Bach, Kerstin

Denaxas, Spiros

Dobson, Richard

Ibrahim, Zina

Massie, Stewart

Sani, Sadiq

Wiratunga, Nirmalie

Wu, Honghan

English

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      PrefaceCitation for published version:Ibrahim, Z, Wu, H, Bach, K, Dobson, R, Denaxas, S, Wiratunga, N, Massie, S & Sani, S 2017, 'Preface: The2nd International Workshop on Knowledge Discovery in Healthcare Data (KDH)', 2nd InternationalWorkshop on Knowledge Discovery in Healthcare Data, KDH at IJCAI 2017, Melbourne, Australia, 20/08/17pp. 3-4.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 11. May. 2020

Preface:The 2nd International Workshop on Knowledge Discovery in Healthcare Data (KDH)

Ibrahim, Z

Wu, H

Bach, K

Dobson, R

Denaxas, S

Wiratunga, N

Massie, S

Sani, S

UCL Discovery

PrefaceThe 2nd International Workshop onKnowledge Discovery in Healthcare Data (KDH)IntroductionThe notion of a learning healthcare system has been put for-ward to denote the translation of routinely collected data intoknowledge that drives the continual improvement of medicalcare by seamlessly embedding learned best practices in thehealthcare delivery process. This notion has been describedin many forms, but each follows a similar cycle of assem-bling, analyzing and interpreting data from multiple sources(clinical records, guidelines, patient-provided data includingwearables, omic data, etc..), followed by feeding the acquiredknowledge back into clinical practice. This framework aimsto provide personalised recommendations and decision sup-port tools to aid both patients and care providers, to improveoutcomes and personalise care.Figure 1: Aspects of knowledge discovery in healthcareThe idea of a learning healthcare system poses grand tech-nical challenges in terms of: 1) data extraction, organisationand assembly of the large amounts structured and free-textdata contained within the data sources, 2) near real time ana-lytics and knowledge discovery from the large, temporal anduncertainty-ridden healthcare data and 3) the design of toolssupporting clinical decision making as well as self manage-ment and care by patients in an autonomous and efficientmanner, without jeopardising existing clinical workflows orthe privacy of patient data. Therefore, the notion of the learn-ing healthcare system encompasses research in prominent ar-eas of Artificial Intelligence including language engineering,data mining, knowledge representation and reasoning, learn-ing and autonomous systems.The workshop received 13 submissions that were peer-reviewed by at least three reviewers each. After the reviewphase, 2 long papers and 5 short papers were accepted forpresentation at the workshop. Among the accepted papers,the current trend of applying deep learning can be seen hereas well, three papers use deep learning methods on health caredata, while other methods used are: case-based reasoning,natural language processing or time series analysis.Invited Speakers1. Daniel Sonntag, German Research Centre of Artifi-cial Intelligence, Saarbruecken, Germany sBio: Daniel Sonntag (German Research Center for Ar-tificial Intelligence, DFKI) is a Principal Researcherand Research Fellow. He has been teaching since2010 at Saarland University and the Technical Uni-versity of Kaiserslautern. His research interests in-clude multimodal and mobile AI-based interfaces, nat-ural language processing, dialogue systems, common-sense modelling, and semantic machine learning meth-ods for cognitive computing and improved usability.This includes intelligent user interfaces (IUIs), multi-modal multisensor interfaces for medical and health sys-tems in particular, common-sense and (interactive) ma-chine learning methods for human computer interfaces,knowledge discovery, information extraction, and cog-nitive modelling with ontologies.He has published over 120 scientific articles, and hasbeen recipient of the German High Tech ChampionAward in 2011 and the AAAI Recognition and IAAIDeployed Application Award in 2013. He is an edito-rial board member of the German Journal on ArtificialIntelligence (KI). Currently, he leads both national andEuropean projects from the Federal Ministry of Educa-tion and Research, the Federal Ministry for EconomicAffairs and Energy, and Horizon 2020.Title: Multimodal Multisensor Interfaces for Medicaland Health SystemsAbstract: We discuss the trends of multimodal-multisensor interfaces of medical and health systems andemphasizes the theoretical foundations of multimodal3interfaces and systems in the healthcare domain, namelymultimodal interaction, distributing multimodal pro-cessing into applications, and multisensory-multimodalfacilitation of cognition in medical and health systems.We aim to provide a better basis for motivating and ac-celerating future interfaces for medical and health sys-tems. Therefore, we will provide many examples of ex-isting and futuristic systems. The goal is to create a pathfor