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Electronic Health Records (EHR) data contain comprehensive longitudinal patient information, which is usually stored in databases in the form of either multi-relational structured tables or unstructured texts, e.g., clinical notes. EHR provides a useful resource to assist doctors' decision making, however, they also present many unique challenges that limit the efficient use of the valuable information, such as large data volume, heterogeneous and dynamic information, medical term abbreviations, and noisy nature caused by misspelled words. This dissertation focuses on the development and evaluation of advanced machine learning algorithms to solve the following research questions: (1) How to seek answers from EHR for clinical activity related questions posed in human language without the assistance of database and natural language processing (NLP) domain experts, (2) How to discover underlying relationships of different events and entities in structured tabular EHRs, and (3) How to predict when a medical event will occur and estimate its probability based on previous medical information of patients. First, to automatically retrieve answers for natural language questions from the structured tables in EHR, we study the question-to-SQL generation task by generating the corresponding SQL query of the input question. We propose a translation-edit model driven by a language generation module and an editing module for the SQL query generation task. This model helps automatically translate clinical activity related questions to SQL queries, so that the doctors only need to provide their questions in natural language to get the answers they need. We also create a large-scale dataset for question answering on tabular EHR to simulate a more realistic setting. Our performance evaluation shows that the proposed model is effective in handling the unique challenges about clinical terminologies, such as abbreviations and misspelled words. Second, to automatically identify answers for natural language questions from unstructured clinical notes in EHR, we propose to achieve this goal by querying a knowledge base constructed based on fine-grained document-level expert annotations of clinical records for various NLP tasks. We first create a dataset for clinical knowledge base question answering with two sets: clinical knowledge base and question-answer pairs. An attention-based aspect-level reasoning model is developed and evaluated on the new dataset. Our experimental analysis shows that it is effective in identifying answers and also allows us to analyze the impact of different answer aspects in predicting correct answers. Third, we focus on discovering underlying relationships of different entities (e.g., patient, disease, medication, and treatment) in tabular EHR, which can be formulated as a link prediction problem in graph domain. We develop a self-supervised learning framework for better representation learning of entities across a large corpus and also consider local contextual information for the down-stream link prediction task. We demonstrate the effectiveness, interpretability, and scalability of the proposed model on the healthcare network built from tabular EHR. It is also successfully applied to solve link prediction problems in a variety of domains, such as e-commerce, social networks, and academic networks. Finally, to dynamically predict the occurrence of multiple correlated medical events, we formulate the problem as a temporal (multiple time-points) and multi-task learning problem using tensor representation. We propose an algorithm to jointly and dynamically predict several survival problems at each time point and optimize it with the Alternating Direction Methods of Multipliers (ADMM) algorithm. The model allows us to consider both the dependencies between different tasks and the correlations of each task at different time points. We evaluate the proposed model on two real-world applications and demonstrate its effectiveness and interpretability. / Doctor of Philosophy / Healthcare is an important part of our lives. Due to the recent advances of data collection and storing techniques, a large amount of medical information is generated and stored in Electronic Health Records (EHR). By comprehensively documenting the longitudinal medical history information about a large patient cohort, this EHR data forms a fundamental resource in assisting doctors' decision making including optimization of treatments for patients and selection of patients for clinical trials. However, EHR data also presents a number of unique challenges, such as (i) large-scale and dynamic data, (ii) heterogeneity of medical information, and (iii) medical term abbreviation. It is difficult for doctors to effectively utilize such complex data collected in a typical clinical practice. Therefore, it is imperative to develop advanced methods that are helpful for efficient use of EHR and further benefit doctors in their clinical decision making. This dissertation focuses on automatically retrieving useful medical information, analyzing complex relationships of medical entities, and detecting future medical outcomes from EHR data. In order to retrieve information from EHR efficiently, we develop deep learning based algorithms that can automatically answer various clinical questions on structured and unstructured EHR data. These algorithms can help us understand more about the challenges in retrieving information from different data types in EHR. We also build a clinical knowledge graph based on EHR and link the distributed medical information and further perform the link prediction task, which allows us to analyze the complex underlying relationships of various medical entities. In addition, we propose a temporal multi-task survival analysis method to dynamically predict multiple medical events at the same time and identify the most important factors leading to the future medical events. By handling these unique challenges in EHR and developing suitable approaches, we hope to improve the efficiency of information retrieval and predictive modeling in healthcare.

Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from different perspectives in multiple modalities, and to use large amounts of commonsense knowledge while performing visual or textual tasks. Inspired by that ability, we approach commonsense learning as leveraging perspectives from multiple modalities for images and text in the context of vision and language tasks. Given a target task (e.g., textual reasoning, matching images with captions), our system first represents input images and text in multiple modalities (e.g., vision, text, abstract scenes and facts). Those modalities provide different perspectives to interpret the input images and text. And then based on those perspectives, the system performs reasoning to make a joint prediction for the target task. Surprisingly, we show that interpreting textual assertions and scene descriptions in the modality of abstract scenes improves performance on various textual reasoning tasks, and interpreting images in the modality of Visual Question Answering improves performance on caption retrieval, which is a visual reasoning task. With grounding, imagination and question-answering approaches to interpret images and text in different modalities, we show that learning commonsense knowledge from multiple modalities effectively improves the performance of downstream vision and language tasks, improves interpretability of the model and is able to make more efficient use of training data. Complementary to the model aspect, we also study the data aspect of commonsense learning in vision and language. We study active learning for Visual Question Answering (VQA) where a model iteratively grows its knowledge through querying informative questions about images for answers. Drawing analogies from human learning, we explore cramming (entropy), curiosity-driven (expected model change), and goal-driven (expected error reduction) active learning approaches, and propose a new goal-driven scoring function for deep VQA models under the Bayesian Neural Network framework. Once trained with a large initial training set, a deep VQA model is able to efficiently query informative question-image pairs for answers to improve itself through active learning, saving human effort on commonsense annotations. / Ph. D.

During the unprecedented global pandemic of Covid-19, the general public is suffering from inaccurate Covid-19 related information including outdated information and fake news. The most used media: TV, social media, newspaper, and radio are incompetent in providing certitude and flash updates that people are seeking. In order to cope with this challenge, several public data resources that are dedicated to providing Covid-19 information were born. They rallied with experts from different fields to provide authoritative and up-to-date pandemic updates. However, the general public cannot still make complete use of such resources since the learning curve is too steep, especially for the aged and under-aged users. To address this problem, in this Thesis, we propose a question answering system that can be interacted with using simple natural language-based sentences. While building this system, we investigate qualified public data resources and from the data content they are providing, and we collect a set of frequently asked questions for Covid-19 tracking. We further build a dedicated dataset named CovidQA for evaluating the performance of the question answering system with different models. Based on the new dataset, we assess multiple machine learning-based models that are built for retrieving relevant information from databases, and then propose two empirical models which utilize the pre-defined templates to generate SQL queries. In our experiments, we demonstrate both quantitative and qualitative results and provide a comprehensive comparison between different types of methods. The results show that the proposed template-based methods are simple but effective in building question answering systems for specific domain problems. / Master of Science / During the unprecedented global pandemic of Covid-19, the general public is suffering from inaccurate Covid-19 related information including outdated information and fake news. The most used media: TV, social media, newspaper, and radio are incompetent in providing certitude and flash updates that people are seeking. In order to cope with this challenge, several public data resources that are dedicated to providing Covid-19 information were born. They rallied with experts from different fields to provide authoritative and up-to-date pandemic updates. However, there is room for improvement in terms of user experience. To address this problem, in this Thesis, we propose a system that can be interacted with using natural questions. While building this system, we evaluate and choose six qualified public data providers as the data sources. We further build a testing dataset for evaluating the performance of the system. We assess two Artificial Intelligence-powered models for the system, and then propose two rule-based models for the researched problem. In our experiments, we provide a comprehensive comparison between different types of methods. The results show that the proposed rule-based methods are simple but effective in building such systems.

Yes / This research aims to identify users who are posting as well as encouraging others to post low-quality and duplicate contents on community question answering sites. The good guys called Caretakers and the bad guys called Reputation Collectors are characterised by their behaviour, answering pattern and reputation points. The proposed system is developed and analysed over publicly available Stack Exchange data dump. A graph based methodology is employed to derive the characteristic of Reputation Collectors and Caretakers. Results reveal that Reputation Collectors are primary sources of low-quality answers as well as answers to duplicate questions posted on the site. The Caretakers answer limited questions of challenging nature and fetches maximum reputation against those questions whereas Reputation Collectors answers have so many low-quality and duplicate questions to gain the reputation point. We have developed algorithms to identify the Caretakers and Reputation Collectors of the site. Our analysis finds that 1.05% of Reputation Collectors post 18.88% of low quality answers. This study extends previous research by identifying the Reputation Collectors and 2 how they collect their reputation points.

Probabilistic databases (PDBs) are usually incomplete, e.g., contain only the facts that have been extracted from the Web with high confidence. However, missing facts are often treated as being false, which leads to unintuitive results when querying PDBs. Recently, open-world probabilistic databases (OpenPDBs) were proposed to address this issue by allowing probabilities of unknown facts to take any value from a fixed probability interval. In this paper, we extend OpenPDBs by Datalog± ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before. We show that the dichotomy between P and PP in (Open)PDBs can be lifted to the case of first-order rewritable positive programs (without negative constraints); and that the problem can become NP^PP-complete, once negative constraints are allowed. We also propose an approximating semantics that circumvents the increase in complexity caused by negative constraints.

In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs). We call these combinations ontological possibilistic networks (OP-nets). We define skyline and k-rank answers to CQs under preferences and provide complexity (including data tractability) results for deciding consistency and CQ skyline membership for OP-nets. We show that our formalism has a lower complexity than a similar existing formalism.

