ORCID Profile
0000-0002-4711-7543
Current Organisation
Deakin University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Health Information Systems (incl. Surveillance) | Medical Devices | Software Engineering | Computer Software
ANZSRC Socio-Economic Objective (SEO)
Behaviour and Health | Integrated Circuits and Devices | Application Software Packages (excl. Computer Games) |
Related Links
Publications
Forecasting Patient Outflow from Wards having No Real-Time Clinical Data
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/ICHI.2016.26
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
Publisher: BMJ
Date: 03-2014
An integrated framework for suicide risk prediction
Publisher: ACM
Date: 11-08-2013
Poster: Vulnerability discovery with function representation learning from unlabeled projects
Publisher: ACM
Date: 30-10-2017
iPoll: Automatic Polling Using Online Search
Publisher: Springer International Publishing
Date: 2014
Modelling Match Outcome in Australian Football: Improved accuracy with large databases
Publisher: Walter de Gruyter GmbH
Date: 07-2019
Abstract: Mathematical models that explain match outcome, based on the value of technical performance indicators (PIs), can be used to identify the most important aspects of technical performance in team field-sports. The purpose of this study was to evaluate several methodological opportunities, to enhance the accuracy of this type of modelling. Specifically, we evaluated the potential benefits of 1) modelling match outcome using an increased number of seasons and PIs compared with previous reports, 2) how to identify eras where technical performance characteristics were stable and 3) the application of a novel feature selection method. Ninety-one PIs across sixteen Australian Football (AF) League seasons were analysed. Change-point and Segmented Regression analyses were used to identify eras and they produced similar but non-identical outcomes. A feature selection ensemble method identified the most valuable 45 PIs for modelling. The use of a larger number of seasons for model development lead to improvement in the classification accuracy of the models, compared with previous studies (88.8 vs 78.9%). This study demonstrates the potential benefits of large databases when creating models of match outcome and the pitfalls of determining whether there are eras in a longitudinal database.
A framework for feature extraction from hospital medical data with applications in risk prediction
Publisher: Springer Science and Business Media LLC
Date: 12-2014
Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data
Publisher: JMIR Publications Inc.
Date: 21-07-2016
A Survey of Android Malware Detection with Deep Neural Models
Publisher: Association for Computing Machinery (ACM)
Date: 06-12-2020
DOI: 10.1145/3417978
Abstract: Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber security research. Deep learning models have many advantages over traditional Machine Learning (ML) models, particularly when there is a large amount of data available. Android malware detection or classification qualifies as a big data problem because of the fast booming number of Android malware, the obfuscation of Android malware, and the potential protection of huge values of data assets stored on the Android devices. It seems a natural choice to apply DL on Android malware detection. However, there exist challenges for researchers and practitioners, such as choice of DL architecture, feature extraction and processing, performance evaluation, and even gathering adequate data of high quality. In this survey, we aim to address the challenges by systematically reviewing the latest progress in DL-based Android malware detection and classification. We organize the literature according to the DL architecture, including FCN, CNN, RNN, DBN, AE, and hybrid models. The goal is to reveal the research frontier, with the focus on representing code semantics for Android malware detection. We also discuss the challenges in this emerging field and provide our view of future research opportunities and directions.
Federated Learning Under Statistical Heterogeneity on Riemannian Manifolds
Publisher: Springer Nature Switzerland
Date: 2023
Traffic Identification in Big Internet Data
Publisher: Springer International Publishing
Date: 2016
Impute Gene Expression Missing Values via Biological Networks: Optimal Fusion of Data and Knowledge
Publisher: IEEE
Date: 18-07-2021
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
Publisher: JMIR Publications Inc.
Date: 16-12-2016
DOI: 10.2196/JMIR.5870
Semantic multi-modal reprojection for robust visual question answering
Publisher: IEEE
Date: 30-11-2022
Exceptional Contrast Set Mining: Moving Beyond the Deluge of the Obvious
Publisher: Springer International Publishing
Date: 2016
Sqn2Vec: Learning sequence representation via sequential patterns with a gap constraint
Publisher: Springer International Publishing
Date: 2019
Unsupervised DRG upcoding detection in healthcare databases
Publisher: IEEE
Date: 12-2010
A review on automatic facial expression recognition systems assisted by multimodal sensor data
Publisher: MDPI AG
Date: 18-04-2019
DOI: 10.3390/S19081863
Abstract: Facial Expression Recognition (FER) can be widely applied to various research areas, such as mental diseases diagnosis and human social hysiological interaction detection. With the emerging advanced technologies in hardware and sensors, FER systems have been developed to support real-world application scenes, instead of laboratory environments. Although the laboratory-controlled FER systems achieve very high accuracy, around 97%, the technical transferring from the laboratory to real-world applications faces a great barrier of very low accuracy, approximately 50%. In this survey, we comprehensively discuss three significant challenges in the unconstrained real-world environments, such as illumination variation, head pose, and subject-dependence, which may not be resolved by only analysing images/videos in the FER system. We focus on those sensors that may provide extra information and help the FER systems to detect emotion in both static images and video sequences. We introduce three categories of sensors that may help improve the accuracy and reliability of an expression recognition system by tackling the challenges mentioned above in pure image/video processing. The first group is detailed-face sensors, which detect a small dynamic change of a face component, such as eye-trackers, which may help differentiate the background noise and the feature of faces. The second is non-visual sensors, such as audio, depth, and EEG sensors, which provide extra information in addition to visual dimension and improve the recognition reliability for ex le in illumination variation and position shift situation. The last is target-focused sensors, such as infrared thermal sensors, which can facilitate the FER systems to filter useless visual contents and may help resist illumination variation. Also, we discuss the methods of fusing different inputs obtained from multimodal sensors in an emotion system. We comparatively review the most prominent multimodal emotional expression recognition approaches and point out their advantages and limitations. We briefly introduce the benchmark data sets related to FER systems for each category of sensors and extend our survey to the open challenges and issues. Meanwhile, we design a framework of an expression recognition system, which uses multimodal sensor data (provided by the three categories of sensors) to provide complete information about emotions to assist the pure face image/video analysis. We theoretically analyse the feasibility and achievability of our new expression recognition system, especially for the use in the wild environment, and point out the future directions to design an efficient, emotional expression recognition system.
Mind change efficient learning
Publisher: Elsevier BV
Date: 06-2006
A multiple feature fusion framework for video emotion recognition in the wild
Publisher: Wiley
Date: 07-04-2020
DOI: 10.1002/CPE.5764
Abstract: Human emotions can be recognized from facial expressions captured in videos. It is a growing research area in which many have attempted to improve video emotion detection in both lab‐controlled and unconstrained environments. While existing methods show a decent recognition accuracy on lab‐controlled datasets, they deliver much lower accuracy in a real‐world uncontrolled environment, where a variety of challenges need to be addressed such as variations in illumination, head pose, and in idual appearance. Moreover, automatically identifying the key frames consisting of the expression from real‐world videos is another challenge. In this article, to overcome these challenges, we provide a video emotion recognition via multiple feature fusion method. First, a uniform local binary pattern (LBP) and the scale‐invariant feature transform features are extracted from each frame in the video sequences. By applying a random forest classifier, all of the static frames are then labelled by the related emotion class. In this way, the key frames can be automatically identified, including neutral and other expressions. Furthermore, from the key frames, a new geometric feature vector and the LBP from three orthogonal planes are extracted. To further improve robustness, audio features are extracted from the video sequences as an additional dimension to augmenting visual facial expression analysis. The audio and visual features are fused through a kernel multimodal sparse representation. Finally, the corresponding emotion labels to the video sequences can be assigned when a multimodal quality measure specifies the quality of each modality and its role in the decision. The results on both acted facial expressions in the Wild and MMI datasets demonstrate that the proposed method outperforms several counterpart video emotion recognition methods.
Stabilized sparse ordinal regression for medical risk stratification
Publisher: Springer Science and Business Media LLC
Date: 17-03-2014
A new hybrid method for Bayesian network learning with dependency constraints
Publisher: IEEE
Date: 03-2009
Backdoor Attack on Deep Neural Networks in Perception Domain
Publisher: IEEE
Date: 18-06-2023
Robust Traffic Classification with Mislabelled Training Samples
Publisher: IEEE
Date: 12-2015
Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition
Publisher: BMJ
Date: 04-2016
Parameter-free search of time-series discord
Publisher: Springer Science and Business Media LLC
Date: 03-2013
Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?
