Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study
Ah Ra Lee, Hojoon Park, Aram Yoo, Seok Kim, Leonard Sunwoo, Sooyoung Yoo · 2023
Read Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study by Ah Ra Lee, Hojoon Park, Aram Yoo, Seok Kim, Leonard Sunwoo, Sooyoung Yoo (2023) 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-10733827.txt.
Description
Ah Ra Lee and co-authors build a machine learning model that predicts which lung cancer patients will land in the emergency department. Their 2023 study draws on records from a large South Korean hospital for patients diagnosed between 2010 and 2017. Lung cancer patients visit emergency care often, and their outlook after such visits is poor.
The model aims to flag a visit to emergency care within thirty days of a routine clinic visit. Since these patients return often for treatment and checks, their routine data offer a fair chance to spot risk early. The authors say such warnings could help hospitals plan resources and step in before a crisis hits.
Catalog record
| CREATOR | Ah Ra Lee, Hojoon Park, Aram Yoo, Seok Kim, Leonard Sunwoo, Sooyoung Yoo |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |