Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics. We state the complexities posed by real-world variability, disease dynamics, user-specific characteristics, and the prevalence of false notifications, necessitating novel evaluation strategies. Drawing insights from large-scale heart studies, the paper offers a comprehensive guideline for robust ML model evaluation on continuous health monitoring..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
arXiv.org - (2023) vom: 04. Dez. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ding, Cheng [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| PPN (Katalog-ID): |
XAR041761022 |
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| 520 | |a This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics. We state the complexities posed by real-world variability, disease dynamics, user-specific characteristics, and the prevalence of false notifications, necessitating novel evaluation strategies. Drawing insights from large-scale heart studies, the paper offers a comprehensive guideline for robust ML model evaluation on continuous health monitoring. | ||
| 650 | 4 | |a Computer Science - Machine Learning |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Electrical Engineering and Systems Science - Signal Processing |7 (dpeaa)DE-84 | |
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| 700 | 1 | |a Guo, Zhicheng |4 aut | |
| 700 | 1 | |a Rudin, Cynthia |4 aut | |
| 700 | 1 | |a Xiao, Ran |4 aut | |
| 700 | 1 | |a Nahab, Fadi B |4 aut | |
| 700 | 1 | |a Hu, Xiao |4 aut | |
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