Using Social Media Websites to Support Scenario-Based Design of Assistive Technology

Indiana University-Purdue University Indianapolis (IUPUI)Having representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies

Classification in Networked Data: A Toolkit and a Univariate Case Study

This paper1 is about classifying entities that are interlinked with entities for which the class is known. After surveying prior work, we present NetKit, a modular toolkit for classification in networked data, and a case-study of its application to networked data used in prior machine learning research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier, a relational classifier, and a collective inference procedure. Various existing node-centric relational learning algorithms can be instantiated with appropriate choices for these components, and new combinations of components realize new algorithms. The case study focuses on univariate network classification, for which the only information used is the structure of class linkage in the network (i.e., only links and some class labels). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate that very simple network-classification models perform quite well—well enough that they should be used regularly as baseline classifiers for studies of learning with networked data. The simplest method (which performs remarkably well) highlights the close correspondence between several existing methods introduced for different purposes—that is, Gaussian-field classifiers, Hopfield networks, and relational-neighbor classifiers. The case study also shows that there are two sets of techniques that are preferable in different situations, namely when few versus many labels are known initially. We also demonstrate that link selection plays an important role similar to traditional feature selectionNYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Explorative Graph Visualization

Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization

진료 내역 데이터를 활용한 딥러닝 기반의 건강보험 남용 탐지

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 조성준.As global life expectancy increases, spending on healthcare grows in accordance in order to improve quality of life. However, due to expensive price of medical care, the bare cost of healthcare services would inevitably places great financial burden to individuals and households. In this light, many countries have devised and established their own public healthcare insurance systems to help people receive medical services at a lower price. Since reimbursements are made ex-post, unethical practices arise, exploiting the post-payment structure of the insurance system. The archetypes of such behavior are overdiagnosis, the act of manipulating patients diseases, and overtreatments, prescribing unnecessary drugs for the patient. These abusive behaviors are considered as one of the main sources of financial loss incurred in the healthcare system. In order to detect and prevent abuse, the national healthcare insurance hires medical professionals to manually examine whether the claim filing is medically legitimate or not. However, the review process is, unquestionably, very costly and time-consuming. In order to address these limitations, data mining techniques have been employed to detect problematic claims or abusive providers showing an abnormal billing pattern. However, these cases only used coarsely grained information such as claim-level or provider-level data. This extracted information may lead to degradation of the model's performance. In this thesis, we proposed abuse detection methods using the medical treatment data, which is the lowest level information of the healthcare insurance claim. Firstly, we propose a scoring model based on which abusive providers are detected and show that the review process with the proposed model is more efficient than that with the previous model which uses the provider-level variables as input variables. At the same time, we devise the evaluation metrics to quantify the efficiency of the review process. Secondly, we propose the method of detecting overtreatment under seasonality, which reflects more reality to the model. We propose a model embodying multiple structures specific to DRG codes selected as important