Name
Abstract | ||
|---|---|---|
Life is filled with puzzles and mysteries, and we often fail to recognize the difference. As described by Gregory Treverton and Malcolm Gladwell, puzzles are solved by gathering and assimilating all relevant data in a logical, linear fashion, as in deciding which antibiotic to prescribe for an infection. In contrast, mysteries remain unsolved until all relevant data are analyzed and interpreted in a way that appreciates their depth and complexity, as in determining how to best modulate the host immune response to infection. When investigating mysteries, we often fail to appreciate their depth and complexity. Instead, we gather and assimilate more data, treating the mystery like a puzzle. This strategy is often unsuccessful. Traditional approaches to predictive analytics and phenotyping in surgery use this strategy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3389/frai.2019.00032 | Frontiers Artif. Intell. |
Keywords | DocType | Volume |
artificial intelligence,decision-making,informed consent,machine learning,phenotyping,prognostics,risk prediction,surgery | Journal | 2 |
ISSN | Citations | PageRank |
2624-8212 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tyler J. Loftus | 1 | 0 | 0.34 |
| Gilbert R. Upchurch Jr. | 2 | 0 | 0.34 |
| Daniel Delitto | 3 | 0 | 0.34 |
| Parisa Rashidi | 4 | 859 | 46.92 |
| Azra Bihorac | 5 | 50 | 8.63 |