# Design of an Edge-based Portable EHR System for Anemia Screening in Remote Health Applications

arXiv: https://arxiv.org/abs/2507.15146 · HTML: https://mlsanity.com/paper/2507.15146/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Edge AI: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2407.04053/) (2024)
- [Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges](https://mlsanity.com/paper/2004.07311/) (2020)
- [MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices](https://mlsanity.com/paper/2403.00830/) (2024)
- [BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain](https://mlsanity.com/paper/2109.14295/) (2021)
- [Improved Healthcare Access in Low-resource Regions: A Review of Technological Solutions](https://mlsanity.com/paper/2205.10913/) (2022)
- [I-Health: Leveraging Edge Computing and Blockchain for Epidemic Management](https://mlsanity.com/paper/2012.14294/) (2020)
- [Harnessing the Digital Revolution: A Comprehensive Review of mHealth Applications for Remote Monitoring in Transforming Healthcare Delivery](https://mlsanity.com/paper/2408.14190/) (2024)
- [Integration of Remote Patient Monitoring Systems into Physicians Work in Underserved Communities: Survey of Healthcare Provider Perspectives](https://mlsanity.com/paper/2207.01489/) (2022)
- [IoT-Enabled Low-Cost Fog Computing System with Online Machine Learning for Accurate and Low-Latency Heart Monitoring in Rural Healthcare Settings](https://mlsanity.com/paper/2302.14131/) (2023)

## Abstract (arXiv)

The design of medical systems for remote, resource-limited environments faces persistent challenges due to poor interoperability, lack of offline support, and dependency on costly infrastructure. Many existing digital health solutions neglect these constraints, limiting their effectiveness for frontline health workers in underserved regions. This paper presents a portable, edge-enabled Electronic Health Record platform optimized for offline-first operation, secure patient data management, and modular diagnostic integration. Running on small-form factor embedded devices, it provides AES-256 encrypted local storage with optional cloud synchronization for interoperability. As a use case, we integrated a non-invasive anemia screening module leveraging fingernail pallor analysis. Trained on 250 patient cases (27% anemia prevalence) with KDE-balanced data, the Random Forest model achieved a test RMSE of 1.969 g/dL and MAE of 1.490 g/dL. A severity-based model reached 79.2% sensitivity. To optimize performance, a YOLOv8n-based nail bed detector was quantized to INT8, reducing inference latency from 46.96 ms to 21.50 ms while maintaining mAP@0.5 at 0.995. The system emphasizes low-cost deployment, modularity, and data privacy compliance (HIPAA/GDPR), addressing critical barriers to digital health adoption in disconnected settings. Our work demonstrates a scalable approach to enhance portable health information systems and support frontline healthcare in underserved regions.