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

by Li Chee Kwong, Wong Chi Cheong Raymond. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 40-41). / ABSTRACT --- p.iii / TABLE OF CONTENTS --- p.iv / LIST OF FIGURES --- p.vi / LIST OF TABLES --- p.vii / ACKNOWLEDGMENTS --- p.viii / Chapter / Chapter I. --- INTRODUCTION --- p.1 / The Cordless Phone Market --- p.1 / The Answering Machine Market --- p.2 / The Cordless Phone with Digital Answering Machine Opportunity --- p.2 / National Semiconductor Corporation --- p.3 / Statement of Objectives --- p.4 / Literature Review --- p.5 / Chapter II. --- METHODOLOGY --- p.8 / Research Design and Data Collection --- p.8 / Limitations --- p.10 / Chapter III. --- FINDINGS --- p.11 / Market Potential --- p.11 / Customer Profile --- p.15 / Technology Trend and Semiconductor Opportunities --- p.16 / Compression Technology --- p.18 / High Integration --- p.18 / Low Voltage Operation --- p.19 / Non-Volatile Storage --- p.19 / Multi-Channel Capability --- p.19 / "Caller ID, Speaker Phone, Voice Recognition" --- p.20 / National Against the Competition --- p.21 / DSP Group --- p.21 / Texas Instruments --- p.22 / Zilog --- p.22 / Toshiba --- p.23 / National --- p.23 / Threats --- p.24 / Chapter IV. --- RECOMMENDATIONS AND MARKETING PLAN --- p.25 / Recommendations --- p.25 / Marketing Plan --- p.25 / Target Market --- p.25 / Marketing Objective --- p.25 / Value Proposition --- p.26 / Marketing Strategy --- p.26 / Product Strategy --- p.26 / Pricing Strategy --- p.28 / Distribution Strategy --- p.29 / Promotional Strategy --- p.30 / Financial Analysis --- p.31 / Risk and Exit Criteria --- p.33 / Conclusion --- p.34 / APPENDIX I: EXPERIENCE SURVEY --- p.35 / APPENDIX II: ROAD MAP --- p.36 / APPENDIX III: FINANCIAL ANALYSIS --- p.37 / BIBLIOGRAPHY --- p.40

by Chan Kit Ping, Wendy. / Includes questionnaire in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 50-52). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iv / LIST OF FIGURES --- p.vi / LIST OF TABLES --- p.vii / ACKNOWLEDGEMENT --- p.viii / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- FACTORS THAT CONTRIBUTE TO THE DEVELOPMENT OF TELEPHONE BANKING SERVICE IN HONG KONG --- p.7 / High Telephone Usage in Hong Kong --- p.7 / Telephone Banking Service as Differentiation Weapon --- p.8 / Hectic Life Style of Hong Kong People --- p.8 / High Property Prices in Hong Kong --- p.8 / Labor Shortage in Hong Kong --- p.9 / Chapter III. --- INDUSTRY REVIEW --- p.10 / Development of Telephone Banking Service in Hong Kong --- p.10 / Procedure of Using Telephone Banking Service --- p.12 / Variants of Telephone Banking Services --- p.16 / Chapter IV. --- LITERATURE REVIEW --- p.20 / Adoption of a Service Innovation --- p.21 / Adoption of New Banking Technology --- p.21 / Hypotheses Setting --- p.25 / Chapter V. --- RESEARCH METHOD --- p.27 / Research Information Needed --- p.27 / Research Design --- p.29 / Demographic Characteristics of the Sample --- p.33 / Chapter VI. --- RESEARCH ANALYSIS --- p.34 / Awareness of Telephone Banking Service in Hong Kong --- p.34 / Way of Learning about Telephone Banking Service --- p.34 / Adoption of the Service --- p.35 / Reasons for Using the Service --- p.35 / Frequency of Using Telephone Banking Services --- p.36 / Most Frequently Used Services --- p.36 / Satisfaction Level of Users --- p.37 / Reasons for Not Using the Service --- p.37 / Attitude Towards Telephone Banking Service --- p.37 / Psychographic Characteristics of Users vs Non-users --- p.38 / Chapter VII. --- RECOMMENDATIONS --- p.40 / Ways to Recruit New Users --- p.40 / Ways to Encourage More Usage from Existing Users --- p.43 / Operational Recommendations --- p.44 / Chapter VIII. --- LIMITATIONS AND SUGGESTIONS --- p.46 / Questionnaire Setting --- p.45 / Sample Size --- p.47 / The Use of Personal Questions --- p.48 / Suggestions for Future Researches --- p.48 / BIBLIOGRAPHY --- p.50 / APPENDIX --- p.53 / Questionnaire --- p.53 / Figures 1-13 --- p.63-80 / Tables 1-13 --- p.81-93