Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.
Martin WagnerJohanna M BrandenburgSebastian BodenstedtAndré SchulzeAlexander C JenkeAntonia SternMarie T J DaumLars MündermannFiona R KolbingerNithya BhaskerGerd SchneiderGrit Krause-JüttlerHisham AlwanniFleur Fritz-KebedeOliver BurgertDirk WilhelmJohannes FallertFelix NickelLena Maier-HeinMartin DugasMarius DistlerJürgen WeitzBeat-Peter Müller-StichStefanie SpeidelPublished in: Surgical endoscopy (2022)
Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
Keyphrases
- electronic health record
- patients undergoing
- big data
- minimally invasive
- risk factors
- coronary artery bypass
- pain management
- coronary artery disease
- cardiovascular disease
- magnetic resonance imaging
- magnetic resonance
- squamous cell carcinoma
- machine learning
- quality improvement
- artificial intelligence
- lymph node metastasis
- atrial fibrillation
- percutaneous coronary intervention