Health Informatics Knowledge Management Conference 2024

Data-Driven Clinical Decision-Making

The role of informatics, data analytics and artificial intelligence supporting evidence-based decision-making in health for improved patient outcomes.

The recent astonishing advances in AI have raised serious concerns how Health Care, Health Information, Knowledge Management and our society in general will be affected. At our Health Informatics Knowledge Management Conference we will explore, review and assess these changes and their effects on care decisions he sustainability of healthcare and society.

Please attend the Conference free by Zoom: (no password required)

Day One: Thursday, 1 February 2024

11:1013:008:005:301:00Delicate Decisions at the Intersection of Intensive Care and Machine Learning – How Human Information Needs inform the Development of Decision Support (Full Paper – 20m+5m)
Tamara Orth, Aloha Ambe, David Lovell & Dimitri Perrin. Australia
11:35From wearable Activity Trackers to Interstitial Glucose: Data to Insight – A proposed Scientific Journey (Short Paper – 10m+5m)
Haider Ali, Samaneh Madanain, David White, Malik Naveed Akhter & Imran Khan Niazi. New Zealand
12:00EEG Seizure Detection via Wavelet Variance (Short paper – 10m+5m)
Paul Grant. Australia
12:40Australia Classification of COVID-19 Severity from Cough Audio Signals (Poster – 5m)
Asmaa Shati, Amitava Datta and Ghulam Mubashar Hassan. Australia
12:45What Physical Examination and Artefacts are conducted during In-Person GP Consultation? Implications for next-generation Design of Virtual Care (Poster – 5m)
Moomna Waheed, Annie Lau &Hao Xiong Australia.
13:00Emotion Variation Detection in Discrete English Speech: A Wavelet Transform Use Case in Mental Health Monitoring (Short paper – 10m+5m)
Adebanji Adeleye, Samaneh Madanian &Olayinka Adeleye. New Zealand
13:15Detecting Brain Activity in ADHD Children and Healthy Controls using Machine Learning Techniques (Full Paper – 20m+5m)
Priyadarshini Natarajan & Samaneh Madanian New Zealand
13:40Fusion of Graph and Natural Language Processing in Predictive Analytics for Adverse Drug Reactions (Short paper – 10m+5m)
Fangyu Zhou & Shahadat Uddin Australia.
14:00Leveraging Natural Language Processing with genomeNLP: A step-by-step Guide to decipher the Grammar of Genetic Code (Poster – 5m)
Tyrone Chen, Navya Tyagi & Sonika Tyagi. Australia. 
14:05Designing and Adopting a Video-based LINE Chatbot System for Endoscopy Explanation in a Real-world Hospital: A Mixed Method Approach (Full Paper – 20m+5m)
Yohei Morita & Zoie Sy Wong Japan
14:30EHRs Beyond Silos: AI will transform Manual Therapy Outcomes (Poster – 5m)
Wael Mahmoud. Australia
14:35………………………… (Poster – 5m)
Farnaz Farid, Abubakar Bello, Fariza Sabrina, Shaleeza Sohail, Fahima Hossain & Farhad Ahamed
14:40Efficiency Model Selection in AutoML for CVD Risk Prediction (Poster – 5m)
Hongkuan Wang, Raymond Wong & Kwok Leung Ong
14:45AI Technologies in Reducing Hospital Readmission for Chronic Diseases: A Recommended Framework (Poster – 5m)
15:00The Prof is IN!
Ask any question, share any news, suggest any ideas or make any comments!
EHR-QC – A streamlined pipeline for automated electronic health records standardisation and
preprocessing to predict clinical outcomes

The integration of electronic health records (EHRs) has opened new avenues for leveraging
historical data in predicting clinical outcomes and enhancing patient care. Nonetheless, the
existence of non-standardized data formats and anomalies poses significant hurdles in
utilising EHRs for digital health research. Additionally, to develop robust and reproducible
predictive models, one needs to use data from multiple healthcare sites to account for
population-wide variations in their modelling approaches. However, institution-specific data
formats and inherent heterogeneity of EHR data hinder seamless data harmonisation. To
tackle these issues head-on, we introduce EHR-QC, a comprehensive tool comprising two
core modules: the Data Standardization Module and the Pre-processing Module.
Associate Professor Sonika Tyagi
Dr. Sonika Tyagi is an Associate Professor of Digital Health and Bioinformatics at the
School of Computational Technologies, RMIT University Australia. She is also an
affiliate Machine Learning lead scientist at Central Clinical School Monash University
Mr Yashpal Ramakrishnaiah
Yashpal Ramakrishnaiah is currently pursuing his PhD at Monash University, where
he is working on healthcare and data science.
18:3013:00Symptoms-Disease Detecting Conversation Agent using Knowledge Graphs (Full Paper – 20m+5m)
Ila Ananta, Sonia Khetarpaul & Dolly Sharma India (1.00pm IST)
18:55Social Media and Dialogues: Unpacking The Role Of Social Networking Sites In Individualized Care (Full Paper – 20m+5m)
Chukwuma Ukoha & Andrew Stranieri.
19:20Close of Day One
. . .

