| Title: | Which causes of death are predictable? A machine learning analysis |
| Lead Institution: | University of Sao Paulo |
| Principal investigator: | Professor Alexandre Chiavegatto Filho |
Application 30140
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About
Being able to predict which individuals are more likely to die is one of medicine?s most recurring challenges. The recent expansion of new machine learning algorithms is a promising way to predict mortality, especially in studies with a wide variety of individual characteristics such as is the case of the UK Biobank. Our analyzes will be able to identify: - Which of the main causes of death can be predicted with higher accuracy, using all baseline characteristics. - Which machine learning algorithms have a higher predictive accuracy for each of the main causes of death. The results of the study may be applied directly by healthcare services to help improve decisions about patient hospitalization and the intensity of care provided for high-risk patients. Our study will test the performance of a few dozen of the most popular machine learning algorithms to predict all-cause mortality, and mortality for each of the most frequent causes of death, using all the baseline characteristic of the individuals. We plan to include the full cohort in our analysis.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12934 | Modifiable risk factors and excess mortality in depression: Data from the UK Biobank | Vivian Boschesi Barros (+1) | 2024 | General Hospital Psychiatry |
Enabling scientific discoveries that improve human health