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Postoperative Complications

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Machine learning algorithm predicts urethral stricture following transurethral prostate resection.

World journal of urology
PURPOSE: To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters.

Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgic...

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries.

BMC surgery
PURPOSE: The debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoper...

Machine learning prediction model for postoperative ileus following colorectal surgery.

ANZ journal of surgery
BACKGROUND: Postoperative ileus (POI) continues to be a major cause of morbidity following colorectal surgery. Despite best efforts, the incidence of POI in colorectal surgery remains high (~30%). This study aimed to investigate machine learning tech...

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)
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
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
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
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
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
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 ...