AI Predicting Sick Leave: Innovative Practice from the Eindhoven Artificial Intelligence Systems Institute

The team from the Eindhoven Artificial Intelligence Systems Institute (EAISI) explores using machine learning to predict employee sick leave, providing data-driven decision support for human resource management. This article introduces the project's background, technical architecture, application scenarios, challenges, and future directions, demonstrating the application potential of AI in human resource analysis.

EAISI is an academic institution under Eindhoven University of Technology in the Netherlands, focusing on AI research and bringing together researchers from multiple fields to promote the practical application of AI. Employee sick leave management affects enterprise productivity; traditional methods rely on experience-based judgments and lack forward-looking insights, while machine learning brings new possibilities to this field.

Data Feature Engineering: Key factors include historical sick leave records, demographic characteristics, work environment factors, time factors, and organizational factors.

Model Selection Considerations: Classification models (identifying high-risk groups), regression models (predicting days/frequency), time series models (macro trends), and survival analysis models (risk evolution).

• Human Resource Planning: Predict sick leave peaks and allocate staff in advance to reduce business impact.
• Health Management Intervention: Provide health benefits to high-risk groups to reduce sick leave rates.
• Work Environment Optimization: Analyze the correlation between sick leave and environment to identify problem areas.
• Cost Control: Precisely budget human resource costs and support insurance decisions.

• Data Quality and Availability: Data is scattered, records are incomplete, and medical data privacy compliance requirements are strict.
• Model Fairness: Need to avoid bias against specific groups and ensure fairness and transparency.
• Privacy Balance: Use technologies like differential privacy and federated learning to protect privacy.
• Human-Machine Collaboration: AI assists rather than replaces human judgment; HR needs to understand the model's limitations.

EAISI's research reflects the integration of academic and industrial needs: universities provide AI technologies and methods, while enterprises provide scenarios and data, promoting academic applications and corporate innovation. Students participating in such projects can translate theory into practice and address challenges like the complexity of real-world data.

1. Multimodal Data Fusion: Integrate wearable device and environmental sensor data to build health profiles.
2. Real-Time Prediction Capability: Shift from post-hoc analysis to real-time monitoring and early warning.
3. Personalized Intervention Recommendations: Predict risks and recommend preventive measures.
4. Cross-Organization Learning: Use multi-party data under privacy protection to improve model generalization ability.

The EAISI project demonstrates the application prospects of AI in HR management; data-driven approaches can optimize human resource allocation. Ethical considerations must be emphasized in technology application to protect employee rights, fairness, and justice. Such projects provide practical opportunities for students, reminding us that the value of technology lies in solving real problems and improving life and work experiences.