---
title: Investigating on the Recent Synergies of IoT and Machine Learning for Proactive Healthcare Systems
url: https://www.ml-quant.com/papers/ssrn/4798953/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4798953
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4798953
featured: 2024-04-24
citations: 0
topic: ML & AI Methods
---


# Investigating on the Recent Synergies of IoT and Machine Learning for Proactive Healthcare Systems

The article explores the role of IoT and Machine Learning in transforming healthcare management, enabling early disease detection and improved patient care.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4798953
- Identifier: SSRN 4798953
- Released: 2023-06-09
- First featured: Quant Letter No. 46 (2024-04-24): https://www.ml-quant.com/issues/2024-04-24/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
- [Octo: An Open-Source Generalist Robot Policy](https://www.ml-quant.com/papers/arxiv/2405.12213/): Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.
- [Mastering Diverse Domains through World Models](https://www.ml-quant.com/papers/arxiv/2301.04104/): Algorithm Mastery: DreamerV3, a universal algorithm, excels in over 150 varied tasks, including diamond collection in Minecraft without human input, expanding the scope of reinforcement learning.
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://www.ml-quant.com/papers/arxiv/2404.02905/): The article discusses Visual AutoRegressive modeling (VAR), a new image learning method that outperforms diffusion transformers in terms of speed, image quality, and scalability.
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://www.ml-quant.com/papers/arxiv/2405.14734/): Simple Preference Optimization: SimPO improves reinforcement learning from human feedback by using the average log probability of a sequence as the implicit reward, enhancing training stability and computational efficiency.