Day Two: Friday, 2 February 2024

11:0013:008:005:30Opening of Day Two
11:0513:058:055:35Utilizing Topological Clustering on a Traumatic Brain Injury Cohort: The Association of Neighborhood Socioeconomic Deprivation Profiles with Injury Mortality (Full Paper – 20m+5m)
Nelofar Kureshi, David B. Clarke & Syed Sibte Raza Abidi. Canada.
11:30The Virtual Caregiver: Enhancing Elderly Healthcare Through AI-Powered Voice-Based Conversational Systems (Poster – 5m)
John Minicz and Sita Venkatraman. Australia.
11:35USA Enhancing Medical History Collection using LLMs (Short paper – 10m+5m)
Rohit Kumar, R.K. Gattani & Kavita Singh.
12:00Lee MartinKEYNOTE: Lessons learnt using Robots in an Aged-Care Environment

Lee Martin – CEO, Tanunda Lutheran Aged Care, Barossa Valley, Australia
13:00Simultaneous Auscultation and ECG Recording to automate R-Peak Annotation (Poster – 5m)
Jeevan Jangam, Sanjeev Hiremath & Vanitha Math Australia
13:05Wechat Use in Health Interventions: a sub-study of Systematic Review of Effects on Health Outcomes in Managing Risk Factors for Secondary Prevention of Cardiovascular Disease (Short paper – 10m+5m)
Lu Yang, Chris Lynch & Caroline de Moel-Mandel. Australia
13:20A Case-Based Approach for Unravelling the Complex Decision Psychology for Adoption Decisions in Enterprise Health Information Systems (Full Paper – 20m+5m)
Yi Lin Jiang, Kevin Kuan & Simon Poon. Australia
13:45A Business, Technical and Clinical Canvas Design of Emerging Technology for Brain Injury Cognitive Assessments and Rehabilitation (Poster – 5m)
Andrew Stranieri, Darren Walker, Giles Oatley, Tanveer Choudhury, Herbert Jelinek & Md Rafiqul Islam. Australia
14:00Clinical Translation of a Heparin-Induced Thrombocytopenia predictor for Intensive Care Patients (Full Paper – 20m+5m)
Prabodi Senevirathna, Douglas Pires & Daniel Capurro Australia
14:25Analysing Patient Care Events using Sequential Pattern Mining (Poster – 5m)
Tony Hoang, Georg Grossmann, Jan Stanek & Markus Stumptner. Australia
14:30Adaptive Semantic Framework for CDSS to a new Environment (Short paper – 10m+5m)
Gourav Gupta, Jan Stanek, Wolfgang Mayer & Georg Grossman Australia
14:45Space War: Investigating the Impact of a Loss Aversion Strategy and Personality Traits in a VR Rowing Exergame (Full Paper – 20m+5m)
Zixuan Wang & Burkhard Wuensche New Zealand
15:05The Prof is IN!
Ask any question, share any news, suggest any ideas or make any comments!
Decoding the Grammar of DNA using Natural Language Processing

DNA is the blueprint defining all living organisms. Therefore, understanding the nature and
function of DNA is at the core of all biological studies. Rapid advances in DNA sequencing
and computing technologies over the past few decades resulted in large quantities of DNA
generated for diverse experiments, exceeding the growth of all major social media platforms
and astronomy data combined [1]. However, biological data is both complex and
high-dimensional, and is difficult to analyse with conventional methods.
Machine learning is naturally well suited to problems with a large volume of data and
complexity [2]. In particular, applying Natural Language Processing to the genome is
intuitive, since DNA is a natural language. Unique challenges exist in Genome-NLP over
natural languages, including the difficulty of word segmentation or corpus comparison.
To tackle these challenges, we developed the first automated and open-source genomeNLP
workflow that enables efficient and accurate knowledge extraction on biological data [1],
automating and abstracting preprocessing steps unique to biology. This lowers the barrier to
perform knowledge extraction by both machine learning practitioners and computational
biologists. In this tutorial, we will demonstrate how our workflow can be used to address the
above challenges, with implications in fields such as personalised medicine [3-4].

Presented by Tyron Chen. Australia. Tyrone Chen is a PhD student in computational biology at Monash University, Australia. Originally Tyrone was a lab biologist, but got interested in machine learning and its
applications in the biological sciences, publishing multiple papers and open source software
on the subject [1-4].
18:30Exploring the Clinical Value of the International Patient Summary – a Systematic Review (Full Paper – 20m+5m)
Djowin Schippers & Robert Stegwee. The Netherlands
18:55Research Partners with Lived Experience: Stories from Patients and Survivors (Poster – 5m)
Andrew Stranieri, Grant Meredith & Sally Firmin. Australia
19:00Conference Close

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Since 2007 HIKM is the leading high-impact conference for Health Information Science researchers across Asia-Pacific and beyond. It is held annually at the same time as the Australasian Computer Science Week and accepted papers are published by the Association for Computing Machinery (ACM).

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