understanding how to design more effective medi-cal systems in the future. The main applications aremedical knowledge acquisition by intelligent user inter-faces; networked embedded systems development andsensors development in activity monitoring of humansby non-intrusive sensors; and knowledge integration to-wards clinical decision support.2. Yuan-Fang Li, Monash University, Victoria, Aus-traliaBio: Yuan-Fang Li is a senior lecturer at Faculty ofInformation Technology, Monash University, Australia.He received his PhD in computer science from NationalUniversity of Singapore in 2006. His research inter-ests include knowledge graphs, knowledge representa-tion and reasoning, ontology languages, and softwareengineering.Title: Who does what: expertise discovery in biomedi-cal researchAbstract: For a large research institution and a broad re-search discipline such as the healthcare and life sciences,it is a highly important and very challenging task to cap-ture researchers’ expertise, and to match researchers byexpertise to assist in identifying inter-disciplinary col-laboration opportunities and in making informed policydecisions. The challenges are multi-dimensional, stem-ming from the needs to (a) provide thorough coverage ofthe breadth and depth of the disciplinary areas, (b) de-velop accurate representation of researcher’s expertise,and (c) process large volumes of data efficiently. Med-ical Subject Headings (MeSH), a comprehensive tax-onomy for the life sciences, has been widely used forindexing MEDLINE publications. In this talk, I willpresent a novel framework for capturing and matchingresearch expertise based on knowledge discovered frompublications and encoded in MeSH.Accepted PapersThe following full papers presenting original research workswere accepted. Chen et al. describe a prototype mobile ap-plication for use by patients with type 1 diabetes to informexercise decisions based on blood glucose levels. The paperdescribes a case-based reasoning application for the person-alised recommendation for diabetics during exercise.Rubin et al. present a deep-learning approach for heartsound classifications. Specifically, the sound data is first con-verted into a heat-map using popular MFCC approach, thena convolutional neural network is trained to do the classifi-cation. The approach has been applied on 2016 PhysioNetComputing in Cardiology challenge and achieved a reason-ably good overall score - 8th out of 48 teams.Choo et al. explore using Virtual Reality as part of thetreatment for Agoraphobia and Social phobia. In the ap-proach the patients are exposed to their phobia in a virtualenvironment. The intuition is that exposure to their phobiacan help be overcome the irrational fear. The approach ispart of a wider Cognitive behavioural therapy approach andhas the benefit that the exposure in virtual space can be bettercontrolled and supervised.Nguyen et al. The paper presents a deep learning approachto handle irregularities and missing data in clinical time se-ries, for mortality prediction.Ormandy et al. present a technique to learn a representationof treatment and diagnoses using the well-known skip-grammodel applied to ICD9 codes. The authors focus on patientsimilarity based on their medication and diagnosis.Wang et al. describe a study on psyco-env corpus, whichaims at annotating published studies for facilitating knowl-edge discovery on pathologies of mental diseases. The cor-pus of this paper focuses on the correlations between mentaldiseases and environmental factors. In addition to the cor-pus, the open source annotation tool should have broad valuebeyond this use case.Zhu et al. introduce a graph based approach to mining ad-verse drug events from MEDLINE papers. The authors use apublicly available database to mine for adverse drug events,compare their graph based clustering approach to other meth-ods. Their results show a slight improvement in accuracy overbaseline methods. Short papers report on work in progress,descriptions of available datasets, as well as data collectionefforts. Short papers can also be position papers regardingpotential research challenges.We very much appreciate the support of the workshop co-ordinator, Tianqing Zhu as well as this year’s conference chairFahiem Bacchus and program chair Carles Sierras.We sincerely hope that the participants enjoy this year’sworkshop program and that this collection of papers will in-spire and encourage more AI-related research for and withinhealthcare in the future.Zina Ibrahim, Honghan Wu,Kerstin Bach, Richard Dobson,Spiros Denaxas, Nirmalie Wiratunga,Stewart Massie, and Sadiq SaniLondon/Trondheim/Aberdeen, July 20174

Preface: The 2nd International Workshop on Knowledge Discovery in Healthcare Data (KDH)

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Preface:The 2nd International Workshop on Knowledge Discovery in Healthcare Data (KDH)

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