Leiden Institute of Advanced Computer Science

      Loch Muick, Aberdeenshire, Scotland

      Peter Lucas

      彼得.卢卡斯

      计算机与信息科学研究所
      访问地址: Leiden Institute of Advanced Computer Science (LIACS)
      Snellius Building, Niels Bohrweg 1, Office 123
      邮递地址: 2333 CA Leiden, The Netherlands

      电子邮件: peterl at cs.ru.nl
      电话: +31 24 3652611; 传真: +31 24 3653366

      个人简介

      Peter Lucas，荷兰奈梅根大学模式系统开发部主任, 荷兰莱顿大学教授，奈梅根大学副教授，IEEE高级会员。1985年，作为荷兰计算机人工智能研究领域的开拓者之一，在荷兰数学和计算机科学国家研究中心(CWI)成立了自己的研究小组。目前，他的研究项目专注于定性与定量方法相结合的计算机辅助决策。在这个领域，他的很多研究成果被发表在重要的人工智能刊物中。他的另一个研究链是概率图模型。2004年，他曾被选举为概率图模型研讨会（PGM2004）的主席。两年一度的PGM和ECSQARU被认为是欧洲人工智能科学活动中最重要的两个研讨会。他的很多研究在新方法的开发以及这些方式方法在生物医学领域的探索两个方面同时齐头并进。作为国际人工智能在医学研究领域的成员，在ESF和NWO支持下，他成功组织过相关领域的研讨会，如ECCAI。与此同时，他和医疗机构同事一起在一些具有很高知名度和影响的医疗（临床）文献中发表他们的研究成果。
      在Peter Lucas的领导下，他的研究组在概率图形模型、基于模型的推理和应用逻辑方面有相当的专业知识。该小组在应用人工智能，特别是概率图形模型及在计算科学、临床问题、临床准则的形式方法方面还有独有的经验。并与研究型的生物医学信息学团体有着紧密的合作。其致力于传染病的诊断和治疗、良性肿瘤预后和治疗及乳腺癌的检测。已在相关顶级专业期刊上发表。其作为项目主持人承担了17项总计经费超过700万欧元，他还是多个国际学术组织的执行委员。

      研讨会议, 特殊问题研究

      • Prestigious Applications of Intelligent Systems (PAIS-2012) with ECAI-2012, Montpelier, France, 29-30 August, 2012
      • Emerging Smart Technologies for Personalised Healthcare (SmarTech4Health) at CBMS 2012, Rome, Italy, 20-22 June, 2012
      • Probabilistic Problem Solving in Biomedicine (ProBioMed-2011) Bled, Slovenia, July 2, 2011
      • CIHC 2010: Computational Intelligence in Health Care, Eindhoven, 20-24 September, 2010
      • ACAI'09: Advanced Course in Artificial Intelligence '09, Belfast, 23-29 August, 2009
      • Lorentz Centre Workshop: Computer-based Clinical Guidelines and Protocols 2008
      • ECAI'06: Workshop on AI techniques in healthcare: evidence-based guidelines and protocols
      • PGM'04: Probabilistic Graphical Models 2004
      • Workshop on Model-based and Qualitative Reasoning in Biomedicine during AIME'03
      • 27-29 October, 2002: ESF workshop, Fundamentals of Medical Informatics
      • IDAMAP-2002 Workshop during ECAI 2002 (Proceedings now online!)
      • Special issue of AIMED on Bayesian Models in Medicine
      • Workshop on Bayesian Models in Medicine during AIME'01 (Proceedings now online)
      • Workshop on Prognostic Models in Medicine during AIME'99 (Proceedings online)
      • Workshop on Intelligent Prognostic Methods in Medical Diagnosis and Treatment Planning during CESA'98

      研究活动

      • AERIAL project [Video]
      • eMomCare project [Video]
      • PANDORA project
      • Octopus project
      • B-Sreen project: Bayesian networks and computer-aided detection
      • TimeBayes and ProBayes projects: Bayesian networks and machine learning
      • Protocure project: using formal methods in guideline design
      • Theory of model-based diagnosis
      • Bayesian network and decision theory
      • Logical foundations of knowledge-based systems
      • MONET network of excellence (Domain-status report now online)
      • ICEA project: Modelling bacterial colonisation by means Bayesian networks, and applying decision theory to improve the use of antibiotics in the intensive care unit
      • NHL and CRC projects: Optimal treatment of patients with cancer (non-Hodgkin lymphoma of the stomach and colorectal cancer)
      • Stroke project: Prediction of outcome of stroke, taking MRI data into account
      • Vitatron: Intelligent systems for pacemaker reprogramming

