딥러닝 기반 생존분석이 적용된 심혈관질환 위험 평가 모델 성능 향상을 위한 콕스 모형과 결합된 하이브리드 접근법: 헬스케어-환경 연계 데이터 활용 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 박상민 .Background and aims: The contribution of different cardiovascular disease (CVD) risk factors for the risk evaluation and predictive modeling for incident CVD is often debated. Also, to what extent data on CVD risk factors from multiple data categories should be collected for comprehensive risk assessment and predictive modeling for CVD risk using survival analysis is uncertain despite the increasing availability of the relevant data sources. This study aimed to evaluate the contribution of different data categories derived from integrated data on healthcare and environmental exposure to the risk evaluation and prediction models for CVD risk using deep learning based survival analysis in combination with Cox proportional hazards regression and Cox proportional hazards regression. Methods: Information on the comprehensive list of CVD risk factors were collected from systematic reviews of variables included in the conventional CVD risk assessment tools and observational studies from medical literature database (PubMed and Embase). Each risk factor was screened for availability in the National Health Insurance Service-National Sample Cohort (NHIS-NSC) linked to environmental exposure data on cumulative particulate matter and urban green space using residential area code. Individual records of 137,249 patients more than 40 years of age who underwent the biennial national health screening between 2009 and 2010 without previous history of CVD were followed up for incident CVD event from January 1, 2011 to December 31, 2013 in the NHIS-NSC with data linkage to environmental exposure. Statistics-based variable selection methods were implemented as follows: statistical significance, subset with the minimum (best) Akaike Information Criteria (AIC), variables selected from the regularized Cox proportional hazards regression with elastic net penalty, and finally a variable set that commonly meets all the criteria from the abovementioned statistical methods. Prediction models using Cox proportional hazards deep neural network (DeepSurv) and Cox proportional hazards regression were constructed in the training set (80% of the total sample) using input feature sets selected from the abovementioned strategies and progressively adding input features by data categories to examine the relative contribution of each data type to the predictive performance for CVD risk. Performance evaluations of the DeepSurv and Cox proportional hazards regression models for CVD risk were conducted in the test set (20% of the total sample) with Unos concordance statistics (C-index), which is the most up-to-date evaluation metrics for the survival models with right censored data. Results: After the comprehensive review, data synthesis, and availability check, a total of 31 risk factors in the categories of sociodemographic, clinical laboratory test and measurement, lifestyle behavior, family history, underlying medical conditions, dental health, medication, and environmental exposure were identified in the NHIS-NSC linked to environmental exposure data. Among the models constructed with different variable selection methods, using statistically significant variables for DeepSurv (Unos C-index: 0.7069) and all of the variables for Cox proportional hazards regression (Unos C-index: 0.7052) showed improved predictive performance for CVD risk, which was a statistically significant increase (p-value for difference in Unos C-index: <0.0001 for both comparisons) compared to the models with basic clinical factors (age, sex, and body mass index), respectively. When all and statistically significant variables in each data category from sociodemographic to environmental exposure were progressively added as input features into DeepSurv and Cox proportional hazards regression for predictive modeling for CVD risk, the DeepSurv model with statistically significant variables pertaining to the sociodemographic factors, clinical laboratory test and measurement, and lifestyle behavior data showed the notable performance that outperformed Cox proportional hazards regression model with statistically significant variables added up to the medication category. Extensive data linkage to environmental exposure on cumulative particulate matter and urban green space offered only marginal improvement for the predictive performance of DeepSurv and Cox proportional hazards regression models for CVD risk. Conclusion: To obtain the best predictive performance of DeepSurv model for CVD risk with minimum number of input features, information on sociodemographic, clinical laboratory test and measurement, and lifestyle behavior should be primarily collected and used as input