Predicting dire outcomes of patients with community acquired pneumonia

By Gregory F. Cooper, Vijoy Abraham, Constantin F. Aliferis, J. M. Aronis, Bruce G. Buchanan, R. Caruana, M. Fine, J. Janosky, Gary Livingston, Tom Michael Mitchell. 2005

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Emergency Medicine, Epidemiology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. R. Caruana
2. Paul Koch
3. Yin Lou
4. Sarah Tan
5. Giles Hooker
6. Marzyeh Ghassemi
7. Johannes Gehrke
8. Cynthia Rudin

In Emergency Medicine

1. Marzyeh Ghassemi

In Epidemiology

1. Irene Y. Chen
2. Peter F. Schulam
3. Marzyeh Ghassemi
4. Tristan Naumann

Citations in the corpus

• Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission (2015, methodology): “In the study [3, 2], the most accurate models that could be trained were multitask neural nets.”
• A Review of Challenges and Opportunities in Machine Learning for Health. (2018, background): “To better understand this issue, consider prior work in which researchers found that asthmatic patients who were admitted to the hospital for pneumonia were more aggressively treated for the infection, lowering the subpopulation mortality rate 14 .”