air pollution and excess indoor heat can also mitigate climate change, and that with thoughtful planning we can improve health at the same time as we foster resiliency in the face of a changing climate

Secondary Analysis of Electronic Health Records

Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level