features in the NHIS-NSC. Also, the overall performance of DeepSurv for CVD risk assessment was improved with a hybrid approach using statistically significant variables from Cox proportional hazards regression as input features. When all the data categories in the NHIS-NSC linked to environmental exposure data are available, progressively adding variables in each data category could incrementally increase the predictive performance of DeepSurv model for CVD risk with the hybrid approach. Data linkage to the environmental exposure with residential area code in the NHIS-NSC offered marginally improved performance for CVD risk in both DeepSurv model with the hybrid approach and Cox proportional hazards regression model.배경 및 목적: 심혈관질환 위험평가 및 예측모델링에서 다양한 심혈관질환 위험인자들의 모델 성능향상에 대한 기여도는 논란의 요지로 보고되어왔다. 또한, 지속적으로 증가하는 활용 가능한 심혈관질환 관련 데이터의 종류와 양에도 불구하고 포괄적인 심혈관질환 위험평가와 최적의 예측 모형 개발을 위해 데이터를 어느 범위와 수준까지 수집해야 하는지에 대한 근거는 부족한 현황이다. 본 연구에서는 콕스 모형과 결합된 딥러닝 기반 생존분석 접근법 및 콕스 모형을 활용한 심혈관질환 위험평가와 예측모델링에서 헬스케어-환경 연계 데이터 활용방법 및 범주에 따른 모델 성능향상에 대한 기여도를 평가하고자 하였다. 연구 방법: 전통적 심혈관질환 위험 평가 도구 및 관찰 연구들에 포함 된 심혈관질환 위험요인 관련 변수들을 체계적 문헌고찰 방법론을 활용하여 의학연구 문헌데이터베이스 (PubMed and Embase)에서 포괄적으로 정보를 수집하였다. 미세먼지 누적장기노출 및 도시녹지면적에 대한 환경 노출 데이터와 연계 된 국민건강보험공단 표본코호트, (National Health Insurance Service-National Sample Cohort, NHIS-NSC)에서 각 심혈관질환 위험인자들의 데이터 확보 가능성을 검토하였다. NHIS-NSC를 기준으로 2009년에서 2010년 사이에 국가건강검진을 받은 40세 이상 대상자 중 과거 심혈관질환 병력이 없는 대상자 137,249명의 환자에 대한 정보를 수집하여 2011 년 1 월 1 일부터 2013 년 12 월 31 일까지 신규 발생한 심혈관질환에 대해 시간 경과에 따라 추적 조사하였다. 통계 기반 변수선택 방법은 콕스비례위험모형에서 통계적 유의성, 최소 (최상의) Akaike Information Criteria (AIC)의 하위 집합, elastic net penalty로 정규화 된 콕스비례위험모형에서 선택된 변수 및 위에 언급된 모든 기준을 충족하는 변수 세트로 선정하였다. 위에 명시된 통계적 방법 외 모든 데이터 범주에 속한 변수 및 콕스비례위험모형에서 통계적으로 유의미한 변수 (하이브리드 접근법)를 점진적으로 입력 피쳐로 추가하는 전략으로 딥러닝 기반 생존분석 (Cox proportional hazards deep neural network, DeepSurv) 및 콕스비례위험모형에서 예측 모델들을 훈련 세트 (전체 샘플의 80 %)를 기반으로 개발하였다. DeepSurv 및 콕스비례 위험모형을 활용한 심혈관질환 예측 모델의 성능평가는 생존분석을 활용한 예측 모델링에 가장 적합한 평가지표로 알려진 Unos concordance statistics (C-index)를 사용하여 테스트 세트 (총 샘플의 20 %)에서 수행하였다. 결과: 체계적 문헌고찰, 데이터 취합 및 추출 가능성 검토 후, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관, 가족력, 건강상태, 구강건강, 약물 및 환경 노출 데이터 범주에서 총 31 개의 심혈관질환 위험인자가 지역환경 자료와 연계된 NHIS-NSC에서 확인되었다. 통계 기반 변수선택 방법으로 개발한 심혈관질환 예측 모델 중 콕스비례위험모형에서 통계적으로 유의미한 변수를 DeepSurv에 적용한 하이브리드 접근법이 Uno 's C-index 값 0.7069, 모든 변수를 콕스비례위험모형에 적용한 콕스비례위험모형이 Uno 's C-index 값 0.7052로 나타나 기본 임상 요인 (연령, 성별 및 체질량지수)이 포함된 예측 모델과 비교하여 통계적으로 유의미한 모델 예측력 증가를 보였다 (두 모델 모두 Unos C-index 차이에 대한 p-value : <0.0001). 인구사회학적 특성에서 환경 노출에 이르기까지 각 데이터 범주에서 모두 통계적으로 유의미한 변수들이 심혈관질환 예측 모델링을위한 DeepSurv 및 Cox 비례 위험 회귀에 입력 피쳐로 점진적으로 추가 된 경우, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관 요인 중 통계적으로 유의미한 변수들로 구성된 DeepSurv 모델이 의약품 사용까지 고려한 Cox 비례 위험 회귀를 기반으로 한 모델 보다 뛰어난 성능을 나타냈다. 미세먼지 및 도시녹지면적에 대한 환경 노출 데이터를 거주지를 기반으로 NHIS-NSC와 연계 후 점진적으로 입력 피쳐로 추가 시 DeepSurv 및 콕스비례위험모형을 활용한 심혈관질환 예측 모델링 성능을 통계적으로 유의미한 수준으로 개선하지 못했다. 결론: 최소 입력 피쳐를 갖춘 생존 분석 기반 심혈관질환 예측 모델에서 최상의 성능을 얻으려면 인구사회학적, 건강검진 및 측정 결과, 및 생활습관에 대한 정보를 NHIS-NSC에서 수집하여 DeepSurv의 입력 피쳐로 활용해야한다. 지역환경 자료와 연계된 NHIS-NSC에서 모든 데이터 범주를 사용할 수 있을 때 점진적으로 각 데이터 범주 중 콕스비례위험모형에서 통계적으로 유의미한 심혈관질환 위험인자를 점진적으로 입력 피쳐로 DeepSurv 모델에 추가하는 하이브리드 접근법에서 심혈관질환 예측 모델링 성능이 점차 향상 될 것으로 기대할 수 있다. 주거 지역 코드를 사용한 NHIS-NSC와 환경 노출 데이터 연계는 DeepSurv 및 콕스비례위험모형 모두에서 심혈관질환 예측 모델링 성능이 향상되었지만 통계적으로 유의미한 증가 수준은 아닌 것으로 나타나 환경 노출 데이터 연계 및 적용 시 검토가 필요할 것으로 추정된다.I. Introduction 1 1. Background 1 2. Research problem 4 3. Hypothesis and objective 6 3.1. Hypothesis 6 3.2. Objective 6 II. Materials and methods 8 1. Comprehensive review and identification of cardiovascular disease (CVD) risk factors 8 1.1. Systematic review on variables included in conventional CVD risk assessment tools 8 1.2. Systematic review on traditional and emerging CVD risk factors from observational studies 9 1.3. Integration of the comprehensive list of CVD risk factors 11 1.4. Screening for data availability 11 2. Cohort analysis for measuring strength of association between risk factors and incident cardiovascular disease 11 2.1 Study population and linkage to environmental exposure data 11 2.2. Variable selection and data processing 15 2.3. Population-based cohort analysis 17 3. Predictive modeling using survival analysis: DeepSurv and Cox proportional hazards regression 17 3.1. Model development 17 3.2. Evaluation of the predictive performance of the models 20 III. Results 21 1. Identification and categorization of cardiovascular disease risk factors 21 2. Magnitude of association between selected risk factors with cardiovascular disease 43 3. Model performance evaluation 56 VI. Discussion 68 1. Key findings and contributions 68 2. Comparison to other studies 69 3. Strengths and limitations 73 4. Implications 74 5. Future perspectives 75 V. Conclusion 77 Reference 78 국문초록 88Docto

