다양한 질환 심각도 하에서 말초동맥 질환 위치 식별을 위한 딥러닝 기반 도메인 적응 방법 연구

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022.2. 윤병동.This paper's primary purpose is to develop a blood pressure waveform (BPW) based deep learning diagnosis model for identifying peripheral arterial disease (PAD) on frequent PAD occurrence arteries. Two issues make it hard to obtain a generalized PAD diagnosis model with a data-driven approach: 1) domain discrepancy resulted from the differences of disease severity and occurring location, 2) data imbalance resulted from the symptomless characteristic of mild PAD. To train a generalized PAD diagnosis model considering practical issues, we propose auxiliary tasks-assisted maximum classifier discrepancy for supervised domain adaptation. The proposed model is validated using virtual patients' BPWs generated from the transmission line model under various disease severity levels. The results show that the proposed model has a superior performance for identifying PAD locations under various disease severity levels. This finding indicates the feasibility of the proposed diagnosis model to real hospitals for identifying the PAD locations in the lower extremities under various disease severity.본 논문의 주요 목적은 말초동맥 질환 빈번 발생 동맥에서 말초 동맥 질환을 식별하기 위한 혈압 파형 기반 딥러닝 진단 모델을 개발하는 것이다. 데이터 기반 방식으로 일반화된 말초동맥 질환 진단 모델을 얻기 위해서는 2가지 문제점이 있다: 1) 질환 심각도와 발병 위치의 차이로 인한 도메인 불일치, 2) 말초동맥 질환 초기 증상이 없다는 특징으로 인한 데이터 불균형. 실제 문제를 고려하여 일반화된 말초동맥 질환 진단 모델 훈련을 위해, 최대 분류 불일치 방법에 두가지 보조 태스크를 추가한 지도 도메인 적응 방법을 제안한다. 제안된 모델은 다양한 질병 심각도 수준에서 전송 선로 모델에서 생성된 가상 환자의 혈압파형을 사용하여 검증된다. 결과는 제안된 모델이 다양한 질병 심각도 수준에서 PAD 위치를 식별하기 위한 우수한 성능을 가지고 있음을 보여준다. 이 결과는 다양한 질병 심각도에서 하지의 PAD 위치를 식별하기 위해 제안된 진단 모델을 실제 병원에 적용할 가능성을 나타낸다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Structure of the Thesis 3 Chapter 2. Materials and Methods 4 2.1 Problem Definition of naïve data-driven approach 4 2.2 Proposed Method for Training Generalized PAD Diagnosis Model 5 2.2.1 Domain Adaptation 5 2.2.2 Maximum Classifier Discrepancy 6 2.2.3 Proposed Methods 10 2.3 Virtual PAD Patients’ BPW Data Generation 14 2.3.1 Transmission Line Model 14 2.3.2 Setting for Virtual PAD Patients 17 2.3.3 Data Description 18 2.4 Overall Procedure 20 Chapter 3. Results 22 3.1 Compared Methods 22 3.2 Results 22 Chapter 4. Discussion 28 4.1 Efficacy of Proposed Learning Method 28 4.2 Effects of Domain Adaptation 29 4.3 Potential for Practical Applicability 30 Chapter 5. Conclusions 31 5.1 Summary and Contributions 31 5.2 Suggestions for Future Research 32 References 35 Abstract (Korean) 41석

Exploring Resilience Models in a Sample of Combat-Exposed Military Service Members and Veterans: A Comparison and Commentary

Background: The term resilience is applied in numerous ways in the mental health field, leading to different perspectives of what constitutes a resilient response and disparate findings regarding its prevalence following trauma. Objective: illustrate the impact of various definitions on our understanding and prevalence of resilience, we compared various resilience definitions (absence of PTSD, absence of current mental health diagnosis, absence of generalized psychological distress, and an alternative trauma load–resilience discrepancy model of the difference between actual and predicted distress given lifetime trauma exposure) within a combat-exposed military personnel and veteran sample. Method: In this combat-trauma exposed sample (N = 849), of which approximately half were treatment seeking, rates of resilience were determined across all models, the kappa statistic was used to determine the concordance and strength of association across models, and t-tests examined the models in relation to a self-reported resilience measure. Results: Prevalence rates were 43.7%, 30.7%, 87.4%, and 50.1% in each of the four models. Concordance analyses identified 25.7% (n = 218) considered resilient by all four models (kappa = .40, p \u3c .001). Correlations between models and self-reported resilience were strong, but did not fully overlap. Conclusions:The discussion highlights theoretical considerations regarding the impact of various definitions and methodologies on resilience classifications, links current findings to a systems-based perspective, and ends with suggestions for future research approaches on resilience