# A multicenter random forest model for effective prognosis prediction in collaborative clinical research network

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-multicenter-random-forest-model-for-effective-prognosis-prediction-in/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jin Li,Jin Li,Yu Tian,Yan Zhu,Tianshu Zhou,Jun Li,Jun Li,Kefeng Ding,Jingsong Li,Jingsong Li |
| Citations | 97 |
| DOI | 10.1016/j.artmed.2020.101814 |
| Fields | Computer Science |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3004491110 |
| PMID | 32143809 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Jingsong Li](https://scholariq.org/researchers/jingsong-li/)
- [Tianshu Zhou](https://scholariq.org/researchers/tianshu-zhou/)

## Paper journal

- [Artificial Intelligence in Medicine](https://scholariq.org/journals/artificial-intelligence-in-medicine/)

## Paper primary topic

- [Privacy-Preserving Technologies in Data](https://scholariq.org/topics/privacy-preserving-technologies-in-data/)

## Paper topics

- [Privacy-Preserving Technologies in Data](https://scholariq.org/topics/privacy-preserving-technologies-in-data/)
- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.