Prediction of complications and prognostication in perioperative medicine: a systematic review and PROBAST assessment of machine learning tools
P Arina, MR Kaczorek, DA Hofmaenner… - …, 2024 - pubs.asahq.org
Background The utilization of artificial intelligence and machine learning as diagnostic and
predictive tools in perioperative medicine holds great promise. Indeed, many studies have …
predictive tools in perioperative medicine holds great promise. Indeed, many studies have …
Artificial intelligence in surgery
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery
remain relatively nascent. Here we review the integration of AI in the field of surgery …
remain relatively nascent. Here we review the integration of AI in the field of surgery …
A gender specific risk assessment of coronary heart disease based on physical examination data
H Yang, YM Luo, CY Ma, TY Zhang, T Zhou… - NPJ digital …, 2023 - nature.com
Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its
prevention and management. Physical examination data has the advantages of wide …
prevention and management. Physical examination data has the advantages of wide …
Development and validation of a personalized model with transfer learning for acute kidney injury risk estimation using electronic health records
K Liu, X Zhang, W Chen, SL Alan, JA Kellum… - JAMA Network …, 2022 - jamanetwork.com
Importance Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among
hospitalized patients. Personalized risk estimation and risk factor identification may allow …
hospitalized patients. Personalized risk estimation and risk factor identification may allow …
Application of machine learning models to predict recurrence after surgical resection of nonmetastatic renal cell carcinoma
Background Predictive tools can be useful for adapting surveillance or including patients in
adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current …
adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current …
Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning
Objective Open surgical treatment options for aortoiliac occlusive disease carry significant
perioperative risks; however, outcome prediction tools remain limited. Using machine …
perioperative risks; however, outcome prediction tools remain limited. Using machine …
Predicting outcomes following endovascular abdominal aortic aneurysm repair using machine learning
Objective: To develop machine learning (ML) models that predict outcomes following
endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Background …
endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Background …
Machine learning applications in healthcare: The state of knowledge and future directions
Detection of easily missed hidden patterns with fast processing power makes machine
learning (ML) indispensable to today's healthcare system. Though many ML applications …
learning (ML) indispensable to today's healthcare system. Though many ML applications …
Predicting major adverse cardiovascular events following carotid endarterectomy using machine learning
Background Carotid endarterectomy (CEA) is a major vascular operation for stroke
prevention that carries significant perioperative risks; however, outcome prediction tools …
prevention that carries significant perioperative risks; however, outcome prediction tools …
[HTML][HTML] Optimizing discharge after major surgery using an artificial intelligence–based decision support tool (DESIRE): An external validation study
D van de Sande, ME van Genderen, C Verhoef… - Surgery, 2022 - Elsevier
Background In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we
have previously developed and validated a machine learning concept in 1,677 …
have previously developed and validated a machine learning concept in 1,677 …