ORCID Profile
0000-0002-7865-072X
Current Organisations
RMIT University
,
University of Melbourne
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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)
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing not elsewhere classified | Neural, Evolutionary and Fuzzy Computation | Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) |
ANZSRC Socio-Economic Objective (SEO)
Information Processing Services (incl. Data Entry and Capture) | Energy Transmission and Distribution (excl. Hydrogen) | Energy Storage (excl. Hydrogen) | Fixed Line Data Networks and Services | Mobile Data Networks and Services
Related Links
Publications
Identifying Singleton Spammers via Spammer Group Detection
Publisher: Springer International Publishing
Date: 2018
Efficient itinerary recommendation via personalized POI selection and pruning
Publisher: Springer Science and Business Media LLC
Date: 02-03-2022
DOI: 10.1007/S10115-021-01648-3
Abstract: Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithm EffiTourRec using an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.
A Location-Query-Browse Graph for Contextual Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Parking Availability Prediction with Long Short Term Memory Model
Publisher: Springer International Publishing
Date: 2019
Extending Information-Theoretic Validity Indices for Fuzzy Clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Incorporating LSTM Auto-Encoders in Optimizations to Solve Parking Officer Patrolling Problem
Publisher: Association for Computing Machinery (ACM)
Date: 21-04-2020
DOI: 10.1145/3380966
Abstract: The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces have provided big spatio-temporal data that be used to analyze parking situations in the city and help parking officers monitor parking violations. The traveling officer problem was customized to formulate a path-finding problem that aims to maximize the probability of catching overstayed cars before they leave. One of the challenges is to extract effective features from the big spatio-temporal data and provide a data-driven solution to replace conventional solutions such as a simple rule-based system or single optimization methods. In this article, we propose a seamless end-to-end learning and optimization framework that combines the long short-term memory auto-encoder neural network, clustering, and path-finding methods to solve the traveling officer problem. Our extensive comparison experiments on a large-scale real-world dataset have shown that our proposed solution outperforms any other single-step or optimization methods.
On compressing weighted time-evolving graphs
Publisher: ACM
Date: 29-10-2012
D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate
Publisher: Elsevier BV
Date: 06-2017
Virtual machine allocation policies against co-resident attacks in cloud computing
Publisher: IEEE
Date: 06-2014
Learning to Rank Items of Minimal Reviews Using Weak Supervision
Publisher: Springer International Publishing
Date: 2018
Clustering and visualization of fuzzy communities in social networks
Publisher: IEEE
Date: 07-2013
Modelling Zeros in Blockmodelling
Publisher: Springer International Publishing
Date: 2022
An Investigation into Prediction + Optimisation for the Knapsack Problem
Publisher: Springer International Publishing
Date: 2019
Ground truth bias in external cluster validity indices
Publisher: Elsevier BV
Date: 05-2017
Group Based Unsupervised Feature Selection
Publisher: Springer International Publishing
Date: 2020
Shopping intent recognition and location prediction from cyber-physical activities via wi-fi logs
Publisher: ACM
Date: 07-11-2018
A time decoupling approach for studying forum dynamics
Publisher: Springer Science and Business Media LLC
Date: 30-05-2013
A Framework for Feature Selection to Exploit Feature Group Structures
Publisher: Springer International Publishing
Date: 2020
An Efficient Binary Search Based Neuron Pruning Method for ConvNet Condensation
Publisher: Springer International Publishing
Date: 2017
An Ambient–Physical System to Infer Concentration in Open-Plan Workplace
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Generalized information theoretic cluster validity indices for soft clusterings
Publisher: IEEE
Date: 12-2014
Learning to optimise general TSP instances
Publisher: Springer Science and Business Media LLC
Date: 03-02-2022
Discovering outlying aspects in large datasets
Publisher: Springer Science and Business Media LLC
Date: 09-02-2016
A Divide and Conquer Algorithm for Predict+Optimize with Non-convex Problems
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 28-06-2022
Abstract: The predict+optimize problem combines machine learning and combinatorial optimization by predicting the problem coefficients first and then using these coefficients to solve the optimization problem. While this problem can be solved in two separate stages, recent research shows end to end models can achieve better results. This requires differentiating through a discrete combinatorial function. Models that use differentiable surrogates are prone to approximation errors, while existing exact models are limited to dynamic programming, or they do not generalize well with scarce data. In this work we propose a novel ide and conquer algorithm based on transition points to reason over exact optimization problems and predict the coefficients using the optimization loss. Moreover, our model is not limited to dynamic programming problems. We also introduce a greedy version, which achieves similar results with less computation. In comparison with other predict+optimize frameworks, we show our method outperforms existing exact frameworks and can reason over hard combinatorial problems better than surrogate methods.
