Machine learning and software engineering in health informatics

Health informatics is a field in which the disciplines of software engineering and machine learning necessarily co-exist. This discussion paper considers the interaction of software engineering and machine learning, set within the context of health informatics, where the scale of clinical practice requires new engineering approaches from both disciplines. We introduce applications implemented in large on-going research programmes undertaken between the Departments of Engineering Science and Computer Science at Oxford University, the Oxford University Hospitals NHS Trust, and the Guy's and St Thomas' NHS Foundation Trust, London.

[1]  Linda C. van der Gaag,et al.  Probabilistic Graphical Models , 2014, Lecture Notes in Computer Science.

[2]  David A. Clifton,et al.  Novelty Detection with Multivariate Extreme Value Statistics , 2011, J. Signal Process. Syst..

[3]  Shlomo Zilberstein,et al.  Formal models and algorithms for decentralized decision making under uncertainty , 2008, Autonomous Agents and Multi-Agent Systems.

[4]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[5]  Lotfi A. Zadeh,et al.  Is there a need for fuzzy logic? , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[6]  Prithviraj Sen,et al.  Representing and Querying Correlated Tuples in Probabilistic Databases , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[7]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[8]  G. Clifford,et al.  Wireless technology in disease management and medicine. , 2012, Annual review of medicine.

[9]  David A. Clifton,et al.  Semiconductor wireless technology for chronic disease management , 2011 .

[10]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[11]  Guoqing Chen Fuzzy logic in data modeling: semantics, constraints, and database design , 1998 .

[12]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[15]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[16]  David A. Clifton,et al.  Automated Novelty Detection in Industrial Systems , 2008, Advances of Computational Intelligence in Industrial Systems.

[17]  Yuxin Ding,et al.  Host-based intrusion detection using dynamic and static behavioral models , 2003, Pattern Recognit..

[18]  L. Tarassenko,et al.  Photoplethysmographic derivation of respiratory rate: a review of relevant physiology , 2012, Journal of medical engineering & technology.

[19]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[20]  S. Roberts,et al.  Coordination vs . information in multi-agent decision processes , 2010 .

[21]  Christopher Ré,et al.  Probabilistic databases: diamonds in the dirt , 2009, CACM.

[22]  David A. Clifton,et al.  Annual Review: Wireless Technology in Disease State Management and Medicine , 2011 .

[23]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[24]  Dan Olteanu,et al.  Conditioning probabilistic databases , 2008, Proc. VLDB Endow..

[25]  David A. Clifton,et al.  Probabilistic approach to the condition monitoring of aerospace engines , 2009 .

[26]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[27]  David A. Clifton,et al.  Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory , 2010, BIOSTEC.