京都大学 / 新制・課程博士 / 博士(工学) / 甲第24212号 / 工博第5040号 / 新制||工||1787(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 中北 英一, 准教授 山口 弘誠, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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This study examined the use of an institutionally-specific risk prediction model in the university's College of Education. Set in a large, urban, public university, the risk model predicted incoming students' first-semester GPAs, which, in turn, predicted the students' risk of attrition. Additionally, the study investigated advising practices within the College of Education via semi-structured interviews with the College's advising staff and a document analysis of students' advising notes in an attempt to find thematic links between undergraduate retention and usage of an advising center. Data were analyzed to determine the accuracy of the risk model in the College of Education. The results of this study are used to inform the College of Education's administration, faculty, and staff about the implications of risk prediction and to suggest potential treatments to increase retention rates. Furthermore, recommendations for future research are discussed for this study's institution and for the field of education. / Educational Psychology

Yes / Objectives: To develop a logistic regression model to predict the risk of sepsis following emergency medical admission using the patient’s first, routinely collected, electronically recorded vital signs and blood test results and to validate this novel computer-aided risk of sepsis model, using data from another hospital. Design: Cross-sectional model development and external validation study reporting the C-statistic based on a validated optimized algorithm to identify sepsis and severe sepsis (including septic shock) from administrative hospital databases using International Classification of Diseases, 10th Edition, codes. Setting: Two acute hospitals (York Hospital - development data; Northern Lincolnshire and Goole Hospital - external validation data). Patients: Adult emergency medical admissions discharged over a 24-month period with vital signs and blood test results recorded at admission. Interventions: None. Main Results: The prevalence of sepsis and severe sepsis was lower in York Hospital (18.5% = 4,861/2,6247; 5.3% = 1,387/2,6247) than Northern Lincolnshire and Goole Hospital (25.1% = 7,773/30,996; 9.2% = 2,864/30,996). The mortality for sepsis (York Hospital: 14.5% = 704/4,861; Northern Lincolnshire and Goole Hospital: 11.6% = 899/7,773) was lower than the mortality for severe sepsis (York Hospital: 29.0% = 402/1,387; Northern Lincolnshire and Goole Hospital: 21.4% = 612/2,864). The C-statistic for computer-aided risk of sepsis in York Hospital (all sepsis 0.78; sepsis: 0.73; severe sepsis: 0.80) was similar in an external hospital setting (Northern Lincolnshire and Goole Hospital: all sepsis 0.79; sepsis: 0.70; severe sepsis: 0.81). A cutoff value of 0.2 gives reasonable performance. Conclusions: We have developed a novel, externally validated computer-aided risk of sepsis, with reasonably good performance for estimating the risk of sepsis for emergency medical admissions using the patient’s first, electronically recorded, vital signs and blood tests results. Since computer-aided risk of sepsis places no additional data collection burden on clinicians and is automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. / Health Foundation

Cardiovascular disease is the leading cause of death worldwide and ischemic heart disease is the most common manifestation. Despite improved outcomes during the last decades, patients with acute coronary syndromes (ACS) are still at substantial risk of recurrent ischemic events and mortality. The aims of this thesis were to investigate the effect of the novel antiplatelet agent ticagrelor versus clopidogrel in patients with non-ST-elevation ACS (NSTE-ACS), overall and in relation to initial revascularization, and to explore this effect in relation to cardiac biomarkers. The impact of timing of revascularization in non-ST-elevation myocardial infarction (NSTEMI) was also studied, by assessing risk of mortality and recurrent myocardial infarction in relation to delay of percutaneous coronary intervention (PCI) in a nation-wide cohort. Finally, a novel clinical prediction model based on angiographic findings, biomarkers, and clinical characteristics was developed to estimate risk of ischemic events after performed revascularization. Ticagrelor treatment compared with clopidogrel was associated with a reduction in the composite endpoint of cardiovascular death/myocardial infarction/stroke and mortality alone, without any increase in overall major bleeding, but increased non-CABG-related major bleeding. The effect of ticagrelor over clopidogrel was consistent independent of initial revascularization. Elevated high-sensitivity cardiac troponin-T predicted benefit of ticagrelor over clopidogrel, while no difference between treatments was detected at normal levels. In patients with NSTEMI, PCI treatment within two days after hospital admission was associated with lower risk of all-cause death and recurrent myocardial infarction compared with delayed PCI. The new clinical prediction model included the following variables: prior vascular disease, extent of coronary artery disease, level of N-terminal pro-B-type natriuretic peptide and estimated glomerular filtration rate; and showed good discriminatory ability for the risk prediction of cardiovascular death/myocardial infarction/stroke and cardiovascular death alone. In conclusion, these results show that ticagrelor reduces the risk of recurrent ischemic events and mortality in patients with NSTE-ACS when compared with clopidogrel, and this effect seems independent of performed revascularization. The results also indicate that biomarkers could be used to select patients who would benefit most from more intense platelet inhibition. Furthermore, early PCI in NSTEMI seems to be associated with improved outcome. Finally, the novel clinical prediction model based only on four variables showed good discriminatory ability, which makes it a potentially effective and simple tool for tailored treatment based on individual risk of recurrent events.