      著作

      • Computer-based Medical Guidelines and Protocols: A Primer and Current Trends. Studies in Health Technology and Informatics, Vol. 139, A. Ten Teije, S. Miksch and P. Lucas, July 2008, approx. 282 pp, hardcover, ISBN: 978-1-58603-873-1
      • Advances in Probabilistic Graphical Models. Series: Studies in Fuzziness and Soft Computing , Vol. 213. Lucas, Peter; Gamez, Jose A.; Salmeron, Antonio (Eds.) 2007, X, 396 p., hardcover, ISBN: 978-3-540-68994-2

      出版论文

      人工智能
      • Introductory scientific paper about knowledge-based expert systems, written for UNESCO (UNESCO, 2007)

      医用人工智能

      • Conversion of a Rule-based System to a Belief Network (including design issues) (Medical Informatics, 1993)
      • Using Theorem Proving Techniques to build Medical Expert Systems (Medical Artificial Intelligence, 1993)
      • Refinement of a Rule-based System (Medical Artificial Intelligence, 1994)
      • Logic Engineering in Medicine (Knowledge Engineering Review, 1995)
      • Knowledge Acquisition for Decision-theoretic Expert Systems (AISB, 1996)
      • A Decision-theoretic Network Approach to Treatment Management and Prognosis (Proceedings of ES'97)
      • Decision Support in the Management of non-Hodgkin lymphoma of the Stomach (Methods of Information in Medicine, 1998)
      • Prognostic methods in medicine (Artificial Intelligence in Medicine, 1999)
      • A diagnostic advice systems based on pathophysiological models of diseases (Proceedings MIE99)
      • Improving antibiotic therapy of ventilator associated pneumonia using a probabilistic approach (Proceedings MIE99)
      • Probabilistic and decision-theoretic approach to the management of infectious disease in the ICU (Artificial Intelligence in Medicine 2000; 19(3): 251-279)
      • Prognostic models in medicine: AI and statistical approaches (Methods of Information in Medicine 2001; 40: 1-5)
      • Bayesian networks in medicine: a model-based approach to medical decision making (K-P. Adlassnig (ed.), Proceedings of the EUNITE workshop on Intelligent Systems in patient Care, Vienna, Oct. 2001, pp. 73-97)
      • A model-based approach to improved prescription of antibiotics (MONET Newsletter, 2003)
      • Quality checking of medical guidelines through logical abduction (Proceedings AI-2003, Springer, London, 2003, pp. 309-321)
      • Meta-level verification of the quality of medical guidelines using interactive theorem proving (JELIA-2004, LNAI 3229, Springer, Berlin Heidelberg, 2004, pp. 654-666)
      • Verification of medical guidelines using background knowledge in task networks (IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 6, 2007, pp. 832-846)
      • Checking the quality of clinical guidelines using automated reasoning tools (Theory and Practice of Logic Programming, vol. 8, pp. 941-641, 2008)
      • A decision support system for breast cancer detection in screening program (Proceedings ECAI 2008, pp. 658-662)
      • A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients (Expert Systems with Applications, vol. 36, 2009, pp. 1249-1258)
      • Improved mammographic CAD performance using multi-view information: a Bayesian network framework (Phys. Med. Biol. 54 (2009) 1131-1147)
      • Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions (Artificial Intelligence in Medicine, 2009)
      • Using model checking for critiquing based on clinical guidelines (Artificial Intelligence in Medicine, vol. 46 (2009) 19-36)
      • Causal probabilistic modelling for two-view mammographic analysis (AI in Medicine, LNAI 5651, 2009, pp. 395-404)
      • Managing COPD exacerbations with telemedicine (AI in Medicine, LNAI 6747, 2011, pp. 169-178)
      • A probabilistic framework for image information fusion with an application to mammographic analysis (Medical Image Analysis 16 (2012) 865-875)