Can high-order dependencies improve mutual information based feature selection?
Publisher: Elsevier BV
Date: 05-2016
Generalised blockmodelling of social and relational networks using evolutionary computing
Publisher: Springer Science and Business Media LLC
Date: 07-02-2014
OccuSpace: Towards a Robust Occupancy Prediction System for Activity Based Workplace
Publisher: IEEE
Date: 03-2019
Effective global approaches for mutual information based feature selection
Publisher: ACM
Date: 24-08-2014
Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation
Publisher: ACM Press
Date: 2013
UFSSF - An Efficient Unsupervised Feature Selection for Streaming Features
Publisher: Springer International Publishing
Date: 2018
Complex Event Summarization Using Multi-Social Attribute Correlation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Structure-Aware Distance Measures for Comparing Clusterings in Graphs
Publisher: Springer International Publishing
Date: 2014
Contextual location imputation for confined wifi trajectories
Publisher: Springer International Publishing
Date: 2018
A Simple Framework for XAI Comparisons with a Case Study
Publisher: IEEE
Date: 27-05-2022
Flight Delay Prediction using Airport Situational Awareness Map
Publisher: ACM
Date: 05-11-2019
Usingvirtual machine allocation policies to defend against co-resident attacksin cloud computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Scalable Outlying-Inlying Aspects Discovery via Feature Ranking
Publisher: Springer International Publishing
Date: 2015
Utilizing common substructures to speedup tensor factorization for mining dynamic graphs
Publisher: ACM
Date: 29-10-2012
Less Is More: Rejecting Unreliable Reviews for Product Question Answering
Publisher: Springer International Publishing
Date: 2021
Churn in Social Networks: A Discussion Boards Case Study
Publisher: IEEE
Date: 08-2010
From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity
Publisher: Informa UK Limited
Date: 09-03-2020
RST1 and RIPR connect the cytosolic RNA exosome to the Ski complex in Arabidopsis
Publisher: Cold Spring Harbor Laboratory
Date: 26-04-2019
DOI: 10.1101/617894
Abstract: The RNA exosome is a key 3’-5’ exoribonuclease with an evolutionarily conserved structure and function. Its cytosolic functions require the co-factors SKI7 and the Ski complex. Here we demonstrate by co-purification experiments that the ARM repeat protein RESURRECTION1 (RST1) and RST1 INTERACTING PROTEIN (RIPR) connect the cytosolic Arabidopsis RNA exosome to the Ski complex. rst1 and ripr mutants accumulate RNA quality control siRNAs (rqc-siRNAs) produced by the post-transcriptional gene silencing (PTGS) machinery when mRNA degradation is compromised. The small RNA populations observed in rst1 and ripr mutants are also detected in mutants lacking the RRP45B/CER7 core exosome subunit. Thus, molecular and genetic evidence supports a physical and functional link between RST1, RIPR and the RNA exosome. Our data reveal the existence of additional cytosolic exosome co-factors besides the known SKI subunits. RST1 is not restricted to plants, as homologues with a similar domain architecture but unknown function exist in animals, including humans.
Analysing virtual machine usage in cloud computing
Publisher: IEEE
Date: 06-2013
Development and validation of retrospective electronic frailty index using operational data of aged care homes
Publisher: Springer Science and Business Media LLC
Date: 12-2022
DOI: 10.1186/S12877-022-03616-0
Abstract: Although elderly population is generally frail, it is important to closely monitor their health deterioration to improve the care and support in residential aged care homes (RACs). Currently, the best identification approach is through time-consuming regular geriatric assessments. This study aimed to develop and validate a retrospective electronic frailty index (reFI) to track the health status of people staying at RACs using the daily routine operational data records. We have access to patient records from the Royal Freemasons Benevolent Institution RACs (Australia) over the age of 65, spanning 2010 to 2021. The reFI was developed using the cumulative deficit frailty model whose value was calculated as the ratio of number of present frailty deficits to the total possible frailty indicators (32). Frailty categories were defined using population quartiles. 1, 3 and 5-year mortality were used for validation. Survival analysis was performed using Kaplan-Meier estimate. Hazard ratios (HRs) were estimated using Cox regression analyses and the association was assessed using receiver operating characteristic (ROC) curves. Two thousand five hundred eighty-eight residents were assessed, with an average length of stay of 1.2 ± 2.2 years. The RAC cohort was generally frail with an average reFI of 0.21 ± 0.11. According to the Kaplan-Meier estimate, survival varied significantly across different frailty categories ( p 0.01). The estimated hazard ratios (HRs) were 1.12 (95% CI 1.09–1.15), 1.11 (95% CI 1.07–1.14), and 1.1 (95% CI 1.04–1.17) at 1, 3 and 5 years. The ROC analysis of the reFI for mortality outcome showed an area under the curve (AUC) of ≥0.60 for 1, 3 and 5-year mortality. A novel reFI was developed using the routine data recorded at RACs. reFI can identify changes in the frailty index over time for elderly people, that could potentially help in creating personalised care plans for addressing their health deterioration.
