Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning.

Area of Science:

• Medical Informatics
• Machine Learning Applications
• Social Network Analysis

Background:

• Aberrant patient profiles in medical social networks pose challenges for clinical recommendation systems.
• These profiles can be exploited for malicious activities like fake remarks, cyber bullying, and cyber-attacks.
• Detecting and mitigating these anomalies is an ongoing area of research.

Purpose of the Study:

• To summarize recent studies on detecting aberrant patient profiles in medical social networks.
• To provide an overarching framework for understanding methods and datasets used in this domain.
• To highlight the role of machine learning in medical data analysis and disease categorization.

Main Methods:

• Review and summarization of recent research on aberrant patient profile detection.
• Presentation of a framework encompassing feature engineering, algorithm selection, and data collection strategies.
• Discussion of various machine learning algorithms, data labeling techniques, and assessment criteria relevant to medical data.

Main Results:

• Machine learning offers advantages in categorizing and evaluating diseases, improving predictability and control.
• Challenges exist in information training and validation due to large datasets, making error elimination difficult.
• The detection of anomalous users in medical social networks remains an active area of development.

Conclusions:

• The study provides a comprehensive overview of methods and evaluation criteria for detecting anomalies in medical social networks.
• Further research is needed to advance the detection of aberrant patient profiles and enhance clinical recommendation systems.
• Machine learning holds significant potential for improving healthcare outcomes through accurate data analysis and disease prediction.