for each given department. We show that the proposed method is more robust to the seasonality than the previous method. Thirdly, we propose an overtreatment detection model accounting for heterogeneous treatment between practitioners. We proposed a network-based approach through which the relationship between the diseases and treatments is considered during the overtreatment detection process. Experimental results show that the proposed method classify the treatment well which does not explicitly exist in the training set. From these works, we show that using treatment data allows modeling abuse detection at various levels: treatment, claim, and provider-level.사람들의 기대수명이 증가함에 따라 삶의 질을 향상시키기 위해 보건의료에 소비하는 금액은 증가하고 있다. 그러나, 비싼 의료 서비스 비용은 필연적으로 개인과 가정에게 큰 재정적 부담을 주게된다. 이를 방지하기 위해, 많은 국가에서는 공공 의료 보험 시스템을 도입하여 사람들이 적절한 가격에 의료서비스를 받을 수 있도록 하고 있다. 일반적으로, 환자가 먼저 서비스를 받고 나서 일부만 지불하고 나면, 보험 회사가 사후에 해당 의료 기관에 잔여 금액을 상환을 하는 제도로 운영된다. 그러나 이러한 제도를 악용하여 환자의 질병을 조작하거나 과잉진료를 하는 등의 부당청구가 발생하기도 한다. 이러한 행위들은 의료 시스템에서 발생하는 주요 재정 손실의 이유 중 하나로, 이를 방지하기 위해, 보험회사에서는 의료 전문가를 고용하여 의학적 정당성여부를 일일히 검사한다. 그러나, 이러한 검토과정은 매우 비싸고 많은 시간이 소요된다. 이러한 검토과정을 효율적으로 하기 위해, 데이터마이닝 기법을 활용하여 문제가 있는 청구서나 청구 패턴이 비정상적인 의료 서비스 공급자를 탐지하는 연구가 있어왔다. 그러나, 이러한 연구들은 데이터로부터 청구서 단위나 공급자 단위의 변수를 유도하여 모델을 학습한 사례들로, 가장 낮은 단위의 데이터인 진료 내역 데이터를 활용하지 못했다. 이 논문에서는 청구서에서 가장 낮은 단위의 데이터인 진료 내역 데이터를 활용하여 부당청구를 탐지하는 방법론을 제안한다. 첫째, 비정상적인 청구 패턴을 갖는 의료 서비스 제공자를 탐지하는 방법론을 제안하였다. 이를 실제 데이터에 적용하였을 때, 기존의 공급자 단위의 변수를 사용한 방법보다 더 효율적인 심사가 이루어 짐을 확인하였다. 이 때, 효율성을 정량화하기 위한 평가 척도도 제안하였다. 둘째로, 청구서의 계절성이 존재하는 상황에서 과잉진료를 탐지하는 방법을 제안하였다. 이 때, 진료 과목단위로 모델을 운영하는 대신 질병군(DRG) 단위로 모델을 학습하고 평가하는 방법을 제안하였다. 그리고 실제 데이터에 적용하였을 때, 제안한 방법이 기존 방법보다 계절성에 더 강건함을 확인하였다. 셋째로, 동일 환자에 대해서 의사간의 상이한 진료 패턴을 갖는 환경에서의 과잉진료 탐지 방법을 제안하였다. 이는 환자의 질병과 진료내역간의 관계를 네트워크 기반으로 모델링하는것을 기반으로 한다. 실험 결과 제안한 방법이 학습 데이터에서 나타나지 않는 진료 패턴에 대해서도 잘 분류함을 알 수 있었다. 그리고 이러한 연구들로부터 진료 내역을 활용하였을 때, 진료내역, 청구서, 의료 서비스 제공자 등 다양한 레벨에서의 부당 청구를 탐지할 수 있음을 확인하였다.Chapter 1 Introduction 1 Chapter 2 Detection of Abusive Providers by department with Neural Network 9 2.1 Background 9 2.2 Literature Review 12 2.2.1 Abnormality Detection in Healthcare Insurance with Datamining Technique 12 2.2.2 Feed-Forward Neural Network 17 2.3 Proposed Method 21 2.3.1 Calculating the Likelihood of Abuse for each Treatment with Deep Neural Network 22 2.3.2 Calculating the Abuse Score of the Provider 25 2.4 Experiments 26 2.4.1 Data Description 27 2.4.2 Experimental Settings 32 2.4.3 Evaluation Measure (1): Relative Efficiency 33 2.4.4 Evaluation Measure (2): Precision at k 37 2.5 Results 38 2.5.1 Results in the test set 38 2.5.2 The Relationship among the Claimed Amount, the Abused Amount and the Abuse Score 40 2.5.3 The Relationship between the Performance of the Treatment Scoring Model and Review Efficiency 41 2.5.4 Treatment Scoring Model Results 42 2.5.5 Post-deployment Performance 44 2.6 Summary 45 Chapter 3 Detection of overtreatment by Diagnosis-related Group with Neural Network 48 3.1 Background 48 3.2 Literature review 51 3.2.1 Seasonality in disease 51 3.2.2 Diagnosis related group 52 3.3 Proposed method 54 3.3.1 Training a deep neural network model for treatment classi fication 55 3.3.2 Comparing the Performance of DRG-based Model against the department-based Model 57 3.4 Experiments 60 3.4.1 Data Description and Preprocessing 60 3.4.2 Performance Measures 64 3.4.3 Experimental Settings 65 3.5 Results 65 3.5.1 Overtreatment Detection 65 3.5.2 Abnormal Claim Detection 67 3.6 Summary 68 Chapter 4 Detection of overtreatment with graph embedding of disease-treatment pair 70 4.1 Background 70 4.2 Literature review 72 4.2.1 Graph embedding methods 73 4.2.2 Application of graph embedding methods to biomedical data analysis 79 4.2.3 Medical concept embedding methods 87 4.3 Proposed method 88 4.3.1 Network construction 89 4.3.2 Link Prediction between the Disease and the Treatment 90 4.3.3 Overtreatment Detection 93 4.4 Experiments 96 4.4.1 Data Description 97 4.4.2 Experimental Settings 99 4.5 Results 102 4.5.1 Network Construction 102 4.5.2 Link Prediction between the Disease and the Treatment 104 4.5.3 Overtreatment Detection 105 4.6 Summary 106 Chapter 5 Conclusion 108 5.1 Contribution 108 5.2 Future Work 110 Bibliography 112 국문초록 129Docto