Grating Aligned Ferroelectric Liquid Crystal Devices

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Cloud Condensation Nuclei and Ice-Nucleating Particles Over Tropical and Subtropical Regions in the Northern Hemisphere

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In this dissertation, I evaluate the health effects of the automobile (or vehicle) fuel economy. Automobile fuel economy is regulated by the Corporate Average Fuel Economy (CAFE) standards put into effect in 1975 in the United States primarily to reduce the oil consumption and dependency on oil import in response to the Oil Embargo in the 1970s. The health benefit was not thoroughly analyzed in policy analyses of CAFE standards. I hypothesize that better automobile fuel economy results in less mobile source air pollutants such as fine Particulate Matters (PM2.5), Carbon Monoxide (CO), and Nitrogen Dioxide (NO2), and hence improves air quality, which in turn reduces air pollutant related diseases such as asthma. Thus, CAFE standards have health benefits because CAFE standards increase the on-road vehicle fleet fuel economy. I seek empirical evidence of the health effects of automobile fuel economy through the improvement of air quality. Using vehicle registration and fuel consumption data, air pollutant data, health survey data, and other relevant data in the United States, I apply statistical mediation analysis techniques to assess the variation of asthma with respect to the changes of automobile fuel economy over time through the air pollutants mechanism. The empirical analysis results, under certain assumptions and with some limitation due to the data, support my key hypotheses: 1) there is a clear negative correlation between the automobile fuel economy and mobile source air pollutants over time; 2) there is a negative correlation between the fuel economy and asthma prevalence through the air pollutants mechanism; 3) empirical evidence supports that the air pollutants are the mediators through which automobile fuel economy affects health. This dissertation provides the empirical evidence of the health effects of automobile fuel economy improvement through improvements in air quality. It contributes to the literature and knowledge to the research community in two aspects: first, by identifying the health benefits of automobile fuel economy and an additional support to tighten the automobile fuel economy standards; second, by applying statistical mediation methods in econometric analysis

