| Autor: |
Patle, Pallavi, Narad, Supriya, Dhawale, Chitra |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3188 Issue 1, p1-5, 5p |
| Abstrakt: |
Big data describes the vast and multifaceted data marts are being produced by a board spectrum of basis. These datasets can be used to deconstruct structure and sequence that would not be possible to detect with tiny data mart. This makes big data a valuable tool for suicide and self-harm prevention research. Suicide is a major public health problem, with over 45,000 suicides occurring in the United States each year. Factors contributing to suicide risk include mental illness, substance abuse, social isolation, and access to lethal means. Big data is widely used to identify these risk factors and to develop targeted interventions to reduce suicide risk. Big data analysis uses advanced data driven and deep learning methods to derive meaningful pattern of big data and complex datasets. This approach has the potential to transform suicide prevention by providing new insights into the factors that contribute to suicide risk. Big data analysis is also used to analyses large datasets of healthcare data, social media data, and wearable sensor data. The evidence can be used to recognize form and current that may indicate suicide risk. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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