Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Integration and Visualization Public Health Dashboard: The medi plus board Pilot Project

Traditional public health surveillance systems would benefit from integration with knowledge created by new situation-aware realtime signals from social media, online searches, mobile/sensor networks and citizens' participatory surveillance systems. However, the challenge of threat validation, cross-verification and information integration for risk assessment has so far been largely untackled. In this paper, we propose a new system, medi+board, monitoring epidemic intelligence sources and traditional case-based surveillance to better automate early warning, cross-validation of signals for outbreak detection and visualization of results on an interactive dashboard. This enables public health professionals to see all essential information at a glance. Modular and configurable to any 'event' defined by public health experts, medi+board scans multiple data sources, detects changing patterns and uses a configurable analysis module for signal detection to identify a threat. These can be validated by an analysis module and correlated with other sources to assess the reliability of the event classified as the reliability coefficient which is a real number between zero and one. Events are reported and visualized on the medi+board dashboard which integrates all information sources and can be navigated by a timescale widget. Simulation with three datasets from the swine flu 2009 pandemic (HPA surveillance, Google news, Twitter) demonstrates the potential of medi+board to automate data processing and visualization to assist public health experts in decision making on control and response measures

The association between ethnicity and the delay time in seeking medical care for chest pain: a systematic review

Made available in accordance with the Publisher's Author's Permissions policyBackground: Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity worldwide, and chest pain is one of the most common symptoms of ACSs. A rapid response to chest pain by patients and appropriate management by health professionals are vital to improve survival rates. People from different ethnic groups are likely to have different perceptions of chest pain, its severity and the need for urgent treatment. These differences in perception may contribute to differences in response to chests pain and precipitate unique coping strategies. Delay in seeking medical care for chest pain in the general population has been well documented; however, limited studies have focused on delay times within ethnic groups. There is little research to date as to whether ethnicity is associated with the time taken to seek medical care for chest pain. Consequently, addressing this gap in knowledge will play a crucial role in improving the health outcomes of culturally and linguistically diverse (CALD) patients suffering from chest pain and for developing appropriate clinical practice and public awareness for these populations. Objectives: The current review aimed to determine if there is an association between ethnicity and delay in seeking medical care for chest pain among CALD populations. Inclusion criteria Types of participants: Patients from different ethnic minority groups presenting to emergency departments (EDs) with chest pain. Types of exposure: The current review will examine studies that evaluate the association between ethnicity and delay in seeking medical care for chest pain among CALD populations. Types of studies: The current review will consider quantitative studies including randomized controlled trials (RCTs), non-RCTs, quasi-experimental, before and after studies, prospective and retrospective cohort studies, case-control studies and analytical cross-sectional studies. Outcomes: The current review will consider studies that measure delay time as the main outcome. The time will be measured as the interval between the time of symptom onset and time to reach an ED. Search strategy: A comprehensive search was undertaken for relevant published and unpublished studies written in English with no date restriction. All searches were conducted in October 2014. We searched the following databases: MEDLINE, PubMed, Cochrane Central Register of Controlled Trials, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, ProQuest (health databases only), Informit, Sociological Abstracts, Scopus and Web of Science. The search for unpublished studies included a wide range of ‘gray literature’ sources including national libraries, digital theses repositories and clinical trial registries. We also targeted specific health research, specialist cardiac, migrant health, and emergency medicine organizational websites and/or conferences. We also checked the reference lists of included studies and contacted authors when further details about reported data was required to make a decision about eligibility. Methodological quality: Papers selected for retrieval were assessed by two independent reviewers for methodological validity prior to being included in the review. Validity was assessed using standardized critical appraisal instruments from the Joanna Briggs Institute. Adjudication was produced by the third reviewer. Data extraction: Data were extracted from included articles by two independent reviewers using the standardized data extraction tool from the Joanna Briggs Institute. Data synthesis: The extracted data were synthesized into a narrative summary. Meta-analysis could not be performed due to the heterogeneity of study protocols and methods used to measure outcomes. Results: A total of 10 studies, with a total of 1,511,382 participants, investigating the association between ethnicity and delay met the inclusion criteria. Delay times varied across ethnic groups, including Black, Hispanic, Asian, South Asian, Southeast Asian and Chinese. Seven studies reported delay in hours and ranged from 1.90 to 3.10 h. Delay times were longer among CALD populations than the majority population. The other three studies reported delay time in categories of time (e.g. <1, <4 and <6 h) and found larger proportions of later presentations to the EDs among ethnic groups compared with the majority groups. Conclusion: There is evidence of an association between ethnicity and time taken in seeking medical care for chest pain, with patients from some ethnic minorities (e.g. Black, Asian, Hispanic and South Asian) taking longer than those of the majority population. Health promotions and health campaigns focusing on these populations are indicated

