A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations
Chen Liang, Xianfeng Meng, Ying Duan, Wei Yang, Gang Zhu, Jinhuan Wang, Ying Sun, Mingtao Wang, Miao Liu, Chenhui Sun, Kunyuan Hu, Wei Shao, Jintao Ren, Xiaojun Shao, Yang Zhang · 2026
Read A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations by Chen Liang, Xianfeng Meng, Ying Duan, Wei Yang, Gang Zhu, Jinhuan Wang, Ying Sun, Mingtao Wang, Miao Liu, Chenhui Sun, Kunyuan Hu, Wei Shao, Jintao Ren, Xiaojun Shao, Yang Zhang (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-13219302.txt.
Description
Chen Liang, Xianfeng Meng, Ying Duan, and a wider team wrote this research paper, published in 2026. It builds a two-layer machine learning model to flag hospital patients with schizophrenia who are at high risk of harming themselves.
The team reviewed records of 477 patients admitted in Liaoning Province over three years. About a third had a documented self-harm or suicide episode during their stay. The model's first layer uses facts known at admission, such as past self-harm and hopelessness. The second layer watches weekly behavior scores recorded by nurses. Six algorithms were trained on each layer, and the best of each were blended. The fused model is meant to catch risk that builds during the stay, not just risk present at the door. The authors aim it at earlier intervention on the ward.
Catalog record
| CREATOR | Chen Liang, Xianfeng Meng, Ying Duan, Wei Yang, Gang Zhu, Jinhuan Wang, Ying Sun, Mingtao Wang, Miao Liu, Chenhui Sun, Kunyuan Hu, Wei Shao, Jintao Ren, Xiaojun Shao, Yang Zhang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 87 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |