- Title Information
- Title
- Using Machine Learning to Predict Surgical Site Infection
- Name:
Personal
- Name Part
- Biron, Dustin
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Jain, Sukrit
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Stey, Paul
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Anand, Rajsavi
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Chen, Elizabeth S.
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Sarkar, Indra Neil
- Role
- Role Term:
Text
- creator
- Type of Resource
- text
- Genre (aat)
- posters
- Origin Information
- Date Created
(keyDate="yes", encoding="w3cdtf")
- 2017
- Language
- Language Term:
Code (ISO639-2B)
- eng
- Note
(displayLabel="Scholarly concentration")
- Biomedical Informatics
- Note
- All rights reserved
- Abstract
- Surgical site infection (SSI) is a rare, but serious complication for patients undergoing total joint replacements. We aimed to create a machine learning algorithm that could accurately diagnose infection in the 199 patients that had undergone total knee or hip replacements from the MIMIC-III database.1 After data preprocessing, processing, and analysis, we determined that the dataset was too small to produce a meaningful machine learning algorithm. However, we recognize that this study serves as an important framework for studying larger datasets with more patients.
- Subject (LCSH)
- Topic
- Machine learning
- Subject (Local)
- Topic
- Risk prediction