Artificial intelligence research in health: The business of health delivery is complex. Employing over 850,000 people and delivering services to 21.3 million residents. The Australian healthcare system is currently strained to the maximum in dealing with the increasing demand for services. And an acute shortage of skilled professionals. The National e-Health Strategy drives a nationwide research agenda to provide the infrastructure and tools required to support the planning, management, and delivery of healthcare services.
Deriving principles from the disciplines of computer science, mathematics, philosophy, and physiology. And consisting of different fields, from machine vision to expert systems, the field of Artificial Intelligence (AI) deals with the creation of “machines that can think.” Focused on traits of reasoning, knowledge representation, planning, learning, communication, perception, and social intelligence. AI has been widely applied to augment state of the art in Health Informatics.
Advances in Artificial Intelligence Research in Health
This special issue reports on the latest developments in the field of AI-motivated research in the health domain. The special issue arose from the inaugural Australian Workshop on Artificial Intelligence in Health (AIH 2011).
The AIH 2011 workshop was a first of its kind, a national initiative that aimed to bring together scholars. And practitioners in the field of AI-driven health informatics to present and discuss their research. Share their knowledge and experiences, define key research challenges and explore possible collaborations to advance e-Health development nationally and internationally. Therefore, AIH 2011 with AI2011 was both timely and mutually beneficial for the communities involved in these events. Artificial intelligence research in health
AIH 2011 received 16 full paper submissions, and three program committee members reviewed each paper.
The workshop brought together researchers from various disciplines across various parts of the country. And provided an excellent forum for discussion and exchange of ideas. In addition to the presentation of papers, the workshop featured two keynote addresses, a poster session during lunch, and a panel discussion.
Artificial Intelligence Research in Health
The first keynote address, “Using Artificial Intelligence to transform the management of Chronic Disease,” was delivered by Professor Michael Georgeff. In addition to presenting ongoing research efforts to apply AI techniques to manage chronic disease better, Professor Georgeff discussed the greatest innovations in healthcare from a medical practitioner’s viewpoint and focussed on how AI could be leveraged to transform healthcare.
The second keynote address, “Knowledge Acquisition Issues in Interpreting Laboratory Data,” was presented by Professor Paul Compton. Posed several questions related to large–scale knowledge acquisition for decision–support systems in medicine. Drawing lessons from experience in working with knowledge acquisition tools that support over 300 million diagnostics laboratory reports. Artificial intelligence research in health
The workshop concluded with a panel discussion in which Professor Abdul Sattar and Professor Yogi Kanagasingam joined the keynote speakers to address the topic “AI for eHealth: 2012 and Beyond”. The discussion that ensued was very energetic and actively engaged audience interaction. Topics discussed ranged from the emerging contribution of AI over the past five decades. Current issues with the marketing and uptake of AI-based solutions in mainstream healthcare. And the challenges for AI-based research and application in years to come. The panel and audience also acknowledged the key role a workshop would play in driving collaborative research efforts between AI and health informatics research communities.
Artificial Intelligence in Health
All accepted papers were also invited to revise and submit their manuscripts for inclusion in the Australasian Medical Journal (AMJ) special issue. As a result, seven papers and a letter to the editor have been accepted for publication in the journal.
The first paper, by Bevan Koopman, Peter Bruza, Laurianne Sitbon, and Michael Lawley. Titled “Towards Semantic Search and Inference in Electronic Medical Records.” Presents concept-based information retrieval for searching electronic medical records. And demonstrates that the approach outperforms keyword-based search, working especially well for queries where the latter performs poorly.
The second paper, by Kinzang Chhogyal, Abhaya Nayak, Rolf Schwitter, and Abdul Sattar, is titled “A Causal Model for Fluctuating Sugar Levels in Diabetes Patients.” Investigates the use of fixed distance-based belief revision to improve causal models. A simple scenario for fluctuating blood sugar levels in a diabetes patient demonstrates the efficacy of this approach.
Artificial Intelligence
The third paper, was by Alexander Krumpholz, David Hawking, Richard Jones, and Tom Gedeon. And Hugh Greville, titled “Automated Medical Literature Retrieval,” describes a system for retrieving relevant medical publications using queries generated automatically from data present in an electronic patient record. Integrated into an electronic record system, such a system would proactively support medical practitioners in delivering care.
The fourth paper is by Abeed Sarker, Diego Molla, and Cecile Paris. Titled “Extractive Summarization of Medical Documents.” Proposes a query-focused approach for automatically summarising medical documents helping medical practitioners find relevant information.
The fifth paper, by Diego Molla and Maria Elena Santiago– Martinez. Titled “A Corpus for Evidence-Based Summarization,” presents a corpus of clinical questions and answers designed for training and testing automated text summarises to support evidence-based medicine.
The sixth paper, by Di Xiao, Janardhan Vignarajan, Jane Lock, Shaun Frost, Mei-Ling Tay–Kearney, and Yogi Kanagasingam. Titled “Retinal Image Registration and Comparison for Clinical Decision Support,” proposes a set of accurate. And robust retinal image registration solutions for longitudinal retinal image alignment and comparison.
Advances in Artificial Intelligence Research
The seventh paper, by Amol Wagholikar, Maggie Fung, and Colleen Nelson, titled “Improving Self–Care of Patients with Chronic Disease using Online Personal Health Record,” employs a case-based reasoning approach to self-care for advanced prostate cancer patients in an online patient health record environment.
By Anthony Nguyen, Yue Kimi Sun, Laurianne Sitbon, and Shlomo Geva, titled “Representation Of Assertions In Clinical Free Text Using SNOMED CT,” a letter to the editor explores the use of the SNOMED CT terminology for representing medical concepts and their assertions in clinical free text.
We hope that the breadth and diversity of the papers presented at the workshop. And published in this special issue will foster further collaboration and AI-driven research in health.
This workshop would not have been possible without the contributions of numerous fine people. First, we are greatly indebted to Professor Aditya Ghose, Professor Anthony Maeder, Professor Wayne Wobcke, Professor Mehmet Orgun, and Dr. Yogesan (Yogi) Kanagasingam for their guidance and support. We would also like to thank the organizing committee of the 24th Australasian Joint Conference on AI. The Institute of Integrated and Intelligent Systems, Griffith University for supporting the workshop. And the CSIRO Australian e-Health Research Centre for their support and sponsorship of travel scholarships and the best paper prize.
Thanks are also due to Professor Moyez Jiwa and the AMJ for supporting the workshop and inviting accepted papers for inclusion in this special issue. Finally, we are indebted to the authors who responded to the invitation to submit their papers and the reviewers who generously donated their time and expertise. And provided very comprehensive reviews of the submitted papers.
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