. Author manuscript; available in PMC: 2022 Mar 14.

Published in final edited form as: Proc ACM Interact Mob Wearable Ubiquitous Technol. 2021 Sep 14;5(3):102. doi: 10.1145/3478107

Table 3.

Performance of opioid administration detection model trained with different feature subsets and models from Leave-One-Subject-Out (LOSO) crossvalidation experiments. Performance was measured in terms of F1-score (weighted), specificity, sensitivity, and Area Under Curve (AUC). Channel-Temporal Attention TCN outperformed all the models.

Features	Model	F1-score	specificity	sensitivity	AUC
Demographical+ Time of the day	Baseline-Logistic	0.44 ± 0.26	0.50 ± 0.46	0.50 ± 0.45	0.48 ± 0.07
[53] ¹	Decision-Tree	0.57 ±0.14	0.70 ± 0.12	0.41 ±0.21	0.51 ±0.17
Physiological Statistical (PSTAT)	Logistic	0.64 ± 0.13	0.65± 0.14	0.48 ± 0.25	0.55± 0.17
Physiological Statistical (PSTAT)	BiLSTM	0.70 ±0.1	0.71 ± 0.2	0.57 ±0.3	0.7 ±0.14
Input signal	BiLSTM	0.66 ±0.1	0.63 ±0.15	0.63 ±0.20	0.62 ±0.09
Input signal	TCN	0.73 ±0.11	0.72 ± 0.14	0.74 ±0.18	0.74 ±0.11
Input signal	CTA-TCN	*0.80 ±0.1*	*0.77 ± 0.14*	*0.80 ±0.17*	*0.77 ±0.1*
Input signal	CNN-LSTM	0.72±0.11	0.65±0.17	0.82 ± 0.12	0.76±0.12
Input signal	LSTM-FCN	0.70±0.08	0.71±0.16	0.69 ± 0.14	0.72±0.15