Le contrôle de stabilité est un sujet essentiel dans les systèmes avancés d’aide à la conduite développés par les constructeurs et équipementiers automobiles. Les systèmes de sécurité actifs sont devenus un standard dans les véhicules particuliers, tels que : le contrôle électronique de la stabilité (ESC) et le système de contrôle de traction (TCS). La description du comportement dynamique du véhicule pendant le mouvement, est fondamental dans le fonctionnement des nouveaux systèmes de sécurité active. Certains systèmes actifs sont déjà implémentés dans des véhicules standards comme des options supplémentaires, pour améliorer la sécurité sur la route ou pour le confort du conducteur et des passagers. Cependant, ces systèmes ont besoin d’informations sur la dynamique de véhicule, qui représente les caractéristiques de mouvement du véhicule sur la route. L’accès à ces informations est souvent difficile, pour des raisons technologiques ou économiques. De ce fait, nous développons des algorithmes, basés sur la technique d’observation d’état, pour estimer une partie de ces variables notamment, les efforts dynamiques du contact pneumatique/chaussée et l’angle de dérive dans son environnement. En revanche, ces systèmes sont conçus pour faire face à l’état actuel du véhicule où la situation de danger a toujours eu lieu, la capacité de ces systèmes est limitée à minimiser les effets de danger. L’objectif ultime est de prévoir et d’éviter efficacement un accident avant qu’il se produise. Par conséquent, ce travail est dédié aussi à développer une méthode de prédiction des risques pour rappeler au conducteur la vitesse de sécurité pour négocier les virages à venir. Dans un premier temps, nous développons dans ce mémoire une nouvelle approche pour estimer la répartition de la charge verticale sur chaque roue dans un environnement réel. L’influence de l’angle de pente est considérée dans la phase de reconstruction du modèle du véhicule. Les forces verticales sont estimées en utilisant un filtre de Kalman. Afin d’estimer la force latérale du pneu, un filtre de Kalman entendu et un filtre Particulaire ont appliqués pour tenir compte des non-linéarités du modèle de véhicule. Deux techniques différentes d’observateurs sont proposées et comparées avec des données expérimentales. Dans un deuxième temps, nous étendons, à l’instant futur, la prise en compte de l’évaluation de risque d’accidents. La prédiction des paramètres de la dynamique du véhicule, l’évaluation du risque potentiel ainsi que la détermination d’une vitesse d’alerte à l’approche des virages, sont introduites pour réduire le risque potentiel d’accident dans les virages. Enfin, la dernière partie du mémoire est consacrée à l’application en temps réel, sur un véhicule démonstrateur, du processus d’observation d’état développé précédemment. Les résultats expérimentaux sont réalisés pour démontrer la performance des estimateurs intégrés en temps réel. / Nowadays, a variety of advanced driving assistance systems are being developed by research centers and automobile manufactures. Stability control is an essential topic in the modern industrial automobile society. Driving safety is widely concerned in the passenger cars to prevent potential risks. More and more electronic active safety systems are fitted out as a standard option, such as Electronic Stability Control (ESC) and Traction Control System (TCS). These safety systems are efficient in helping the driver maintain control of the car and also are considered highly cost-effective. However, for the future development trend of these systems, a more complex and integrated control unit requires more information about the vehicle dynamics. Some fundamental parameters such as tire road forces and sideslip angle are effective in describing vehicle dynamics. Nevertheless, it is lacking an effective and low-cost sensor to measure directly. Therefore, this study presents amethod to estimate these parameters using observer technologies and low-cost sensors which are available on the passenger cars in real environment. In addition, these systems are designed for dealing with vehicle current state where danger situation has always occurred, the capacity of these systems is limited to minimize the effects. We were wondering whether shall we predict and effectively avoid a crash before it occurred. Therefore, this work is also addressed to develop a risk prediction method for proposing driver a safe speed to negotiate the upcoming curves. First, this dissertation develops a new approach to estimate the vertical load distribution in real environment. The influence of bank angle is considered in the phase of reconstruction of vehicle model. The vertical tire force on banked road is estimated by using Kalman filter. In order to estimate the lateral tire force, two nonlinear observers are addressed to solve the nonlinearity of vehicle model. The Extended Kalman filter is widely discussed in the previous literature, while we firstly use a Particle filter to estimate the vehicle dynamics parameters. Two different observer technologies are proposed and compared using the experimental data. Second, extending the consideration of road safety to the future instant. Prediction of vehicle dynamics parameters, evaluation of potential risk as well as establishment of advisory speed on curves are introduced to reduce the possibility of crash occurrence on curves. Last but not least, the real-time sampling and process system is presented, the estimator with EKF and PF has been developed as a real-time application. Experimental results are performed to demonstrate the performance of these integrated systems in real-time.