Non-energy Circular Economy Potential of Rice Husks: A Techno-eco-environmental Assessment

The non-energy circular bioeconomy potential of rice husks was examined via sustainability assessments, namely life cycle assessment (LCA), life cycle impact cost assessment (LCICA), and techno-economic assessment (TEA). The study was conducted with three objectives. The first objective was to review previous studies on the non-energy utilization potential of rice husks by the method of meta-analysis. This review followed a systematic approach where research papers were collected following a defined set of criteria. The study revealed 16 key utilization pathways, all of which showed promising results. However, a comprehensive sustainability assessment was lacking in all of the pathways. The second objective was to examine the circular bioeconomy potential of rice husks as a resource for bioplastic production. This study evaluated the techno-environmental assessment of three bioplastics, namely carboxymethyl cellulose, cellulose acetate, and cellulose nitrate relative to rice husks combustion. This provided information on the environmental impacts and the environmental impact costs of all three bioplastics. The result suggested that carboxymethylcellulose would be the most sustainable pathway, reducing the impact on human health and the cost of open-air combustion by 82% and 74%, respectively. The third objective was to examine the sustainable production of xylo-oligosaccharide from rice husk via a techno-economic and an environmental performance assessment. The study examined two production methods: autohydrolysis and enzymatic hydrolysis, considering a pilot and a large production scale for each. The results revealed that autohydrolysis is the best method to produce xylo-oligosaccharides, considering the damage to the environment and human health, and profitability (net profits of $7.1M and$ 42.4M for pilot and large-scale setups) hence, it is viable to thrive in the market

Non-energy Circular Economy Potential of Rice Husks: A Techno-eco-environmental Assessment

The non-energy circular bioeconomy potential of rice husks was examined via sustainability assessments, namely life cycle assessment (LCA), life cycle impact cost assessment (LCICA), and techno-economic assessment (TEA). The study was conducted with three objectives. The first objective was to review previous studies on the non-energy utilization potential of rice husks by the method of meta-analysis. This review followed a systematic approach where research papers were collected following a defined set of criteria. The study revealed 16 key utilization pathways, all of which showed promising results. However, a comprehensive sustainability assessment was lacking in all of the pathways. The second objective was to examine the circular bioeconomy potential of rice husks as a resource for bioplastic production. This study evaluated the techno-environmental assessment of three bioplastics, namely carboxymethyl cellulose, cellulose acetate, and cellulose nitrate relative to rice husks combustion. This provided information on the environmental impacts and the environmental impact costs of all three bioplastics. The result suggested that carboxymethylcellulose would be the most sustainable pathway, reducing the impact on human health and the cost of open-air combustion by 82% and 74%, respectively. The third objective was to examine the sustainable production of xylo-oligosaccharide from rice husk via a techno-economic and an environmental performance assessment. The study examined two production methods: autohydrolysis and enzymatic hydrolysis, considering a pilot and a large production scale for each. The results revealed that autohydrolysis is the best method to produce xylo-oligosaccharides, considering the damage to the environment and human health, and profitability (net profits of $7.1M and$ 42.4M for pilot and large-scale setups) hence, it is viable to thrive in the market

Sustainability of marketing systems: systeming interpretation of hybrid car manufacturer and consumer communications

