  We extended dynamic time warping (DTW) into interval-based dynamic time
warping (iDTW), including (A) interval-based representation (iRep): [1]
abstracting raw, time-stamped data into interval-based abstractions, [2]
comparison-period scoping, [3] partitioning abstract intervals into a given
temporal granularity; (B) interval-based matching (iMatch): matching
partitioned, abstract-concepts records, using a modified DTW. Using domain
knowledge, we abstracted the raw data of medical records, for up to three
concepts out of four or five relevant concepts, into two interval types: State
abstractions (e.g. LOW, HIGH) and Gradient abstractions (e.g. INCREASING,
DECREASING). We created all uni-dimensional (State or Gradient) or
multi-dimensional (State and Gradient) abstraction combinations. Tasks:
Classifying 161 oncology patients records as autologous or allogenic
bone-marrow transplantation; classifying 125 hepatitis patients records as B or
C hepatitis; predicting micro- or macro-albuminuria in the next year for 151
Type 2 diabetes patients. We used a k-Nearest-Neighbors majority, k = an odd
number from 1 to SQRT(N), N = set size. 75,936 10-fold cross-validation
experiments were performed: 33,600 (Oncology), 28,800 (Hepatitis), 13,536
(Diabetes). Measures: Area Under the Curve (AUC), optimal Youden's Index.
Paired t-tests compared result vectors for equivalent configurations other than
a tested variable, to determine a significant mean accuracy difference
(P<0.05). Mean classification and prediction using abstractions was
significantly better than using only raw time-stamped data. In each domain, at
least one abstraction combination led to a significantly better mean
performance than raw data. Increasing feature number and using
Multi-dimensional abstractions enhanced performance. Unlike when using raw
data, optimal mean performance was often reached with k=5, using abstractions.