Publisher: Elsevier BV
Date: 06-2016
Keep Calm and Know Where to Focus: Measuring and Predicting the Impact of Android Malware
Publisher: Springer International Publishing
Date: 2018
Succinct contrast sets via false positive controlling with an application in clinical process redesign
Publisher: Elsevier BV
Date: 12-2020
Distributed Optimization of Graph Convolutional Network Using Subgraph Variance
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Publisher: Frontiers Media SA
Date: 27-09-2021
DOI: 10.3389/FNEUR.2021.670379
Abstract: Aim: To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques. Method: The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009–2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation. Three different stroke types [intracerebral hemorrhage (ICH), ischemic stroke, transient ischemic attack (TIA)] were considered and five different comparison outcome settings were considered. Our electronic administrative record based predictive model was compared with a predictive model composed of “baseline” clinical features, more specific for stroke, such as age, gender, smoking habits, co-morbidities (high cholesterol, hypertension, atrial fibrillation, and ischemic heart disease), types of imaging done (CT scan, MRI, etc.), and occurrence of in-hospital pneumonia. Risk factors associated with likelihood of negative outcomes were identified. Results: The data was highly reliable at predicting discharge to rehabilitation and all other outcomes vs. death for ICH (AUC 0.85 and 0.825, respectively), all discharge outcomes except home vs. rehabilitation for ischemic stroke, and discharge home vs. others and home vs. rehabilitation for TIA (AUC 0.948 and 0.873, respectively). Electronic health record data appeared to provide improved prediction of outcomes over stroke specific clinical factors from the machine learning models. Common risk factors associated with a negative impact on expected outcomes appeared clinically intuitive, and included older age groups, prior ventilatory support, urinary incontinence, need for imaging, and need for allied health input. Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
A Novel Video Emotion Recognition System in the Wild Using a Random Forest Classifier
Publisher: Springer Singapore
Date: 2020
Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset
Publisher: Public Library of Science (PLoS)
Date: 04-05-2015
Detecting contaminated birthdates using generalized additive models
Publisher: Springer Science and Business Media LLC
Date: 12-06-2014
Mind-change optimal learning of Bayes net structure from dependency and independency data
Publisher: Elsevier BV
Date: 2010
Robust federated learning under statistical heterogeneity via hessian-weighted aggregation
Publisher: Springer Science and Business Media LLC
Date: 03-01-2023
Cyber Code Intelligence for Android Malware Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Web search activity data accurately predict population chronic disease risk in the USA
Publisher: BMJ
Date: 24-03-2015
Abstract: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.
Bias-regularised Neural-Network Metamodelling of Insurance Portfolio Risk
Publisher: IEEE
Date: 07-2020
Understanding the relative contribution of technical and tactical performance to match outcome in Australian Football
Publisher: Informa UK Limited
Date: 07-02-2020
Fast Valuation of Large Portfolios of Variable Annuities via Transfer Learning
Publisher: Springer International Publishing
Date: 2019
Embracing the dropouts in single-cell RNA-seq dynamics modelling
Publisher: IEEE
Date: 18-06-2023
Gathering intelligence on student information behavior using data mining
Publisher: Project MUSE
Date: 2020
Unsupervised inference of significant locations from WiFi data for understanding human dynamics
Publisher: ACM
Date: 25-11-2014
A3CM: Automatic Capability Annotation for Android Malware
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Predicting the Impact of Android Malicious Samples via Machine Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Toxicity prediction in cancer using multiple instance learning in a multi-task framework
Publisher: Springer International Publishing
Date: 2016
Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets
Publisher: Springer International Publishing
Date: 2018
Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records
Publisher: JMIR Publications Inc.
Date: 11-07-2016
DOI: 10.2196/MENTAL.5475
Abstract: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.
