Abstract
Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. ML is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g. prediction of disease progression, extraction of medical knowledge for outcome research, therapy planning and support, and for the overall patient management. ML is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and intelligent alarming resulting in effective and efficient monitoring. It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care. Below, we summarize some major ML applications in medicine.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akay, Y.M., Akay, M., Welkowitz, W., and Kostis, J.B. “Noninvasive detection of coronary artery disease using wavelet-based fuzzy neural networks”. IEEE Engineering in Medicine and Biology, 761–764, 1994.
Alexopoulos, E., Dounias, G.D. and Vemmos, K. “Medical diagnosis of stroke using inductive machine learning”. In [38].
Anderson, J.G. “Clearing the way for physician’s use of clinical information systems”. Communications of the ACM, 40,8, 83–90, 1997.
Asteroth, A. and Möller, K. “Identification of individualized models of the human cardiovascular system”. In [38].
Bourlas, Ph., Sgouros, N., Papakonstantinou, G., and Tsanakas, P. “Towards a knowledge acquisition and management system for ECG diagnosis”. In Proceedings of 13 th International Congress Medical Informatics Europe-MIE96, Copenhagen, 1996.
Bourlas, Ph., Giakoumakis, E. and Papakonstantinou, G. “A knowledge acquisition and management system for ECG diagnosis”. In [38].
Bratko, I., Mozetic, I., and Lavrač, N. KARDIO: A study in deep and qualitative knowledge for expert systems, Cambridge, Massachusetts: MIT Press, 1989.
Delaney, P.M, Papworth, G.D, and King, R.G. “Fibre optic confocal imaging (FOCI) for in vivo subsurface microscopy of the colon”. In Methods in disease: Investigating the Gastrointestinal Tract, Preedy, V.R. and Watson, R.R. (eds.), Greenwich Medical Media, London, 1998.
Gindi, G.R., Darken, C.J., O’Brien, K.M., Sterz, M.L., and Deckelbaum, L.I. “Neural network and conventional classifiers for fluorescence-guided laser angioplasty”. IEEE Transactions on Biomedical Engineering, 38,3, 246–252, 1991.
Hanka, R., Harte, T.P., Dixon, A.K., Lomas, D.J., and Britton, P.D. “Neural networks in the interpretation of contrast-enhanced magnetic resonance images of the breast”. In Proceedings of Healthcare Computing, Harrogate, UK, 275–283, 1996.
Hau, D., and Coiera, E. “Learning qualitative models of dynamic systems”. Machine Learning, 26, 177–211, 1997.
Ifeachor, E.C., and Rosen, K. G. (eds.) Proceedings of the International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, Plymouth, UK, 1994.
Innocent, P.R., Barnes, M., and John, R. “Application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification”. Artificial Intelligence in Medicine, 11, 241–263, 1997.
Jankowski, N. “Approximation and classification in medicine with IncNet neural networks”. In [38].
Karkanis, S., Magoulas, G.D., Grigoriadou, M. and Schurr, M. “Detecting abnormalities in colonoscopic images by textural description and neural networks”. In [38].
Karkanis, S., Galoussi, K. and Maroulis, D. “Classification of endoscopic images based on texture spectrum”. In [38].
Kennedy, L.R., Harrison, R.F., Burton, A.M., Fraser, H.S., Hamer, W.G., MacArthur, D., McAllum, R., and Steedman, D.J. “An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements”. Computer Methods and Programs in Biomedicine, 52, 93–103, 1997.
Kralj, K. and Kuka, M. “Using machine learning to analyze attributes in the diagnosis of coronary artery disease”. In Proceedings of Intelligent Data Analysis in Medicine and Pharmacology-IDAMAP98, Brighton, UK, 1998.
Lane, V.P., Lane, D., and Littlejohns, P. “Neural networks for decision making related to asthma diagnosis and other respiratory disorders”. In Proceedings of Healthcare Computing, Harrogate, UK, 85–93, 1996.
Lavrač, N. “Data mining in medicine: Selected techniques and applications”. In Proceedings of Intelligent Data Analysis in Medicine and Pharmacology-IDAMAP98, Brighton, UK, 1998.
Lim, C.P., Harrison, R.F., and Kennedy, R.L. “Application of autonomous neural network systems to medical pattern classification tasks”. Artificial Intelligence in Medicine, 11, 215–239, 1997.
Micheli-Tzanakou, E., Yi, C., Kostis, W.J., Shindler, D.M., and Kostis, J.B. “Myocardial infarction: Diagnosis and vital status prediction using neural networks”. IEEE Computers in Cardiology, 229–232, 1993.
Moustakis, V. and Charissis, G. “Machine learning and medical decision making”. In [38].
Nekovei, R. and Sun, Y. “Back-propagation network and its configuration for blood vessel detection in angiograms”. IEEE Transactions on Neural Networks, 6,1, 64–72, 1995.
Neves, J., Alves, V., Nelas, L., Romeu, A. and Basto, S. “An information system that supports knowledge discovery and data mining in medical imaging”. In [38].
Pattichis, C., Schizas, C., and Middleton, L. “Neural network models in EMG diagnosis”. IEEE Transactions on Biomedical Engineering, 42,5, 486–496, 1995.
Phee, S.J., Ng, W.S., Chen, I.M., Seow-Choen, F., and Davies, B.L. “Automation of colonoscopy part II: visual-control aspects”. IEEE Engineering in Medicine and Biology, May/June, 81–88, 1998.
Pouloudi, A. “Information technology for collaborative advantage in health care revisited”. Information and Management, 35,6, 345–357, 1999.
Pranckeviciene, E. “Finding similarities between an activity of the different EEGs by means of a single layer perceptron”. In [38].
Prentza, A. and Wesseling, K.H. “Catheter-manometer system damped blood pressures detected by neural nets”. Medical and Biological Engineering and Computing, 33, 589–595, 1995.
Reategui, E.B., Campbell, J.A., and Leao, B.F. “Combining a neural network with casebased reasoning in a diagnostic system”. Artificial Intelligence in Medicine, 9, 5–27, 1996.
Ridderikhoff, J. and van Herk, B. “Who is afraid of the system? Doctors’ attitude towards diagnostic systems”. International Journal of Medical Informatics 53, 91–100, 1999.
Ruseckaite, R. “Computer interactive system for ascertainment of visual perception disorders”. In [38].
Schurr, M. “The Role of Machine Learning Methods in Endoscopic Techniques”. In [38].
Strausberg, J. and Person, M. “A process model of diagnostic reasoning in medicine”. International Journal of Medical Informatics, 54, 9–23, 1999.
Zupan, B., Halter, J.A., and Bohanec, M. “Qualitative model approach to computer assisted reasoning in physiology”. In Proceedings of Intelligent Data Analysis in Medicine and Pharmacology-IDAMAP98, Brighton, UK, 1998.
Zelič, I., Lavrač, N., Najdenov, P. and Rener-Primec, Z. “Impact of machine learning to the diagnosis and prognosis of first cerebral paroxysm”. In [38].
Proceedings of the Workshop on Machine Learning in Medical Applications, Advanced Course on Artificial Intelligence (ACAI’ 99), Chania, Greece, 1999 (http://www.iit.demokritos.gr/skel/eetn/acai99/Workshops.htm).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Magoulas, G.D., Prentza, A. (2001). Machine Learning in Medical Applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_19
Download citation
DOI: https://doi.org/10.1007/3-540-44673-7_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42490-1
Online ISBN: 978-3-540-44673-6
eBook Packages: Springer Book Archive