The purpose of this qualitative macromarketing investigation is to explore the issue of the sustainability of marketing systems. Drawing on complex systems thinking, an alternative logic of marketing systems and a methodological basis for interpreting communicated meanings are developed. The alternative logic of marketing systems recognises the unity of a difference between a marketing system and its environment. This insight has become a cornerstone for synthesising the systeming methodology. Systeming comprises the philosophy, the model, and the method of interpreting communication-as-self-observation of marketing system agents. Data, communication by hybrid car manufacturers and consumers, were collected from netnographic sources such as corporate websites, reports posted online, weblogs, and consumer forums. The interpretation of these data was accomplished using systeming procedures, e.g. communication analysis, distinction identification, re-entry description, and logical level tracking. The systeming analysis of the hybrid car marketer and consumer communications illustrates that meaning-creation in the system is underpinned by purposeful human behaviour in reducing complexity of marketplace experience into a meaningful pattern, sustainability. Both manufacturers and consumers claim to become sustainable in reference to being unsustainable by creating self-referential differences, operating in different interaction contexts, and expanding meaning paradoxes. The interpretation shows that interactive meaning-creation in the system is inherently contradictory. Manufacturers expand (give a logical form to) contradictions through introducing hierarchical meaning structures, temporality, new functions, and communicative transvection. Consumers deal with the contradictions through enriching co-creation experiences and learning the proper continuation of specific hybrid car driving practices. The significant insight gained from this investigation is that the hybrid car marketing system is not a passive entity; it is the locus of purposefully expanding meanings. Two modes of sustainability with regard to the hybrid car marketing system can be distinguished: the content of communication that denotes enacted meanings of sustainability and the form of communication that indicates how sustainable these sustainability enactments are. The content/form distinction implies that the sustainability of the hybrid car marketing system is a matter of interactive meaning-creation between system agents. The sustainable development process, in at least a mobility domain, is driven by purposeful social interaction rather than static product attributes. This investigation is innovative because it a) offers a conceptualisation of a marketing system as a meaning flow; b) synthesises and compiles a methodology and method for interpreting communication in a marketing system; c) reveals systemic insights into the hybrid car marketing system; d) characterises the sustainability dimension of the hybrid car marketing system; e) explains a conceptual ground for reconciling the marketing system and society; f) provides a general macromarketing perspective to scrutinise recent conceptual developments in the marketing discipline; g) unifies marketing systems thinking with recent advancements in the marketing discipline, such as the service-dominant logic, and consumer culture theory; and, also, h) provides recommendations for a number of micro-managerial situations from a holistic perspective

An exploration of process oriented guided inquiry learning in undergraduate Chemistry classes

The research study explored student’s understanding of stereochemistry and their perceptions of learning chemistry in first year undergraduate chemistry classes following a modified Process-Oriented Guided Inquiry Learning (POGIL) that included group work. A 5-item two-tier stereochemistry concept diagnostic test (SCDT) was developed and administered to explore their understanding of stereochemistry concepts. The students’ perception of POGIL learning was gauged in an effort to establish construct and convergent validity to the SALG instrument

Volume 3 – Conference

We are pleased to present the conference proceedings for the 12th edition of the International Fluid Power Conference (IFK). The IFK is one of the world’s most significant scientific conferences on fluid power control technology and systems. It offers a common platform for the presentation and discussion of trends and innovations to manufacturers, users and scientists. The Chair of Fluid-Mechatronic Systems at the TU Dresden is organizing and hosting the IFK for the sixth time. Supporting hosts are the Fluid Power Association of the German Engineering Federation (VDMA), Dresdner Verein zur Förderung der Fluidtechnik e. V. (DVF) and GWT-TUD GmbH. The organization and the conference location alternates every two years between the Chair of Fluid-Mechatronic Systems in Dresden and the Institute for Fluid Power Drives and Systems in Aachen. The symposium on the first day is dedicated to presentations focused on methodology and fundamental research. The two following conference days offer a wide variety of application and technology orientated papers about the latest state of the art in fluid power. It is this combination that makes the IFK a unique and excellent forum for the exchange of academic research and industrial application experience. A simultaneously ongoing exhibition offers the possibility to get product information and to have individual talks with manufacturers. The theme of the 12th IFK is “Fluid Power – Future Technology”, covering topics that enable the development of 5G-ready, cost-efficient and demand-driven structures, as well as individual decentralized drives. Another topic is the real-time data exchange that allows the application of numerous predictive maintenance strategies, which will significantly increase the availability of fluid power systems and their elements and ensure their improved lifetime performance. We create an atmosphere for casual exchange by offering a vast frame and cultural program. This includes a get-together, a conference banquet, laboratory festivities and some physical activities such as jogging in Dresden’s old town.:Group 8: Pneumatics Group 9 | 11: Mobile applications Group 10: Special domains Group 12: Novel system architectures Group 13 | 15: Actuators & sensors Group 14: Safety & reliabilit

Goods and Services of Marine Bivalves

The aim of this book is to review and analyse the goods and services of bivalve shellfish. How they are defined, what determines the ecological functions that are the basis for the goods and services, what controversies in the use of goods and services exist, and what is needed for sustainable exploitation of bivalves from the perspective of the various stakeholders. The book is focused on the goods and services, and not on impacts of shellfish aquaculture on the benthic environment, or on threats like biotoxins; neither is it a shellfish culture handbook although it can be used in evaluating shellfish culture. The reviews and analysis are based on case studies that exemplify the concept, and show the strengths and weaknesses of the current applications. The multi-authored reviews cover ecological, economic and social aspects of bivalve goods and services. The book provides new insights for scientists, students, shellfish producers, policy advisors, nature conservationists and decision makers. This book is open access under the CC BY license.publishedVersio