Shrink: Distance preserving graph compression
Publisher: Elsevier BV
Date: 09-2017
Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns
Publisher: Springer Science and Business Media LLC
Date: 02-01-2021
Multi-level simulation of the physical, cognitive and social
Publisher: Elsevier BV
Date: 04-2022
Discovering correlated spatio-temporal changes in evolving graphs
Publisher: Springer Science and Business Media LLC
Date: 09-01-2008
LICRE: Unsupervised feature correlation reduction for lipidomics
Publisher: Oxford University Press (OUP)
Date: 13-06-2014
DOI: 10.1093/BIOINFORMATICS/BTU381
Abstract: Motivation: Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of in idual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of s les. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with disease risk, progression and treatment response. Clinically, these lipid biomarkers can be used to construct classification models for the purpose of disease screening or diagnosis. However, the inclusion of a large number of highly correlated biomarkers within a model may reduce classification performance, unnecessarily inflate associated costs of a diagnosis or a screen and reduce the feasibility of clinical translation. An unsupervised feature reduction approach can reduce feature redundancy in lipidomic biomarkers by limiting the number of highly correlated lipids while retaining informative features to achieve good classification performance for various clinical outcomes. Good predictive models based on a reduced number of biomarkers are also more cost effective and feasible from a clinical translation perspective. Results: The application of LICRE to various lipidomic datasets in diabetes and cardiovascular disease demonstrated superior discrimination in terms of the area under the receiver operator characteristic curve while using fewer lipid markers when predicting various clinical outcomes. Availability and implementation: The MATLAB implementation of LICRE is available from ite/licrerepository/ Contact: gerard.wong@bakeridi.edu.au or gerard.wong@unimelb.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
The effect of user features on churn in social networks
Publisher: ACM
Date: 15-06-2011
A stochastic approximation approach to fixed instance selection
Publisher: Elsevier BV
Date: 05-2023
POI recommendation with queuing time and user interest awareness
Publisher: Springer Science and Business Media LLC
Date: 03-10-2022
DOI: 10.1007/S10618-022-00865-W
Abstract: Point-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential patterns behaviours. These studies do not pay attention to user personal interests, which is a significant factor for POI recommendation. Besides user interests, queuing time also plays a significant role in affecting user mobility behaviour, e.g., having to queue a long time to enter a POI might reduce visitor’s enjoyment. Recently, attention-based recurrent neural networks-based approaches show promising performance in the next POI recommendation task. However, they are limited to single head attention, which can have difficulty in finding the appropriate user mobility behaviours considering complex relationships among POI spatial distances, POI check-in time, user interests and POI queuing times. In this research work, we are the first to consider queuing time and user interest awareness factors for next POI recommendation. We demonstrate how it is non-trivial to recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi-head attention transformer model called TLR-M_UI. The model recommends the next POIs to the target users and predicts queuing time to access the POIs simultaneously by considering user mobility behaviours. The proposed model utilises POIs description-based user personal interest that can also solve the new categorical POI cold start problem. Extensive experiments on six real-world datasets show that the proposed models outperform the state-of-the-art baseline approaches in terms of precision, recall, and F1-score evaluation metrics. The model also predicts and minimizes the queuing time. For the reproducibility of the proposed model, we have publicly shared our implementation code at GitHub ( ajalhalder/TLR-M_UI ).