Bio-Inspired Dual-Network Model to Tackle Statistical Heterogeneity in Federated Learning
Publisher: IEEE
Date: 18-06-2023
Robust Neural Regression via Uncertainty Learning
Publisher: IEEE
Date: 18-07-2021
Microwave Link Failures Prediction via LSTM-based Feature Fusion Network
Publisher: IEEE
Date: 18-07-2021
Understanding Toxicities and Complications of Cancer Treatment: A Data Mining Approach
Publisher: Springer International Publishing
Date: 2015
LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment
Publisher: Elsevier BV
Date: 12-2018
Machine Learning for Financial Risk Management: A Survey
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Protocol for economic evaluation alongside the SHINE (Supporting Healthy Image, Nutrition and Exercise) cluster randomised controlled trial
Publisher: BMJ
Date: 08-2020
DOI: 10.1136/BMJOPEN-2020-038050
Abstract: Limited evidence exists on the cost-effectiveness of interventions to prevent obesity and promote healthy body image in adolescents. The SHINE (Supporting Healthy Image, Nutrition and Exercise) study is a cluster randomised control trial (cRCT) aiming to deliver universal education about healthy nutrition and physical activity to adolescents, as well as targeted advice to young people with body image concerns who are at risk of developing disordered eating behaviours. This paper describes the methods for the economic evaluation of the SHINE cRCT, to determine whether the intervention is cost-effective as an obesity prevention measure. A public payer perspective will be adopted, with intervention costs collected prospectively. Within-trial cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) will quantify the incremental costs and health gains of the intervention as compared with usual practice (ie, teacher-delivered curriculum). CEA will present results as cost per body mass index unit saved. CUA will present results as cost per quality-adjusted life year gained. A modelled CUA will extend the target population, time horizon and decision context to provide valuable information to policymakers on the potential for incremental cost offsets attributable to disease prevention arising from intervention. Intervention costs and effects will be extrapolated to the population of Australian adolescents in Grade 7 of secondary school (approximate age 13 years) and modelled over the cohort’s lifetime. Modelled CUA results will be presented as health-adjusted life years saved and healthcare cost-savings of diseases averted. Incremental cost-effectiveness ratios will be calculated as the difference in costs between the intervention and comparator ided by the difference in benefit. Semi-structured interviews with key intervention stakeholders will explore the potential impact of scalability on cost-effectiveness. These data will be thematically analysed to inform sensitivity analysis of the base case economic evaluation, such that cost-effectiveness evidence is reflective of the potential for scalability. Ethics approval was obtained from the Deakin University Human Research Ethics Committee (#2017–269) and the Victorian Department of Education and Training (#2018_003630). Study findings will be disseminated through peer-reviewed academic papers and participating schools will receive annual reports over the 3 years of data collection. ACTRN 12618000330246 Pre-results.
Robust visual question answering via semantic cross modal augmentation
Publisher: Elsevier BV
Date: 2024
Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Understanding effective tactics in Australian football using network analysis
Publisher: Informa UK Limited
Date: 11-04-2019
Estimating the intensity of ward admission and its effect on emergency department access block
Publisher: Wiley
Date: 21-11-2012
DOI: 10.1002/SIM.5684
Abstract: Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks.
Attention-based Feature Fusion for Reconstructing Gene-Regulatory Interactions
Publisher: IEEE
Date: 13-10-2022
Robust federated learning under statistical heterogeneity via Hessian spectral decomposition
Publisher: Elsevier BV
Date: 09-2023
Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique
Publisher: Springer Science and Business Media LLC
Date: 13-01-2021
Deep Neighbor Embedding for Evaluation of Large Portfolios of Variable Annuities
Publisher: Springer International Publishing
Date: 2019
Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data
Publisher: CSIRO Publishing
Date: 2014
DOI: 10.1071/AH14059
Abstract: Objective Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations. Methods The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation. Results The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78 95% confidence interval (CI) 0.71–0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72 95% CI 0.66–0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission. Conclusions Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions. What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.
Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments
Publisher: Springer Science and Business Media LLC
Date: 14-03-2014
MODEL: Motif-Based Deep Feature Learning for Link Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Data-Driven Android Malware Intelligence: A Survey
Publisher: Springer International Publishing
Date: 2019
Con2Vec: Learning embedding representations for contrast sets
Publisher: Elsevier BV
Date: 10-2021
Cross-Project Transfer Representation Learning for Vulnerable Function Discovery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-0006
Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding
Publisher: Springer Science and Business Media LLC
Date: 11-04-2018
DOI: 10.1007/S10916-018-0951-4
Abstract: Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of in idual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.
The relationship between match performance indicators and outcome in Australian Football
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.JSAMS.2018.09.235
Abstract: To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models. Observational. Ninety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win-Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome. Decision tree models predicted Win-Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win-Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include Turnovers Forced score, Inside 50s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs. This work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches.
Related Organisations
Related Funding Activities
Preventing Obesity And Promoting Healthy Body Image In Australian Secondary Schools: A Web-based System Tailored To Individual Needs
Start Date: 2017
End Date: 2021
Funder: National Health and Medical Research Council
View Funded ActivityARC Research Hub For Digital Enhanced Living
Start Date: 2017
End Date: 2021
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH170100013
Start Date: 03-2018
End Date: 10-2023
Amount: $2,962,655.00
Funder: Australian Research Council
View Funded Activity