feat: new article

Author: DiogoRibeiro7

_posts/-_ideas/2030-01-01-data_model_drift.md

@@ -13,9 +13,6 @@ tags: []
 
 ## Article Ideas on Data Drift and Model Drift
 
-### 6. **Data Drift vs. Concept Drift: Understanding the Differences and Implications**
-   - **Overview**: Differentiate between **data drift** (changes in the input data distribution) and **concept drift** (changes in the underlying relationships between inputs and outputs).
-   - **Focus**: Provide real-world examples to illustrate how each type of drift affects model performance and decision-making.
 
 ### 7. **Using Unsupervised Learning for Early Detection of Data Drift**
    - **Overview**: Explore how unsupervised learning techniques like **clustering** and **autoencoders** can detect anomalies in data that signal data drift.

_posts/-_ideas/2030-01-01-elderly_care.md

@@ -11,9 +11,6 @@ seo_type: article
 tags: []
 ---
 
-### 3. Improving Elderly Mental Health with Machine Learning and Data Analytics
-   - **Overview**: Discuss the role of data analytics and machine learning in understanding and treating mental health conditions like depression, anxiety, and dementia in the elderly.
-   - **Focus**: Use cases of AI-powered mood tracking and early detection of cognitive decline based on behavioral and health data.
 
 ### 4. Big Data in Geriatric Medicine: Enhancing Care for Aging Populations
    - **Overview**: Explain how big data analytics is being used to improve geriatric care by analyzing trends in elderly health, treatment outcomes, and care patterns.