Discovering patterns in sequences of events
Artificial Intelligence
Temporal reasoning based on semi-intervals
Artificial Intelligence
Use of modular architectures for time series prediction
Neural Processing Letters
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Sequence Learning: From Recognition and Prediction to Sequential Decision Making
IEEE Intelligent Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
The Induction of Temporal Grammatical Rules from Multivariate Time Series
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
TAR: Temporal Association Rules on Evolving Numerical Attributes
Proceedings of the 17th International Conference on Data Engineering
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Methodology for long-term prediction of time series
Neurocomputing
Knowledge construction from time series data using a collaborative exploration system
Journal of Biomedical Informatics
Proceedings of the VLDB Endowment
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Computational Statistics & Data Analysis
Long-term prediction of time series by combining direct and MIMO strategies
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Prediction of multivariate responses with a selected number of principal components
Computational Statistics & Data Analysis
Probabilistic inductive logic programming
An efficient algorithm for a class of fused lasso problems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multistep-Ahead time series prediction
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Direct and recursive prediction of time series using mutual information selection
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Autonomous learning of sequential tasks: experiments and analyses
IEEE Transactions on Neural Networks
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The mining of temporal aspects over multi-dimensional data is increasingly critical for healthcare planning tasks. A healthcare planning task is, in essence, a classification problem over health-related attributes across temporal horizons. The increasingly integration of healthcare data through multi-dimensional structures triggers new opportunities for an adequate long-term planning of resources within and among clinical, pharmaceutical, laboratorial, insurance and e-health providers. However, the flexible nature and random occurrence of health records claim for the ability to deal with both structural attribute-multiplicity and arbitrarily-high temporal sparsity. For this purpose, two solutions using different structural mappings are proposed: an adapted multi-label classifier over denormalized tabular data and an adapted multiple time-point classifier over multivariate sparse time sequences. This work motivates the problem of long-term prediction in healthcare, and places key requirements and principles for its accurate and efficient solution.