Mhealth

Team

Mohmmad Arif Shaik

Graduate Teaching Assistant

Department of Information Technology

Kennesaw State University

mshaik@students.kennesaw.edu

Advisor

Dr. Nazmus Sakib

Assistant Professor

Department of Information Technology

Kennesaw State University

nsakib1@kennesaw.edu

Methodology - DSMA for Resilient Georgia

This study employs a mixed-methods approach combining synthetic ACE data analysis with stakeholder feedback to develop and evaluate the DSMA's AI-powered dashboard system for optimizing trauma-informed resource allocation and tracking intervention effectiveness.

Key Components

Data Collection: Synthetic RG datasets mirroring real-world distributions
Tools: Python, Dash, Plotly, SQLAlchemy, LangChain and OpenAI API
Analysis: Quantitative and Qualitative metrics on training data + survey reports
Output: Interactive dashboards with heatmaps, training effectiveness tracking and AI powered chat box for data insights
Evaluation: Measures both short-term and long-term systemic impact to remove intergenerational trauma

This is Workflow

Road Map

DSMA Pipeline

Features and Sub-features

Our Project

Transforming trauma-informed care through data science and interactive analytics to combat Adverse Childhood Experiences (ACEs).

🚀 Data Science Management Application (DSMA)

An AI-powered dashboard system for Resilient Georgia that visualizes ACE prevalence, tracks trauma-informed care adoption, and optimizes resource allocation through real-time analytics.

Key Features:

Interactive Heatmaps: Geographic visualization of ACE risk factors across Georgia
AI Risk Assessment: Personalized ACE scoring with intervention recommendations
Adoption Trackers: Sector-specific monitoring of trauma-informed practices
Natural Language Queries: OpenAI-powered insights for non-technical users

Python Dash/Plotly SQLAlchemy OpenAI API Lang Chain Trauma-Informed Care

Impact: Enabled data-driven policy decisions making to tackle intergenerational trauma

Data Science Application for Resilient Georgia