High-throughput analysis of multimodal monitoring data: the role of machine learning in early warning systems for high-risk neonates
Huiyi Huo, Yongxue Lu, Jinyu Zhou, Li Zhang, Jinjuan Pei, Chao Hu · 2026
Read High-throughput analysis of multimodal monitoring data: the role of machine learning in early warning systems for high-risk neonates by Huiyi Huo, Yongxue Lu, Jinyu Zhou, Li Zhang, Jinjuan Pei, Chao Hu (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13264878.txt.
Description
Huiyi Huo and co-authors review how machine learning can warn of decline in newborn care in a 2026 paper. Intensive care units for high risk babies pour out waves of monitor data in many forms at once. Old alarm systems that trip on fixed limits cry wolf too often and tire staff out.
The review maps the data types, from heart waveforms to brain signals and health records, and the snags they bring. It then walks through the learning tools used on such streams, from deep networks to fused multi source models. Work on sepsis, gut injury, and brain harm beat old methods, yet uptake still faces tech and ethical bars.
Catalog record
| CREATOR | Huiyi Huo, Yongxue Lu, Jinyu Zhou, Li Zhang, Jinjuan Pei, Chao Hu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |