Profile
Dr. Martin Ester, is the Digital Health Hub Co-Lead for the Point of Care Health Technologies Team. Dr. Ester received a PhD in Computer Science from ETH Zurich, Switzerland, in 1989 with a thesis on knowledge-based systems and logic programming. He has been working for Swissair developing expert systems before he joined University of Munich as an Assistant Professor in 1993. Since November 2001, he has been an Associate Professor, now Full Professor at the School of Computing Science of Simon Fraser University, where he co-directs the Database and Data mining research lab. From May 2010 to April 2015, he has served as the School Director. Dr. Ester has published extensively in the top conferences and journals of his field such as ACM SIGKDD, WWW, ICDM and ACM RecSys. According to Google Scholar, his publications have received more than 23'000 citations, and his h-index is 53. He received the KDD 2014 Test of Time Award for his paper on DBSCAN. Martin Ester’s current research interests include social network analysis, recommender systems, biological network analysis and data mining for personalized medicine. Dr. Ester is very interested in the translation of his research results into practical applications and has had many collaborations with partners in industry, science, and government. AGE-WELL Funded ProjectsOutputs
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| Heidegger: Interpretable Temporal Causal DiscoveryTemporal causal discovery aims to find cause-effect relationships between time-series. However, none of the existing techniques is able to identify the causal profile, the temporal pattern that the causal variable needs to follow in order to trigger the most significant change in the outcome. Toward a new horizon, this study introduces the novel problem of Causal Profile Discovery, which is crucial for many applications such as adverse drug reaction and cyber-attack detection. This work correspondingly proposes Heidegger to discover causal profiles, comprised of a flexible randomized block design for hypothesis evaluation and an efficient profile search via on-the-fly graph construction and entropy-based pruning. Heidegger's performance is demonstrated/evaluated extensively on both synthetic and real-world data. The experimental results show the proposed method is robust to noise and flexible at detecting complex patterns.PCHT, AWNIH-DHC Simon Fraser University | Scientific Excellence - Advancing Knowledge | 2021-05-24 | "Mehrdad Mansouri, ", Ali Arab, "Zahra Zohrevand ", Martin Ester | | Automation of CT-based haemorrhagic stroke assessment for improved clinical outcomes: study protocol and designHaemorrhagic stroke is of significant healthcare concern due to its association with high mortality and lasting impact on the survivors’ quality of life. Treatment decisions and clinical outcomes depend strongly on the size, spread and location of the haematoma. Non-contrast CT (NCCT) is the primary neuroimaging modality for haematoma assessment in haemorrhagic stroke diagnosis. Current procedures do not allow convenient NCCT-based haemorrhage volume calculation in clinical settings, while research-based approaches are yet to be tested for clinical utility; there is a demonstrated need for developing effective solutions. The project under review investigates the development of an automatic NCCT-based haematoma computation tool in support of accurate quantification of haematoma volumes.PCHT, AWNIH-DHC Simon Fraser University, Fraser Health | Scientific Excellence - Advancing Knowledge | 2021-05-24 | | | A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CTThis research was aimed to develop novel methods that can improve precision and efficiency for hematoma segmentation and quantification in clinical applications for more effective stroke management. The three objectives are: 1) To develop an artificial intelligence method applying Convoluted Neural Network with Deep Supervision (CNN-DS), to fully automate segment and quantify ICH volume in CT images; 2) To evaluate the performance of the CNN-DS method in terms of accuracy and efficiency; 3) To compare the performance of this AI method to that of standard clinical evaluation and established machine learning (ML) based methods. Simon Fraser University, Fraser Health | Scientific Excellence - Advancing Knowledge | 2020-11-09 | |
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