OnlineAirTrajClus: An online aircraft trajectory clustering for tarmac situation awareness
Publisher: IEEE
Date: 03-2019
Identifying in-app user actions from mobile web logs
Publisher: Springer International Publishing
Date: 2018
Multi-attention 3D Residual Neural Network for Origin-Destination Crowd Flow Prediction
Publisher: IEEE
Date: 11-2020
Bus travel time prediction with real-time traffic information
Publisher: Elsevier BV
Date: 08-2019
rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking
Publisher: Springer Science and Business Media LLC
Date: 28-11-2016
Towards an Unsupervised Feature Selection Method for Effective Dynamic Features
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Identifying glycan motifs using a novel subtree mining approach
Publisher: Springer Science and Business Media LLC
Date: 04-02-2020
DOI: 10.1186/S12859-020-3374-4
Abstract: Glycans are complex sugar chains, crucial to many biological processes. By participating in binding interactions with proteins, glycans often play key roles in host–pathogen interactions. The specificities of glycan-binding proteins, such as lectins and antibodies, are governed by motifs within larger glycan structures, and improved characterisations of these determinants would aid research into human diseases. Identification of motifs has previously been approached as a frequent subtree mining problem, and we extend these approaches with a glycan notation that allows recognition of terminal motifs. In this work, we customised a frequent subtree mining approach by altering the glycan notation to include information on terminal connections. This allows specific identification of terminal residues as potential motifs, better capturing the complexity of glycan-binding interactions. We achieved this by including additional nodes in a graph representation of the glycan structure to indicate the presence or absence of a linkage at particular backbone carbon positions. Combining this frequent subtree mining approach with a state-of-the-art feature selection algorithm termed minimum-redundancy, maximum-relevance (mRMR), we have generated a classification pipeline that is trained on data from a glycan microarray. When applied to a set of commonly used lectins, the identified motifs were consistent with known binding determinants. Furthermore, logistic regression classifiers trained using these motifs performed well across most lectins examined, with a median AUC value of 0.89. We present here a new subtree mining approach for the classification of glycan binding and identification of potential binding motifs. The Carbohydrate Classification Accounting for Restricted Linkages (CCARL) method will assist in the interpretation of glycan microarray experiments and will aid in the discovery of novel binding motifs for further experimental characterisation.
Improving Personalized Trip Recommendation by Avoiding Crowds
Publisher: ACM
Date: 24-10-2016
Security games for virtual machine allocation in cloud computing
Publisher: Springer International Publishing
Date: 2013
FILTA: Better View Discovery from Collections of Clusterings via Filtering
Publisher: Springer Berlin Heidelberg
Date: 2014
Feature Selection for Multiclass Binary Data
Publisher: Springer International Publishing
Date: 2018
ciForager
Publisher: Association for Computing Machinery (ACM)
Date: 10-2012
Abstract: Data mining techniques for understanding how graphs evolve over time have become increasingly important. Evolving graphs arise naturally in erse applications such as computer network topologies, multiplayer games and medical imaging. A natural and interesting problem in evolving graph analysis is the discovery of compact subgraphs that change in a similar manner. Such subgraphs are known as regions of correlated change and they can both summarise change patterns in graphs and help identify the underlying events causing these changes. However, previous techniques for discovering regions of correlated change suffer from limited scalability, making them unsuitable for analysing the evolution of very large graphs. In this paper, we introduce a new algorithm called ciForager, that addresses this scalability challenge and offers considerable improvements. The efficiency of ciForager is based on the use of new incremental techniques for detecting change, as well as the use of Voronoi representations for efficiently determining distance. We experimentally show that ciForager can achieve speedups of up to 1000 times over previous approaches. As a result, it becomes feasible for the first time to discover regions of correlated change in extremely large graphs, such as the entire BGP routing topology of the Internet.
