The programme features keynote talks, research papers, discussion panels, software demos and poster sessions. The PhD and fellowship forum will feature talks from early carer researchers presenting their ongoing research.

    Programme Outline

    Wednesday, April 24th 2019
    08:30-09:00 Registration
    09:00-10:00 PhD forum
    10:00-10:30 Coffee break
    10:30-12:00 Research paper presentations
    12:00-13:00 Lunch
    13:00-14:00 Keynote: Prof. Stephane M. Meystre, Medical University of South Carolina
    Clinical Trials and Patients Automated Matchmaking
    14:00-15:00 Posters and demos (with coffee break)
    15:00-16:30 Panel: Ethics and governance in text-mining for trustworthy health research: progress and opportunities
    16:30-18:00 Industry forum
    18:30-20:30 Drinks reception and dinner

     

    Thursday, April 25th 2018
    09:00-09:45 PhD forum
    09:45-10:00 Coffee break
    10:00-11:00 Presentation of Healtex feasibility studies
    11:00-12:00 Panel: Welsh language in healthcare
    12:00-13:00 Lunch
    13:00-14:00 Keynote: Prof Hongfang Liu, Mayo Clinic
    Digital Health Sciences - towards the care of tomorrow
    14:00-15:00 Posters and demos (with coffee) – session 2
    15:00-16:15 Panel: Natural language processing in mental health: progress, challenges and opportunities
    16:15-17:00 Open discussions and close

     

    Keynote speakers

    Prof Hongfang Liu, Mayo Clinic

    Hongfang Liu, Ph.D., is a professor of biomedical informatics in the Mayo Clinic College of Medicine, and is a consultant in the Department of Health Sciences Research at Mayo Clinic. As a researcher, she is leading Mayo Clinic’s clinical natural language processing (NLP) program with the mission of providing support to access clinical information stored in unstructured text for research and practice. Administratively, Dr. Liu serves as the section head for Medical Informatics in the Division of Biomedical Statistics and Informatics.

    Dr Liu’s primary research interest is in biomedical NLP and data normalization. She has been developing a suite of open-source NLP systems for accessing clinical information, such as medications or findings from clinical notes. Additionally, she has been conducting collaborative research in the past decade in utilizing existing knowledge bases for high-throughput omics profiling data analysis and functional interpretation. Dr. Liu’s work in informatics has resulted in informatics systems that unlock clinical information stored in clinical narratives. Her work accelerates the pace of knowledge discovery, implementation and delivery for improved health care. She leads the American Medical Informatics Association Natural Language Processing (NLP) Working Group and is a member of the Informatics Domain Task Force, National CTSA Consortium.

    Prof. Stephane M. Meystre, Medical University of South Carolina

    Dr. Meystre is a SmartState Endowed Chair and Founding Director of the Translational Biomedical Informatics Center at MUSC. Dr. Meystre has a medical training and background, with graduate education and experience in biomedical informatics and Natural Language Processing (NLP). He has developed and evaluated NLP systems for clinical practice and for research, and led several projects applying NLP to clinical text for automatic text de-identification, or clinical information extraction. He is the Founder and CEO of Clinacuity, Inc.

    Dr. Stephane M. Meystre earned his PhD in Medical Informatics from the University of Utah, his MD from the University of Lausanne, Switzerland, and his MS in Medical Informatics from the University of California, Davis. He is a Research Assistant Professor in the University of Utah’s Department of Biomedical Informatics.

    His expertise in clinical informatics research involves the following areas: easing access to clinical data for clinical care and research purposes using advanced techniques such as Natural Language Processing (NLP) for information extraction and automated de-identification; providing research support by integrating clinical with research data; and integrating research with clinical systems. He also specializes in ontologies development automation, knowledge representation, and clinical text disambiguation. Other areas of interest include: biomedical information and knowledge modeling and representation; telemedicine, teleconsultation, and remote monitoring.

     

    Accepted papers

    • Philip John Gorinski, Honghan Wu, Claire Grover, Richard Tobin, Conn Talbot, Heather Whalley, Cathie Sudlow, William Whiteley and Beatrice Alex. Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
    • Amal Alharbi and Mark Stevenson. Using Query Adaptation to Improve the Identification of Relevant Studies for Systematic Reviews
    • Noa Cruz, Sergio Collazo, Ana López-Ballesteros and Ignacio Hernández-Medrano. Annotation of Atherosclerotic/Cardiovascular Clinical Entities from Electronic Health Records
    • Antoine Pironet, Joris Mattheijssens, Kris Henau, Nancy Van Damme, Harlinde de Schutter and Liesbet Van Eycken. Automatic extraction of breast receptor status from bilingual free-text cancer pathology reports

     