A complex trait is a trait or disease that is controlled by both genetic and environmental factors, along with their interactions. Trait architecture encompasses the genetic variants and environmental causes of variation in the trait or disease, their effects on the trait or disease and the mechanism by which these factors interact at molecular and organism levels. It is important to understand trait architecture both from a biological viewpoint and a health perspective. In this thesis, I laid emphasis on exploring the influence of familial environmental factors on complex trait architecture alongside the genetic components. I performed a variety of studies to explore the architecture of anthropometric and cardio-metabolic traits, such as height, body mass index, high density lipoprotein content of blood and blood pressure, using a cohort of 20,000 individuals of recent Scottish descent and their phenotype measurements, Single Nucleotide Polymorphism (SNP) data and genealogical information. I extended a method of variance component analysis that could simultaneously estimate SNP-associated heritability and total heritability whilst considering familial environmental effects shared among siblings, couples and nuclear family members. I found that most missing heritability could be explained by including closely related individuals in the analysis and accounting for these close relationships; and that, on top of genetics, couple and sibling environmental effects are additional significant contributors to the complex trait variation investigated. Subsequently, I accounted for couple and sibling environmental effects in Genome- Wide Association Study (GWAS) and prediction models. Results demonstrated that by adding additional couple and sibling information, both GWAS performance and prediction accuracy were boosted for most traits investigated, especially for traits related to obesity. Since couple environmental effects as modelled in my study might, in fact, reflect the combined effect of assortative mating and shared couple environment, I explored further the dissection of couple effects according to their origin. I extended assortative mating theory by deriving the expected resemblance between an individual and in-laws of his first-degree relatives. Using the expected resemblance derived, I developed a novel pedigree study which could jointly estimate the heritability and the degree of assortative mating. I have shown in this thesis that, for anthropometric and cardio-metabolic traits, environmental factors shared by siblings and couples seem to have important effects on trait variation and that appropriate modelling of such effects may improve the outcome of genetic analyses and our understanding of the causes of trait variation. My thesis also points out that future studies on exploring trait architecture should not be limited to genetics because environment, as well as mate choice, might be a major contributor to trait variation, although trait architecture varies from trait to trait.

<p>The cardiac troponins are the biochemical markers of choice for the diagnosis of acute myocardial infarction (AMI) and risk prediction in patients with acute coronary syndrome (ACS). In this thesis, the role of early serial cardiac troponin I (cTnI) testing was assessed in fairly unselected patient populations admitted because of chest pain and participating in the FAST II-study (n=197) and the FASTER I-study (n=380). Additionally, the importance of cTnI testing in stable post-ACS patients from the FRISC II-study (n=1092) was studied.</p><p>The analyses in chest pain patients demonstrate that cTnI is very useful for early diagnostic and prognostic assessment. cTnI allowed already 2 hours after admission the reliable exclusion of AMI and the identification of low-risk patients when ECG findings and a renal marker such as cystatin C were added as conjuncts. Other biomarkers such as CK-MB, myoglobin, NT-pro BNP or CRP did not provide superior clinical information. However, myoglobin may be valuable in combination with cTnI results for the early prediction of an impending major AMI when used as input variable for an artificial neural network. Such an approach applying cTnI results only may also furthermore improve the early diagnosis of AMI.</p><p>Persistent cTnI elevation > 0.01 μg/L was detectable using a high-sensitive assay in 26% of the stable post-ACS patients from the FRISC II-study. NT-pro BNP levels at 6 months were the most important variable independently associated to persistent cTnI elevation besides male gender, indicating a relationship between adverse left ventricular remodeling processes and cTnI leakage. Patients with persistent cTnI elevation had a considerable risk for both mortality and AMI during 5 year follow-up. </p><p>These analyses thus, confirm the value of cTnI for early assessment of chest pain patients and provide new and unique evidence regarding the role of cTnI for risk prediction in post-ACS populations.</p>