The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges

Where are you talking about? Advances and Challenges of Geographic Analysis of Text with Application to Disease Monitoring

The Natural Language Processing task we focus on in this thesis is Geoparsing. Geoparsing is the process of extraction and grounding of toponyms (place names). Consider this sentence: "The victims of the Spanish earthquake off the coast of Malaga were of American and Mexican origin." Four toponyms will be extracted (called Geotagging) and grounded to their geographic coordinates (called Toponym Resolution). However, our research goes further than any previous work by showing how to distinguish the literal place(s) of the event (Spain, Malaga) from other linguistic types/uses such as nationalities (Mexican, American), improving downstream task accuracy. We consolidate and extend the Standard Evaluation Framework, discuss key research problems, then present concrete solutions in order to advance each stage of geoparsing. For geotagging, as well as training a SOTA neural Location-NER tagger, we simplify Metonymy Resolution with a novel minimalist feature extraction combined with an LSTM-based classifier, matching SOTA results. For toponym resolution, we deploy the latest deep learning methods to achieve SOTA performance by augmenting neural models with hitherto unused geographic features called Map Vectors. With each research project, we provide high-quality datasets and system prototypes, further building resources in this field. We then show how these geoparsing advances coupled with our proposed Intra-Document Analysis can be used to associate news articles with locations in order to monitor the spread of public health threats. To this end, we evaluate our research contributions with production data from a real-time downstream application to improve geolocation of news events for disease monitoring. The data was made available to us by the Joint Research Centre (JRC), which operates one such system called MediSys that processes incoming news articles in order to monitor threats to public health and make these available to a variety of governmental, business and non-profit organisations. We also discuss steps towards an end-to-end, automated news monitoring system and make actionable recommendations for future work. In summary, the thesis aims are twofold: (1) Generate original geoparsing research aimed at advancing each stage of the pipeline by addressing pertinent challenges with concrete solutions and actionable proposals. (2) Demonstrate how this research can be applied to news event monitoring to increase the efficacy of existing biosurveillance systems, e.g. European Commission’s MediSys.I was generously funded by DREAM CDT, which was funded by NERC of UKRI

Epidemiology of Periodontal Health:diagnosis, trends, and systemic relationships

As a prevalent oral disease, periodontitis leads to the loss of tooth-supporting tissue and eventually tooth loss. It is associated with a variety of systemic conditions and is considered a public health problem worldwide. The thesis aimed to understand the periodontal health problem at a population level based on epidemiological principles and methods. We systematically reviewed the definitions of periodontal health used in the literature. There is significant heterogeneity in measuring methods, periodontal parameters, and cut-off values. Tooth loss related to periodontitis (TLPD) is viewed as a critical parameter to stage periodontitis severity. We proposed a surrogate indicator (periodontal status of adjacent teeth) to determine the TLPD when the reasons for extracted teeth are lacking. In the descriptive study, socioeconomic inequalities in oral hygiene and periodontal status were present in the Netherlands over two decades. The analytical studies were performed to understand the determinants and systemic links of periodontal health problems in the population. Apart from professional periodontal care and optimal oral hygiene measures, maintaining periodontal health can be achieved through non-pharmacological strategies (e.g., dietary and lifestyle adjustments). Consuming a pro-inflammatory diet indicated by the energy-adjusted dietary inflammatory index (E-DII) score is related to periodontal disease. Relatively stronger associations were seen in older adults and males. Periodontal inflammation was associated with poor cognitive performance in the elderly. Systemic inflammation (as measured by WBC count) was an explanatory mediator of this association. In the prospective cohort study, concomitant presence of cognitive impairment and periodontitis seemed to increase the risks of all-cause and cardiometabolic mortality among older adults

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis

Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on "correlation's correlation" has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely "hybrid high-order FC networks" by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome