# Efficient and Personalized Mobile Health Event Prediction via Small Language Models

arXiv: https://arxiv.org/abs/2409.18987 · HTML: https://mlsanity.com/paper/2409.18987/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases](https://mlsanity.com/paper/2406.10290/) (2024)
- [Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health](https://mlsanity.com/paper/2408.07313/) (2024)
- [Enabling On-Device LLMs Personalization with Smartphone Sensing](https://mlsanity.com/paper/2407.04418/) (2024)
- [UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers](https://mlsanity.com/paper/2402.09264/) (2024)
- [Leveraging LLMs to Predict Affective States via Smartphone Sensor Features](https://mlsanity.com/paper/2407.08240/) (2024)
- [UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction](https://mlsanity.com/paper/2307.16651/) (2023)

## Built on by (most cited first, 6 of 6)

- [Beyond Scale: Small Language Models are Comparable to GPT-4 in Mental Health Understanding](https://mlsanity.com/paper/2507.08031/) (2025)
- [ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue](https://mlsanity.com/paper/2601.20323/) (2026)
- [HealthSLM-Bench: Benchmarking Small Language Models for Mobile and Wearable Healthcare Monitoring](https://mlsanity.com/paper/2509.07260/) (2025)
- [Language modelling techniques for analysing the impact of human genetic variation](https://mlsanity.com/paper/2503.10655/) (2025)
- [SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies](https://mlsanity.com/paper/2607.25716/) (2026)
- [On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health](https://mlsanity.com/paper/2609.11961/) (2026)

## Abstract (arXiv)

Healthcare monitoring is crucial for early detection, timely intervention, and the ongoing management of health conditions, ultimately improving individuals' quality of life. Recent research shows that Large Language Models (LLMs) have demonstrated impressive performance in supporting healthcare tasks. However, existing LLM-based healthcare solutions typically rely on cloud-based systems, which raise privacy concerns and increase the risk of personal information leakage. As a result, there is growing interest in running these models locally on devices like mobile phones and wearables to protect users' privacy. Small Language Models (SLMs) are potential candidates to solve privacy and computational issues, as they are more efficient and better suited for local deployment. However, the performance of SLMs in healthcare domains has not yet been investigated. This paper examines the capability of SLMs to accurately analyze health data, such as steps, calories, sleep minutes, and other vital statistics, to assess an individual's health status. Our results show that, TinyLlama, which has 1.1 billion parameters, utilizes 4.31 GB memory, and has 0.48s latency, showing the best performance compared other four state-of-the-art (SOTA) SLMs on various healthcare applications. Our results indicate that SLMs could potentially be deployed on wearable or mobile devices for real-time health monitoring, providing a practical solution for efficient and privacy-preserving healthcare.