This article is primarily based on our 2020 publication featured in the prestigious journal PLoS ONE. In this case, by amalgamating data analytics with health sciences, we directed our focus towards mental health. We resolved to explore which fundamental factors most powerfully determine susceptibility to anxiety among young men confronting severe stress (in this specific context, the threat of armed conflict).
Methodology: Why Did We Use Artificial Intelligence?
Instead of heavily relying on traditional statistical models, we opted to harness Machine Learning algorithms. Why? Because the human psyche and lifestyle comprise thousands of interlinked, hidden variables. AI algorithms are highly capable of perceiving obscure patterns and dependencies spanning immense psychosociological datasets that a human eye might completely overlook.
What Did We Investigate?
Our analysis enveloped a broad spectrum of psychosociological data, including habits, overall physical activity, and baseline health state, to architect a precise predictive model:
- The machine learning model permitted the extraction of key health determinants which predispose a specific individual to be significantly more vulnerable to acute distress.
- The study fundamentally proved that psychological and physical health are inextricably linked – maintaining proper lifestyle habits acts as an impermeable buffer shielding the mind against excessive anxiety.
What Does This Mean for Therapeutic Practice?
Applying Data Science and machine learning algorithms within public health frameworks is absolutely the future of prophylaxis. Thanks to such models, modern specialists, including therapists and physiotherapists, can much earlier pinpoint individuals in high-risk categories and implement suitable preventative support networks (both psychological and biomechanical movement-related) long before baseline stress morphs into profound medical issues.
Pavlova I., Zikrach D., Mosler D., Ortenburger D., Góra T., Wąsik J. (2020). Determinants of anxiety levels among young males in a threat of experiencing military conflict—Applying a machine-learning algorithm in a psychosociological study. PLoS ONE, 15(10): e0239749.