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Development of multivariate and network models for the analysis of Big Data: applications in economics, insurance, and social sciences

In questa tesi sviluppo metodi statistici multivariati e di rete per lo studio di sistemi complessi. In particolare, focalizzo la mia analisi sullo studio di reti complesse bipartite e le loro applicazioni a (i) l'economia, per capire l'effetto di contagio tra istituti finanziari e stati sovrani, (ii) la sorveglianza nelle assicurazioni, per individuare comportamenti fraudolenti, e (iii) le scienze sociali, per studiare l'effetto delle politiche del REF sulle eccellenze nella ricerca delle università in UK.In this thesis I develop multivariate statistical and network methods for the study of complex systems. In particular, I focus my analysis on the study of bipartite complex networks and their applications to (i) economics to understand the contagion effect between sovereign and financial institutions, (ii) insurance surveillance to uncover fraudsters and (iii) social science to study the effect of the politics of REF on research excellence of universities in the UK

Analysis and Visualisation of Edge Entanglement in Multiplex Networks

Cette thèse présente une nouvelle méthodologie pour analyser des réseaux. Nous développons l'intrication d'un réseau multiplex, qui se matérialise sous forme d'une mesure d'intensité et d'homogénéité, et d'une abstraction, le réseau d'interaction des catalyseurs, auxquels sont associés des indices d'intrication. Nous présentons ensuite la mise en place d'outils spécifiques pour l'analyse visuelle des réseaux complexes qui tirent profit de cette méthodologie. Ces outils présente une vue double de deux réseaux,qui inclue une un algorithme de dessin, une interaction associant brossage d'une sélection et de multiples liens pré-attentifs. Nous terminons ce document par la présentation détaillée d'applications dans de multiples domaines.When it comes to comprehension of complex phenomena, humans need to understand what interactions lie within them.These interactions are often captured with complex networks. However, the interaction pluralism is often shallowed by traditional network models. We propose a new way to look at these phenomena through the lens of multiplex networks, in which catalysts are drivers of the interaction through substrates. To study the entanglement of a multiplex network is to study how edges intertwine, in other words, how catalysts interact. Our entanglement analysis results in a full set of new objects which completes traditional network approaches: the entanglement homogeneity and intensity of the multiplex network, and the catalyst interaction network, with for each catalyst, an entanglement index. These objects are very suitable for embedment in a visual analytics framework, to enable comprehension of a complex structure. We thus propose of visual setting with coordinated multiple views. We take advantage of mental mapping and visual linking to present simultaneous information of a multiplex network at three different levels of abstraction. We complete brushing and linking with a leapfrog interaction that mimics the back-and-forth process involved in users' comprehension. The method is validated and enriched through multiple applications including assessing group cohesion in document collections, and identification of particular associations in social networks.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

New Deep Neural Networks for Unsupervised Feature Learning on Graph Data

Graph data are ubiquitous in the real world, such as social networks, biological networks. To analyze graph data, a fundamental task is to learn node features to benefit downstream tasks, such as node classification, community detection. Inspired by the powerful feature learning capability of deep neural networks on various tasks, it is important and necessary to explore deep neural networks for feature learning on graphs. Different from the regular image and sequence data, graph data encode the complicated relational information between different nodes, which challenges the classical deep neural networks. Moreover, in real-world applications, the label of nodes in graph data is usually not available, which makes the feature learning on graphs more difficult. To address these challenging issues, this thesis is focusing on designing new deep neural networks to effectively explore the relational information for unsupervised feature learning on graph data. First, to address the sparseness issue of the relational information, I propose a new proximity generative adversarial network which can discover the underlying relational information for learning better node representations. Meanwhile, a new self-paced network embedding method is designed to address the unbalance issue of the relational information when learning node representations. Additionally, to deal with rich attributes associated to nodes, I develop a new deep neural network to capture various relational information in both topological structure and node attributes for enhancing network embedding. Furthermore, to preserve the relational information in the hidden layers of deep neural networks, I develop a novel graph convolutional neural network (GCN) based on conditional random fields, which is the first algorithm applying this kind of graphical models to graph neural networks in an unsupervised manner