Improving Algorithm Response to Preference Changes in Multiobjective Optimisation Using Archives
Publisher: IEEE
Date: 06-2019
Traveling Officer Problem: Managing Car Parking Violations Efficiently Using Sensor Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
A game theoretical approach to defend against co-resident attacks in cloud computing: Preventing co-residence using semi-supervised learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Semi-supervised Blockmodelling with Pairwise Guidance
Publisher: Springer International Publishing
Date: 2019
Adaptive Cluster Tendency Visualization and Anomaly Detection for Streaming Data
Publisher: Association for Computing Machinery (ACM)
Date: 03-12-2016
DOI: 10.1145/2997656
Abstract: The growth in pervasive network infrastructure called the Internet of Things (IoT) enables a wide range of physical objects and environments to be monitored in fine spatial and temporal detail. The detailed, dynamic data that are collected in large quantities from sensor devices provide the basis for a variety of applications. Automatic interpretation of these evolving large data is required for timely detection of interesting events. This article develops and exemplifies two new relatives of the visual assessment of tendency (VAT) and improved visual assessment of tendency (iVAT) models, which uses cluster heat maps to visualize structure in static datasets. One new model is initialized with a static VAT/iVAT image, and then incrementally (hence inc-VAT/inc-iVAT) updates the current minimal spanning tree (MST) used by VAT with an efficient edge insertion scheme. Similarly, dec-VAT/dec-iVAT efficiently removes a node from the current VAT MST. A sequence of inc-iVAT/dec-iVAT images can be used for (visual) anomaly detection in evolving data streams and for sliding window based cluster assessment for time series data. The method is illustrated with four real datasets (three of them being smart city IoT data). The evaluation demonstrates the algorithms’ ability to successfully isolate anomalies and visualize changing cluster structure in the streaming data.
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
Publisher: Association for Computing Machinery (ACM)
Date: 13-08-2020
DOI: 10.1145/3393692
Abstract: This article investigates the cyber-physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation,propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user’s activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications.
Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces
Publisher: Springer Science and Business Media LLC
Date: 03-01-2018
Tour recommendation and trip planning using location-based social media: a survey
Publisher: Springer Science and Business Media LLC
Date: 11-12-2018
Multi-attention graph neural networks for city-wide bus travel time estimation using limited data
Publisher: Elsevier BV
Date: 09-2022
Event Popularity Prediction Using Influential Hashtags From Social Media
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Transformer-Based Multi-task Learning for Queuing Time Aware Next POI Recommendation
Publisher: Springer International Publishing
Date: 2021
Exploiting Redundancy in Network Flow Information for Efficient Security Attack Detection
Publisher: Springer Nature Switzerland
Date: 2022
Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs
Publisher: Springer International Publishing
Date: 2016
Enhancing Diffusion Models by Embedding Cognitive Reasoning
Publisher: IEEE
Date: 08-2018
TRIBAC: Discovering Interpretable Clusters and Latent Structures in Graphs
Publisher: IEEE
Date: 12-2014
A Bayesian classifier for learning from tensorial data
Publisher: Springer Berlin Heidelberg
Date: 2013
Personalized Itinerary Recommendation with Queuing Time Awareness
Publisher: ACM
Date: 07-08-2017
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Publisher: arXiv
Date: 2022
Solving multiple travelling officers problem with population-based optimization algorithms
Publisher: Springer Science and Business Media LLC
Date: 17-05-2020
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Publisher: Association for Computing Machinery (ACM)
Date: 18-01-2022
DOI: 10.1145/3490234
Abstract: The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to improve healthcare practice. EHRs provide a rich set of information that includes demographics, medical history, medications, laboratory test results, and diagnosis. Data mining and analytics techniques have extensively exploited EHR information to study patient cohorts for various clinical and research applications, such as phenotype extraction, precision medicine, intervention evaluation, disease prediction, detection, and progression. But the presence of erse data types and associated characteristics poses many challenges to the use of EHR data. In this article, we provide an overview of information found in EHR systems and their characteristics that could be utilized for secondary applications. We first discuss the different types of data stored in EHRs, followed by the data transformations necessary for data analysis and mining. Later, we discuss the data quality issues and characteristics of the EHRs along with the relevant methods used to address them. Moreover, this survey also highlights the usage of various data types for different applications. Hence, this article can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.
Virtual machine customization and task mapping architecture for efficient allocation of cloud data center resources
Publisher: Oxford University Press (OUP)
Date: 30-11-2016
Erratum: LICRE: unsupervised feature correlation reduction for lipidomics (Bioinformatics (2014) 30:19 (2832-2833) DOI: 10.1093/bioinformatics/btu381)
Publisher: Oxford University Press (OUP)
Date: 27-10-2015
Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency
Publisher: Springer Science and Business Media LLC
Date: 05-05-2018
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Related Funding Activities
Linkage Projects - Grant ID: LP190100991
Start Date: 03-2021
End Date: 06-2024
Amount: $315,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP200101960
Start Date: 08-2020
End Date: 04-2024
Amount: $477,000.00
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View Funded ActivityDiscovery Projects - Grant ID: DP170103174
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End Date: 06-2020
Amount: $498,500.00
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View Funded Activity