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.
- 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
- 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’
- 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
- 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
- Averbis Text analytics
- Babylon Health. Facet Explorer for Medical Domain Properties
- Natalia Viani. Exploring alternative approaches to address temporal information extraction for clinical use-cases
- 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
Wednesday, April 24th 2019
|10:30-12:00||Research paper presentations|
|13:00-14:00||Keynote: Prof. Stephane M. Meystre, Medical University of South Carolina
Clinical Trials and Patients Automated Matchmaking
Insufficient patient enrollment in clinical trials remains a serious and costly problem and is often considered the most critical issue to solve for the clinical research community. Most patients are never offered an opportunity to enroll in clinical trials, causing wasted time, resources and potential survival extension and quality of life improvements. The participation of physicians is essential for successful patient enrollment and lack of awareness of trials is often cited as a reason for low enrollment levels. One potential barrier to this awareness is the difficulty in correlating eligibility criteria with patient characteristics in a timely manner. Automated eligibility criteria extraction from narrative trial descriptions and extraction of matching clinical information from patient electronic health records (EHR) have been explored to address the aforementioned issues, but both approaches suffer from limited scope and generalizability. More importantly, no fully automated matching between trials and patients for trial eligibility have been attempted. In this talk, I will present our vision to improve patient enrollment in clinical trials and some foundational work to automatically extract eligibility criteria from EHR text notes and to match trials with eligible patients automatically.
|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|
|18:30-20:30||Drinks reception and dinner|
Thursday, April 25th 2018
|10:00-11:00||Presentation of Healtex feasibility studies|
|11:00-12:00||Panel: Welsh language in healthcare|
|13:00-14:00||Keynote: Prof Hongfang Liu, Mayo Clinic
Digital Health Sciences - towards the care of tomorrow
With the increase in digitalization and advancement of high throughput technologies, the life sciences, biomedicine, and healthcare are increasingly turning into data-intensive sciences. One of the greatest challenges today in today’s science is how to manage, integrate, and mine biomedical data today resulting from various sources in different structural dimensions, ranging from the microscopic world (e.g., -omics data) to the macroscopic world (e.g., population health). Systematic and comprehensive exploration of all these data provides a mechanism for data-driven hypothesis generation, optimized experiment planning, precision medicine, and evidence-based healthcare delivery. In this talk, I will discuss our efforts towards digital health sciences illustrated through the discovery, translation, and application (DTA) paradigm.
|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|
|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.