Data Science and Ebola

Data Science---Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things, and generating even more data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The science that is transforming this ocean of data into a sea of knowledge is called data science. This lecture will discuss how data science has changed the way in which one of the most visible challenges to public health is handled, the 2014 Ebola outbreak in West Africa.Comment: Inaugural lecture Leiden Universit

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

Designing a training tool for imaging mental models

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network

Desenvolvimento de um jogo virtual simulado em suporte básico de vida

Objective: To validate the content of a virtual learning object in the format of a Role Playing Game – educational simulation game about basic life support, aimed at academics and health professionals. Method: Methodological, construction and validation study with qualitative data approach on the content of a virtual learning object, conducted between August and September 2016. Results: The game was developed in 13 screens, of which nine presented contents of basic life support, and the others presented general guidelines for progress in the game. The five suggestions of the experts were accepted by the researchers, and were mostly related to organization, clarity and vocabulary. No item was considered inappropriate by the judges, and the game had a mean content validity index of 0.96 and a Kappa value of 0.92. In the Likert scale evaluation, the game was considered in all analyzes as an excellent content for a virtual learning object. Conclusion: This learning technology is expected to support teaching of basic life support techniques for academics and health professionals, and to stimulate the development of similar teaching strategies in other scenarios, in order to bring advancements to the design of health training processes

Measurements, Models, Systems and Design

531 s.