Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): An external validation study.

Background: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.

Objective: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research.

Predicting need for hospital-specific interventional care after surgery using electronic health record data.

Background: A significant proportion of surgical inpatients is often admitted longer than necessary. Early identification of patients who do not need care that is strictly provided within hospitals would allow timely discharge of patients to a postoperative nursing home for further recovery. We aimed to develop a model to predict whether a patient needs hospital-specific interventional care beyond the second postoperative day.

[Osteoarticular infections in patients with sickle cell disease in Lubumbashi: epidemiological study focusing on etiology and management].

Introduction: infections are the leading cause of morbidity and mortality in patients with sickle cell disease, especially before age 5 years. The purpose of this study was to highlight the epidemiological features, etiologies and management of osteoarticular infections in patients with sickle cell disease in Lubumbashi.

Methods: we conducted a descriptive, cross-sectional and retrospective study at the Research Center for Sickle Cell Disease in Lubumbashi (RCSCDL) over a three-year period from June 2014 to June 2017.