      基于模型推理和贝叶斯网络

      • Symbolic Diagnosis and its Formalisation (Knowledge Engineering Review, 1997)
      • Modeling Interactions in Diagnosis (Proceedings of Proceedings of CESA'96 IMACS Multiconference, Symposium on Modelling, Analysis and Simulation, 1996)
      • Diagnosis as Hypothesis Refinement (Proceedings of NAIC'97, 1997)
      • Analysis of notions of diagnosis (Artificial Intelligence, vol. 105, 1998)
      • A model-based system for pacemaker reprogramming (Artificial Intelligence in Medicine 1999; 17: 249-269)
      • Certainty-factor-like structures in Bayesian networks (Technical Report, 2000; the Knowledge-based Systems journal 2001; 14: 327-335)
      • Bayesian model-based diagnosis (Technical Report, 2000; published in the International Journal of Approximate Reasoning 2001; 27(2): 99-119)
      • Comparison of rule-based and Bayesian network approaches in medical diagnostic systems (In: S. Quaglini, P. Barahona, S. Andreassen (eds.). Artificial Intelligence in Medicine (AIME2001), LNAI2101, Springer, Berlin, 2001, pp. 283-292)
      • Decision network semantics of branching constraint satisfaction problems (Proceedings ECSQARU 2003, LNAI2711. Springer, Berlin, 2003, pp. 230-242)
      • Bayesian network modelling by qualitative patterns (ECAI 2002, 690-694)
      • A system for pacemaker treatment advice (ECAI 2004, pp. 735-739)
      • Bayesian networks in biomedicine and health-care (Artificial Intelligence in Medicine 30 (2004) 201-214)
      • Bayesian network modelling through qualitative patterns (Artificial Intelligence 2005; 163: 233-263)
      • Exploiting causal independence in large Bayesian networks (Knowledge-based Systems, 18 (2005) 153-162)
      • Conflict-based diagnosis: adding uncertainty to model-based diagnosis (IJCAI 2007, pp. 380-388)
      • A generic qualitative characterization of independence of causal influence Journal of Approximate Reasoning, 48 (2008) 214-236
      • Explaining clinical decisions by extracting regularity patterns (Decision Support Systems, 44 (2008) 397-408)
      • A Bayesian decision-support system for diagnosing ventilator-associated pneumonia (Intensive Care Medicine, 2008)
      • Standardizing Research Methods for Prognostics (International Conference on Prognostics and Health Management 2008)
      • The Probabilistic Interpretation of Model-based Diagnosis (ECSQARU 2009, LNAI 5590, pp. 204-215)
      • Using Bayesian networks in an industrial setting: making printing systems adaptive (Proceedings of ECAI-2010, H. Coelho, et al., IOS Press, Amsterdam, pp. 401-406)
      • Modelling the interactions between discrete and continuous causal factors in Bayesian networks (Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010), Petri Myllymki, Teemu Roos and Tommi Jaakkola (Eds), HIIT Publications 2010, pp. 185-193)
      • Generalising the interaction rules in probabilistic logic (IJCAI 2011, pp. 912-917)

      机器学习
      • Enhancement of learning by declarative expert-based models (IDAMAP, 2000)
      • Expert knowledge and its role in learning Bayesian networks in medicine (extended version) (In: S. Quaglini, P. Barahona, S. Andreassen (eds.). Artificial Intelligence in Medicine (AIME2001), LNAI2101, Springer, Berlin, 2001, pp. 156-166)
      • Learning Bayesian-network topologies in realistic medical domains (IDAMAP, 2001)
      • Restricted Bayesian network structure learning (in: G.A. Gamez, S. Moral, A. Salmeron (eds.). Advances in Bayesian Networks, Studies in Fuzziness and Soft Computing, vol. 146, Springer-Verlag, Berlin, 2004, pp. 217-232). )
      • Markov equivalence in Bayesian networks (Technical Report, NIII-R0436, 2004)
      • Bayesian analysis, pattern analysis, and data mining in health care (Current Opinion in Critical Care 2004, vol. 10, pp. 399-403)
      • Integrating logical reasoning and probabilistic chain graphs (Proceedings ECML PKDD 2009, volume 5781 of LNAI, pages 548-563, 2009)
      • Using local context information to improve automatic mammographic mass detection (Proceedings of MEDINFO-2010, IOS Press, Amsterdam, pp. 1291-1295)
      • Critiquing knowledge representation in medical Image interpretation using structure learning (KR4HC-2010, volume 6512 of LNAI, Springer, 2011)

      医学

      • Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units (The Lancet of Infectious Disease 2005, vol. 5, 305-312)
      • Effects of systemic antibiotic therapy on bacterial resistence in the repiratory tract of mechanically ventilated patients (Intensive Care Medicine 2008, vol. 34, pp. 692-699)
      • Predicting pathogens causing ventilator-associated pneumonia using a Bayesian network model (Journal of Antimicrobial Chemotherapy 2008, vol. 62, 184-188)

      教学材料

      • ACAI09: The Model-based Approach to Medical Decision Support
      • Data Mining
      • SIKS course Learning and Reasoning:
        Probabilistic Reasoning: Uncertainty and Bayesian Networks
      • SIKS course Computational Intelligence:
        Expert Systems: a knowledge-based approach to intelligent systems
      • Bayesian Networks
      • Programming in Prolog
      • Intelligent Systems
      • Knowledge Representation and Reasoning
      • Medical Informatics



      Peter Lucas | Staff & Students | LIACS
      Leiden University

      Last updated: 10th June, 2012
      peterl at cs.ru.nl

      Free Software
      Support Linux
      Boycott Microsoft
      		 Support Linux, Boycot Microsoft