# Machine learning analyses of automated performance metrics during granular sub-stitch phases predict surgeon experience

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/machine-learning-analyses-of-automated-performance-metrics-during-granular-sub/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Andrew B. Chen,Siqi Liang,Jessica H. Nguyen,Yan Liu,Andrew J. Hung |
| Citations | 43 |
| DOI | 10.1016/j.surg.2020.09.020 |
| Fields | Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://www.ncbi.nlm.nih.gov/pmc/articles/8093318 |
| OpenAlex ID | https://openalex.org/W3095377037 |
| PMID | 33160637 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Jessica H. Nguyen](https://scholariq.org/researchers/jessica-h-nguyen/)

## Paper journal

- [Surgery](https://scholariq.org/journals/surgery/)

## Paper primary topic

- [Surgical Simulation and Training](https://scholariq.org/topics/surgical-simulation-and-training/)

## Paper topics

- [Surgical Simulation and Training](https://scholariq.org/topics/surgical-simulation-and-training/)
- [Cardiac, Anesthesia and Surgical Outcomes](https://scholariq.org/topics/cardiac-anesthesia-and-surgical-outcomes/)
- [Anorectal Disease Treatments and Outcomes](https://scholariq.org/topics/anorectal-disease-treatments-and-outcomes/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.