The cardiac troponins are the biochemical markers of choice for the diagnosis of acute myocardial infarction (AMI) and risk prediction in patients with acute coronary syndrome (ACS). In this thesis, the role of early serial cardiac troponin I (cTnI) testing was assessed in fairly unselected patient populations admitted because of chest pain and participating in the FAST II-study (n=197) and the FASTER I-study (n=380). Additionally, the importance of cTnI testing in stable post-ACS patients from the FRISC II-study (n=1092) was studied. The analyses in chest pain patients demonstrate that cTnI is very useful for early diagnostic and prognostic assessment. cTnI allowed already 2 hours after admission the reliable exclusion of AMI and the identification of low-risk patients when ECG findings and a renal marker such as cystatin C were added as conjuncts. Other biomarkers such as CK-MB, myoglobin, NT-pro BNP or CRP did not provide superior clinical information. However, myoglobin may be valuable in combination with cTnI results for the early prediction of an impending major AMI when used as input variable for an artificial neural network. Such an approach applying cTnI results only may also furthermore improve the early diagnosis of AMI. Persistent cTnI elevation &gt; 0.01 μg/L was detectable using a high-sensitive assay in 26% of the stable post-ACS patients from the FRISC II-study. NT-pro BNP levels at 6 months were the most important variable independently associated to persistent cTnI elevation besides male gender, indicating a relationship between adverse left ventricular remodeling processes and cTnI leakage. Patients with persistent cTnI elevation had a considerable risk for both mortality and AMI during 5 year follow-up. These analyses thus, confirm the value of cTnI for early assessment of chest pain patients and provide new and unique evidence regarding the role of cTnI for risk prediction in post-ACS populations.

A large part of people's lives are spent in indoor spaces such as office and university buildings, shopping malls, subway stations, airports, museums, community centers, etc. Such kind of spaces can be very large and paths inside the locations can be constrained and complex. Deployment of indoor tracking technologies like RFID, Bluetooth, and Wi-Fi can track people and object movements from one symbolic location to another within the indoor spaces. The resulting tracking data can be massive in volume. Analyzing these large volumes of tracking data can reveal interesting patterns that can provide opportunities for different types of location-based services, security, indoor navigation, identifying problems in the system, and finally service improvements. In addition to the huge volume, the structure of the unprocessed raw tracking data is complex in nature and not directly suitable for further efficient analysis. It is essential to develop efficient data management techniques and perform different kinds of analysis to make the data beneficial to the end user. The Ph.D. study is sponsored by the BagTrack Project (http://daisy.aau.dk/bagtrack). The main technological objective of this project is to build a global IT solution to significantly improve the worldwide aviation baggage handling quality. The Ph.D. study focuses on developing data management techniques for efficient and effective analysis of RFID-based symbolic indoor tracking data, especially for the baggage tracking scenario. First, the thesis describes a carefully designed a data warehouse solution with a relational schema sitting underneath a multidimensional data cube, that can handle the many complexities in the massive non-traditional RFID baggage tracking data. The thesis presents the ETL flow that loads the data warehouse with the appropriate tracking data from the data sources. Second, the thesis presents a methodology for mining risk factors in RFID baggage tracking data. The aim is to find the factors and interesting patterns that are responsible for baggage mishandling. Third, the thesis presents an online risk prediction technique for indoor moving objects. The target is to develop a risk prediction system that can predict the risk of an object in real-time during its operation so that the object can be saved from being mishandled. Fourth, the thesis presents two graph-based models for constrained and semi-constrained indoor movements, respectively. These models are used for mapping the tracking records into mapping records that represent the entry and exit times of an object at a symbolic location. The mapping records are then used for finding dense locations. Fifth, the thesis presents an efficient indexing technique, called the $DLT$-Index, for efficiently processing dense location queries as well as point and interval queries. The outcome of the thesis can contribute to the aviation industry for efficiently processing different analytical queries, finding problems in baggage management systems, and improving baggage handling quality. The developed data management techniques also contribute to the spatio-temporal data management and data mining field. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished