![](https://dblp.org/img/logo.ua.320x120.png)
![](https://dblp.org/img/dropdown.dark.16x16.png)
![](https://dblp.org/img/peace.dark.16x16.png)
Остановите войну!
for scientists:
![search dblp search dblp](https://dblp.org/img/search.dark.16x16.png)
![search dblp](https://dblp.org/img/search.dark.16x16.png)
default search action
"Mining Themes in Clinical Notes to Identify Phenotypes and to Predict ..."
Ankita Agarwal et al. (2023)
- Ankita Agarwal
, Tanvi Banerjee, William L. Romine, Krishnaprasad Thirunarayan, Lingwei Chen, Mia Cajita:
Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure. ICDH 2023: 208-216
![](https://dblp.org/img/cog.dark.24x24.png)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.