Mary C Schuller
Biographic Data
| ID | 10213403 |
|---|---|
| NAME | Mary C Schuller |
| GIVEN NAMES | Mary C |
| FAMILY NAME | Schuller |
| SIGNATURE | SCHULLER M C |
| AFFILIATIONS | University of Michigan |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 1 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Using Natural Language Processing to Automatically Assess Feedback Quality
PURPOSE: Learning is markedly improved with high-quality feedback, yet assuring the quality of feedback is difficult to achieve at scale. Natural language processing (NLP) algorithms may be useful in this context as they can automatically classify large volumes of narrative data. However, it is unknown if NLP models can accurately evaluate surgical trainee feedback. This study evaluated which NLP techniques best classify the quality of surgical t…
No prominent works on this page.
Using Natural Language Processing to Automatically Assess Feedback Quality
PURPOSE: Learning is markedly improved with high-quality feedback, yet assuring the quality of feedback is difficult to achieve at scale. Natural language processing (NLP) algorithms may be useful in this context as they can automatically classify large volumes of narrative data. However, it is unknown if NLP models can accurately evaluate surgical trainee feedback. This study evaluated which NLP techniques best classify the quality of surgical t…
Artificial Intelligence (1 works) · Computer Science (1 works) · Diversity and Career in Medicine (1 works) · Formative assessment (1 works) · Innovations in Medical Education (1 works) · Logistic regression (1 works) · Machine learning (1 works) · Naive Bayes classifier (1 works) · Natural language processing (1 works) · Random forest (1 works)