Accurate and Interpretable Machine Learning for Prediction and Precision Medicine — NSF Award to Emory University (GA, $219,995)
Many machine learning technologies are built using black-box approaches, which can make it difficult to scrutinize the technology's decision-making process. This lack of interpretability represents a fundamental barrier to the adoption of machine learning technologies in some areas, such as health care, where transpare
| Award title | Accurate and Interpretable Machine Learning for Prediction and Precision Medicine |
|---|---|
| Award ID | 2015540 |
| Awardee | Emory University |
| City | ATLANTA |
| State | GA |
| Amount obligated | $219,995 |
| Principal investigator | David Benkeser |
| Program | STATISTICS |
| Start date | 09/01/2020 |
| Abstract | Many machine learning technologies are built using black-box approaches, which can make it difficult to scrutinize the technology's decision-making process. This lack of interpretability represents a fundamental barrier to the adoption of machine learning technologies in some areas, such as health care, where transparency is key. Researchers have long relied on decision trees as a means of interpretable machine learning. In this approach, one develops a series of yes/no questions that eventually |
| Source | NSF Awards |
$799/mo
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