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


Programme (draft)
Wednesday, April 18th 2018
08:30-09:00 Registration
09:00-11:00 PhD forum
11:00-12:00 Career talk
12:00-13:00 Lunch
13:00-14:00 Keynote: ICD-10 coding of clinical free-text narrative
Prof Pierre Zweigenbaum, LIMSI-CNRS
14:00-15:00 Research paper presentations
15:00-15:30 Coffee break (with posters)
15:30-17:00 Panel: Text mining veterinary clinical records – opportunities for veterinary and human healthcare researchers
17:00-18:30 HealTAC-2018 Industry Forum
18:30-20:30 Dinner


Thursday, April 19th 2018
09:00-10:30 Research paper presentations
10:30-12:00 Posters and Demos (with coffee)
12:00-13:00 Lunch
13:00-14:00 Keynote: Prof Wendy W. Chapman, University of Utah
14:00-14:30 Open discussions
14:30-15:00 Coffee break
15:00-16:30 Panel: Understanding how to gain public trust in healthcare text analytics
16:30-17:00 Close


Keynote speakers

Prof Wendy W. Chapman, University of Utah

Dr. Chapman earned her Bachelor’s degree in Linguistics and her PhD in Medical Informatics from the University of Utah in 2000. From 2000-2010 she was a National Library of Medicine postdoctoral fellow and then a faculty member at the University of Pittsburgh. She joined the Division of Biomedical Informatics at the University of California, San Diego in 2010. In 2013, Dr. Chapman became the chair of the University of Utah, Department of Biomedical Informatics. Dr. Chapman’s research focuses on developing and disseminating resources for modeling and understanding information described in narrative clinical reports. She is interested not only in better algorithms for extracting information out of clinical text through natural language processing (NLP) but also in generating resources for improving the NLP development process (such as shareable annotations and open source toolkits) and in developing user applications to help non-NLP experts apply NLP in informatics-based tasks like clinical research and decision support.She has been a principal investigator on several NIH grants from the National Library of Medicine, National Institute for Dental and Craniofacial Research, and National Institute for General Medical Sciences. In addition, she has collaborated on multi-center grants including the ONC SHARP Secondary Use of Clinical Data and the iDASH National Center for Biomedical Computing. Dr. Chapman is a PI and a co-investigator on a number of VA Health Services Research and Development (HSRD) grant proposals extending the development and application of NLP within the VA.A tenured Professor at the University of Utah, Dr. Chapman continues her research in addition to leading the Department of Biomedical Informatics. Dr. Chapman is an elected fellow of the American College of Medical Informatics and currently serves as Treasurer, was the previous chair of the American Medical Informatics Association (AMIA) Natural Language Processing Working Group, and is the chair of the AMIA Student Paper Awards Committee.

Prof Pierre Zweigenbaum, LIMSI-CNRS

Dr. Zweigenbaum has pioneered the use of natural language processing in biomedicine, particularly in the field of biomedical information extraction and automatic linguistic knowledge extraction from free text in multiple languages. Dr. Zweigenbaum’s research focus is Natural Language Processing (NLP), with medicine as a main application domain – what is now known as “BioNLP”, of which he is one of the pioneers and a recognized authority.

Through his work on information extraction in multilingual settings, he has developed innovative methods (and companion tools) to detect various types of medical entities, expand abbreviations, resolve co-references, and detect relations. He has also designed novel methods for the automatic acquisition of linguistic knowledge from corpora and thesauri, as well as methods to help extend lexicons and terminologies, including in multiple languages.


Accepted papers (long)

  • Text Mining Brain Imaging Reports
    Claire Grover, Richard Tobin, Beatrice Alex, Catherine Sudlow, Grant Mair and William Whiteley
  • Closing in on open–ended patient questionnaires with text mining
    Irena Spasic, David Owen, Andrew Smith and Kate Button
  • Natural language processing of primary care records for patients with myocardial infarction: a pilot study
    Anoop D. Shah, Emily Garrett, Tim Williams, Spiros Denaxas and Harry Hemingway
  • Can a Semantic Deep Learning approach gain insights for One Health? A case study on heart failure
    M. Arguello Casteleiro, R. Stevens, D. Maseda-Fernandez, M.J. Fernandez-Prieto, J.J. Des-Diz, C. Wroe, G. Demetriou, D.A. Singleton, E. Arsevska, P.J. Noble, P.H. Jones, J. Dukes-Mcewan, A.D. Radford, J. Keane and G. Nenadic


Accepted papers (short presentations)