    Feasibility studies

    • Anja Belz, Richard Hoile, Azam Mullick, Elizabeth Ford, Jackie Cassell, Harm van Marwijk and David Weir. Conceptualising and Quantifying the Social Media Signal Relating to Non-adherence in the Treatment of Depression: Dataset and Annotation Scheme
    • Elizabeth Ford, Lamiece Hassan, Malcolm Oswald. Citizens’ Jury: What access should researchers have to free-text data in health records?
    • J. Ive, S. Velupillai, N. Viani, A. Roberts, R. Stewart, S. Puntis, W.O. Pickrell, R.N. Cardinal. Towards Shareable Data in Clinical Natural Language Processing: Generating Synthetic Electronic Health Records
    • W. Dixon, G. Nenadic, A. Bulcock, M. Evans, A. Anand, M. Belousov, G. Demetriou. Feasibility of text-mining to support nudging of real-time side effect reporting to drug regulators within the online health social network ‘HealthUnlocked’

     

    Panels

    • Elizabeth Ford, Kerina Jones, Lamiece Hassan, Anoop Shah and Nathan Lea. Ethics and Governance in Text-mining for Trustworthy Health Research – Progress and Opportunities
    • Yunfei Long, Elvira Perez Vallejos, Mat Rawsthorne, Angus Roberts and Harish Tayyar Madabushi. Natural Language Processing (NLP) in Mental Health: progress, challenges, and opportunities
    • Gareth Morlais, Dawn Knight, Steve Morris, Paul Rayson, Irena Spasić. Welsh language in health
    • Industry forum

     

    Posters

    • Jaya Chaturvedi, Natalia Viani, Sumithra Velupillai and Angus Roberts. Analysis and Annotation of temporal information related to medications in EHRs
    • Lamiece Hassan, Mahmoud Elhawati, Mary Tully, James Cunningham and Goran Nenadic. #Datasaveslives: a mixed methods analysis of a Twitter-based social media campaign to promote the benefits of using health data for research purposes
    • Lama Alqurashi, Angus Roberts and Rina Dutta. Using corpus linguistics to explore gender differences in EHR text
    • Irena Spasic, Padraig Corcoran, Dominik Krzeminski and Alexander Balinsky. Supervised text classification for cohort selection in clinical trials
    • Mercedes Arguello-Casteleiro, Celal Cankaya, David Singleton, P.J. Noble, A.D. Radford and Goran Nenadic. Extracting medications from veterinary clinical text: a case study within SAVSNET

    • Polona Štefanič, Padraig Corcoran and Irena Spasić. The role of morphological structure in acronym recognition
    • André Bittar, Sumithra Velupillai, Angus Roberts and Rina Dutta. Testing Sentiment Lexicons for Suicide Risk Assessment
    • Samuel Dobbie, Arron Lacey and Owen Pickrell. Phrase approximation to enhance UMLS code mapping in clinic letters
    • Erik Tjong Kim Sang, Ben de Vries, Wouter Smink, Bernard Veldkamp, Gerben Westerhof and Anneke Sools. De-identification of Dutch Medical Text
    • Mercedes Arguello Casteleiro, P.J. Noble, A.D. Radford and Goran Nenadic. Clinical Text De-identification in SAVSNET
    • Daphné Chopard, Matthias Treder and Irena Spasić. Text Self-normalisation: Automatic Abbreviation Expansion

    • Maneesh Kumar, Preeti Zade, Suvarna Vasanthakumar and Ying Liu. Impact of Online Brokerage Firm on improvement in Healthcare Service Delivery
    • Alexia Sampri, Nophar Geifman, Philip Couch and Niels Peek. Challenges in the aggregation of biomedical datasets and probabilistic approaches to overcome representational heterogeneity
    • Daphné Chopard, Matthias Treder and Irena Spasić. Text Self-Normalization: An Adaptive and Self-sufficient Approach to Abbreviation Expansion
    • Aurelie Mascio, Rashmi Patel, Robert Stewart, Richard Dobson, Angus Roberts. Attention dysfunctions in Schizophrenia: extracting symptoms from Electronic Health Records using Natural Language Processing
    • Ghada Alfattni, Niels Peek, Goran Nenadic. Temporal Expression Extraction from Clinical Narrative: A Comparative Analysis of Different Tools

     

    Demos

    • Averbis Text analytics
    • DeepCognito
    • Babylon Health. Facet Explorer for Medical Domain Properties

     

    Fellowship forum

    • Natalia Viani. Exploring alternative approaches to address temporal information extraction for clinical use-cases

     

    PhD forum

    [see also details here.]

    • Mark Ormerod. Assessing the Interpretability of Sentence-Level Clinical Notes Diagnosis Models
    • Glorianna Jagfeld. Talking about personal recovery in bipolar disorder
    • Denis Newman-Griffis. Kickstarting NLP for whole-person function information with representation learning and data analysis
    • Julia Walsh. Using spontaneously generated online patient experiences to improve healthcare for patients and providers
    • Daphné Chopard. Text Self-Normalization: An Adaptive and Self-sufficient Approach to Abbreviation Expansion