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Search: authors:"Dinh Phung"

6 papers found.
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Discovering topic structures of a temporally evolving document corpus

Trust and a winner of numerous awards. Dinh Phung is Professor of Computer Science in the School of Information Technology at Deakin University, Australia, and the Deputy Director of the Centre for

Bayesian Nonparametric Approaches to Abnormality Detection in Video Surveillance

In data science, anomaly detection is the process of identifying the items, events or observations which do not conform to expected patterns in a dataset. As widely acknowledged in the computer vision community and security management, discovering suspicious events is the key issue for abnormal detection in video surveillance. The important steps in identifying such events include ...

A framework for feature extraction from hospital medical data with applications in risk prediction

Background Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser ...

Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic ...

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

Background To date, our ability to accurately identify patients at high risk from suicidal behaviour, and thus to target interventions, has been fairly limited. This study examined a large pool of factors that are potentially associated with suicide risk from the comprehensive electronic medical record (EMR) and to derive a predictive model for 1–6 month risk. Methods 7,399 ...