SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Appendix H Broader Impacts
Our approach for self-supervised contrastive learning improves classification performance on target datasets in patient-independent medical diagnosis scenarios. Leveraging different data consistency levels in medical time series is crucial to enable effective and accurate contrastive learning without sufficient labels. Our work will encourage the research community to discover universal frameworks for other practical applications based on time series representation learning. We also hope our work can attract more researchers to the more general problem of hierarchical consistency from other related fields.
From the societal perspective, our work and the line of contrastive learning can promote more efficient use of medical time series with the lack of labels. Specifically, our model has the potential to identify patterns and anomalies that may not be immediately apparent to human experts. This could lead to earlier and more accurate diagnoses, improving patient outcomes and reducing healthcare costs. However, practitioners need to be aware of the limitations of the model.
Table 9: Heavy Duty Baselines. Run more epochs or add more contrastive blocks to SimCLR and TS2vec on the AD dataset.
Fraction	Models	Accuracy	Precision	Recall	F1 score	AUROC	AUPRC
100%	4E-SimCLR	56.87±2.51	57.67±4.02	53.31±2.30	48.28±4.63	57.67±4.02	51.97±1.44
4B-SimCLR	53.92±3.81	51.88±3.25	51.26±3.05	46.92±5.07	51.88±3.25	50.75±1.69
SimCLR	54.77±1.97	50.15±7.02	50.58±1.92	43.18±4.27	50.15±7.02	50.42±1.06
2E-TS2vec	76.49±6.10	78.97±3.54	77.69±5.16	76.21±6.45	88.44±2.40	88.12±2.53
2B-TS2vec	81.61±1.65	81.47±1.75	81.53±1.68	81.43±1.65	89.50±1.60	89.22±1.75
TS2vec	81.26±2.08	81.21±2.14	81.34±2.04	81.12±2.06	89.20±1.76	88.94±1.85
COMET (Ours)	84.50±4.46	88.31±2.42	82.95±5.39	83.33±5.15	94.44±2.37	94.43±2.48
10%	4E-SimCLR	57.97±1.74	58.41±8.31	53.69±2.25	46.47±5.71	58.41±8.31	52.33±1.38
4B-SimCLR	53.57±6.29	53.89±5.05	52.97±4.22	50.60±5.46	53.89±5.05	51.80±2.34
SimCLR	56.09±2.25	53.81±5.74	51.73±2.59	44.10±4.84	53.81±5.74	51.08±1.53
2E-TS2vec	66.29±7.86	69.92±5.13	67.79±6.44	65.36±8.55	78.54±4.98	77.95±5.29
2B-TS2vec	72.61±4.46	73.86±4.36	72.98±3.66	72.30±4.24	81.91±4.83	81.74±4.85
TS2vec	73.28±4.34	74.14±4.33	73.52±3.77	73.00±4.18	81.66±5.20	81.58±5.11
COMET (Ours)	91.43±3.12	92.52±2.36	90.71±3.56	91.14±3.31	96.44±2.84	96.48±2.82
1%	4E-SimCLR	58.07±1.93	57.72±3.50	54.92±1.99	51.93±3.11	57.72±3.50	52.91±1.28
4B-SimCLR	54.67±5.43	54.86±4.94	54.48±4.64	53.68±4.89	54.86±4.94	52.67±2.68
SimCLR	55.42±2.43	52.18±5.55	51.37±2.76	45.02±4.79	52.18±5.55	50.87±1.45
2E-TS2vec	63.56±4.62	64.97±3.53	64.49±3.90	63.28±4.69	70.26±3.55	68.77±3.59
2B-TS2vec	64.18±4.53	64.26±4.80	64.26±4.80	63.93±4.61	70.07±5.97	68.62±6.25
TS2vec	64.93±3.53	65.28±3.52	65.14±3.59	64.64±3.58	70.56±5.38	68.97±5.75
COMET (Ours)	88.22±3.36	88.55±2.73	88.56±3.14	88.14±3.37	96.05±1.36	96.12±1.31
All datasets in this paper are publicly available and are not associated with privacy or security concerns. Furthermore, we have followed guidelines on responsible use specified by the primary authors of the datasets used in the current work.
Appendix References
[S1] Cosimo Ieracitano, Nadia Mammone, Alessia Bramanti, Amir Hussain, and Francesco C Morabito. A convolutional neural network approach for classification of dementia stages based on 2d-spectral representation of EEG recordings. Neurocomputing, 323:96-107, 2019.
[S2] Ling Yang and Shenda Hong. Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion. In International Conference on Machine Learning, pages 25038-25054. PMLR, 2022.
[S3] Dennis Bäßler, Tobias Kortus, and Gabriele Gühring. Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks. Machine Learning, 111(4):1377-1408, 2022.