AIMC Topic: Postoperative Complications
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Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.
International journal of surgery (London, England)
May 1, 2024
BACKGROUND: Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery us...
Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.
The journal of obstetrics and gynaecology research
Apr 30, 2024
AIM: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecological malignant tumor patients, and to explore the value of machine learning (ML) models in VTE occurren...
Personalizing patient risk of a life-altering event: An application of machine learning to hemiarch surgery.
The Journal of thoracic and cardiovascular surgery
Apr 27, 2024
OBJECTIVE: The study objective was to assess a machine learning model's ability to predict the occurrence of life-altering events in hemiarch surgery and determine contributing patient characteristics and intraoperative factors.
Development and validation of a machine learning model to predict postoperative delirium using a nationwide database: A retrospective, observational study.
Journal of clinical anesthesia
Apr 27, 2024
STUDY OBJECTIVE: Postoperative delirium is a neuropsychological syndrome that typically occurs in surgical patients. Its onset can lead to prolonged hospitalization as well as increased morbidity and mortality. Therefore, it is important to promptly ...
The use of artificial intelligence in reconstructive surgery for head and neck cancer: a systematic review.
European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
Apr 25, 2024
OBJECTIVES: The popularity of artificial intelligence (AI) in head and neck cancer (HNC) management is increasing, but postoperative complications remain prevalent and are the main factor that impact prognosis after surgery. Hence, recent studies aim...
An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.
HPB : the official journal of the International Hepato Pancreato Biliary Association
Apr 23, 2024
BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. This retrospective study aimed to create a model predicting AKI through machine learning and identify ...
Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.
BMC medical informatics and decision making
Apr 22, 2024
OBJECTIVES: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of N...
Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.
British journal of anaesthesia
Apr 18, 2024
BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that ...
The usefulness of artificial intelligence in breast reconstruction: a systematic review.
Breast cancer (Tokyo, Japan)
Apr 15, 2024
BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to p...
Deep learning-based radiomics of computed tomography angiography to predict adverse events after initial endovascular repair for acute uncomplicated Stanford type B aortic dissection.
European journal of radiology
Apr 15, 2024
PURPOSE: This study aimed to construct a predictive model integrating deep learning-derived radiomic features from computed tomography angiography (CTA) and clinical biomarkers to forecast postoperative adverse events (AEs) in patients with acute unc...
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