  • Identifying misspelt names of drugs in medical records written in Portuguese
    Hegler Tissot
  • Using natural language processing to extract clinical data from epilepsy clinic letters
    Arron Lacey, Beata Fonferko-Shadrach, Ashley Akbari, Simon Thompson, David Ford, Ronan Lyons, Mark Rees and Owen Pickrell
  • Text mining of stroke outcomes from the electronic health records in an integrative stroke unit
    Josiah Poon, Yiu-Ming Ng, Kong-Ming Leung, Simon Poon, Justin Wu, Vincent Mok and Alexander Lau
  • Large scale healthcare text analytics with IBM Watson: a Pilot Study
    Alicja Piotrkowicz, Owen Johnson and Geoff Hall
  • Applying the Transtheoretical Model of Behavioral Change to Reddit Data: A Pilot Study of Cessation Strategies and Outcomes among Tobacco Users
    Danielle Mowery, Albert Park and Mike Conway



  • HealTAC-2018 Industry Forum
    (organisers: Angus Roberts, Clare Grover and David Milward)
  • Understanding how to gain public trust in healthcare text analytics
    (organisers: Elizabeth Ford, Lamiece Hassan, Malcolm Oswald and Jessica Stockdale)
  • Text mining veterinary clinical records – opportunities for veterinary and human healthcare researchers
    (organisers: Rachel Dean, PJ Noble, Noel Kennedy, Fabio Rinaldi, Mercedes Arguello Casteleiro)



  • Sharing NLP and Compute Services: Cloud Based Natural Language Processing at the NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust
    Angus Roberts, Matthew Broadbent, Anna Kolliakou, Jyoti Sanyal, Ian Roberts and Robert Stewart
  • UNSILO Classify for Medical Literature search
    Michael Upshall
  • Introducing the LION literature-based discovery system for cancer biology
    Simon Baker



  • KneeQApp: Supporting self-management of knee conditions with question answering
    David Owen, Kate Button and Irena Spasic
  • Large-scale Text Mining of Veterinary Narrative in SAVSNET for Research and Surveillance
    A.D. Radford, P.J. Noble, P.H. Jones, F. Sánchez-Vizcaíno, B. Brant, S. Smyth, E. Arsevska, J. Newman, D.A. Singleton, Mercedes Arguello Casteleiro and G. Nenadic
  • Linguistics Derived Rule-Based Methods in Chinese Biomedical and Clinical Text Mining
    Josiah Poon, Kun-Jun Lee, Nick Enfield, Zevari Hung, Wai-Ling Lin, Wendy Wong, Yan-Li Ju, Simon Poon, Justin Wu and Alexander Lau 
  • Identification of Time Expressions in Mental Health Records
    Natalia Viani, André Bittar, Joyce Kam, Ayunni Alawi, Lucia Yin, Rina Dutta, Robert Stewart and Sumithra Velupillai
  • Using the Big Picture – A Deep and Distant Approach to Classification of Suicidal Ideation in Autism Spectrum Disorder
    Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Rina Dutta and Angus Roberts
  • Early prediction of dementia with Lewy bodies with natural language processing of electronic health records
    Julia Ive, Sumithra Velupillai and Robert Stewart
  • Temporal Distribution of Suicidality Mentions in Electronic Health Records
    André Bittar, Sumithra Velupillai, Johnny Downs, Catherine Polling, Natalia Viani, Robert Stewart and Rina Dutta
  • Dementia-related information extraction from electronic patient records of a secondary care mental health NHS trust using the UK-CRIS platform
    Kim Chi-Hun, Nevado-Holgado Alejo and Goran Nenadic
  • Mining free-text patient feedback comments
    Azad Dehghan, Humayun Kayesh, Gavin Daker-White, Caroline Sanders and Goran Nenadic
  • Exploring research domains for author disambiguation from MEDLINE to and back
    Dina Vishnyakova, Raul Rodriguez-Esteban and Fabio Rinaldi
  • Improving the Identification of Diagnostic Test Accuracy Evidence using Lexical Statistics
    Amal Alharbi and Mark Stevenson
  • Machine Learning and NLP in EMRs: Identify CHF from Free Text Clinical Notes
    Margot Yann, Therese Stukel, Karen Tu and Liisa Jaakkimainen. 
  • Mining Free-Text Medical Notes for Suicide Risk Assessment
    Marios Adamou, Grigoris Antoniou, Elissavet Greasidou, Vincenzo Lagani, Paulos Charonyktakis and Ioannis Tsamardinos
  • Extracting adverse drug reactions and their context using sequence labelling ensembles
    Maksim Belousov, Nikola Milosevic, William Dixon and Goran Nenadic


PhD forum

[see also details here.]

  • Understanding underdiagnosis: The potential contribution of free-text to dementia research
    Shanu Sadhwani
  • Knowledge capture for a novel text analytics tool: analysis of Public Comments on Patient Information Leaflets to Measure Quality Perception and Patient Understanding
    Fernando Santos Sanchez
  • Integrating Structured and Unstructured Sources for Temporal Representation of Patients’ Histories
    Ghada Alfattni
  • A deep learning approach to sentiment analysis of patient narratives
    Anastasia Zunic
  • Extraction of benefits and harms of medication from social media
    Maksim Belousov