Robust Bayesian cumulative probit linear mixed models for longitudinal ordinal data

Lee, Kuo-Jung; Chen, Ray-Bing; Lee, Keunbaik

doi:10.1007/s00180-024-01499-w

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2024

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Data-Driven Analytics for Sustainable Mental Healthcare

Sundaram

2024

Artificial Intelligence Transformations for Healthcare Applications

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The integration of data-driven analytics and mental healthcare has emerged as a promising frontier in the contemporary landscape of healthcare. Mental health disorders constitute a significant global burden, affecting millions of individuals across diverse demographics. However, traditional approaches to mental healthcare grapple with challenges such as resource constraints, stigma, etc. The integration of data-driven analytics into mental health strategies promises to address these challenges by offering personalized, evidence-based interventions, optimizing resource allocation, and promoting preventive measures. This chapter navigates through the landscape of sustainable mental healthcare, shedding light on how data analytics can contribute to early detection, personalized treatment plans, and continuous monitoring of mental health conditions. Moreover, the chapter delves into the ethical considerations surrounding the use of data in mental healthcare, exploring the delicate balance between privacy concerns and the imperative to harness the potential of data for the greater good.

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