Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
Abstract Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280–360 nm, maximum 320 nm), or narrow-ban...
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| Autores principales: | , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1dc2e0508aca434089c9b5e53676e277 |
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| Sumario: | Abstract Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280–360 nm, maximum 320 nm), or narrow-band UV B irradiation (main emission spectrum 310–315 nm, maximum 311 nm). For patients who cannot access phototherapy centers, sunbathing, or heliotherapy, can be a safe and effective treatment alternative. However, as sunlight contains the full range of UV radiation (290–400 nm), careful sunbathing supervised by photodermatologist based on accurate UV radiation forecast is vital to minimize potential adverse effects. Here, using 10-year UV radiation data collected at Nakhon Pathom, Thailand, we developed a deep learning model for UV radiation prediction which achieves around 10% error for 24-h forecast and 13–16% error for 7-day up to 4-week forecast. Our approach can be extended to UV data from different geographical regions as well as various biological action spectra. This will become one of the key tools for developing national heliotherapy protocol in Thailand. Our model has been made available at https://github.com/cmb-chula/SurfUVNet . |
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