Temporal pattern mining for multivariate clinical decision support.
Multivariate temporal data are collections of contiguous data values that reflect complex temporal changes over a given duration. Technological advances have resulted in significant amounts of such data in high-throughput disciplines, including EEG and iEEG data for effective and efficient healthcare informatics, and decision support. Most data analytics and data-mining algorithms are effective in capturing global trends, but fail to capture localized behavioral changes in large temporal data sets. We present a two-step algorithmic methodology to uncover temporal patterns and exploiting them for an efficient and accurate decision support system. This methodology aids the discovery of previously unknown, nontrivial, and potentially useful temporal patterns for enhanced patient-specific clinical decision support with high degrees of sensitivity and specificity. Classification results on multivariate time series iEEG data for epileptic seizure detection also demonstrate the efficacy and accuracy of the technique to uncover interesting and effective domain class-specific temporal patterns.
Data Mining Research Laboratory, Computer Science, Louisiana Tech University, Ruston, LA, USA.
Sheetal
S
Saini
Sumeet
S
Dua
D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors