AIMC Topic: Retrospective Studies

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Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Journal of medical Internet research
BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a l...

Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination.

Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients.

International journal of medical sciences
Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications a...

Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

Skeletal radiology
OBJECTIVE: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians.

Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.

Journal of robotic surgery
Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compared to open esophagectomy. The precise role of robot-assisted laparoscopic esophagectomy (RALE) is still evolving. Single center retrospective analysi...

Weakly supervised deep learning for determining the prognostic value of F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type.

European journal of nuclear medicine and molecular imaging
PURPOSE: To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment F-FDG PET/CT resul...

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.

Computers in biology and medicine
BACKGROUND: Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent...

Intrahepatic cholestasis of pregnancy: machine-learning algorithm to predict elevated bile acid based on clinical and laboratory data.

Archives of gynecology and obstetrics
PURPOSE: Applying machine-learning models to clinical and laboratory features of women with intrahepatic cholestasis of pregnancy (ICP) and creating algorithm to identify these patients without bile acid measurements.

Primary vs redo robotic pyeloplasty: A comparison of outcomes.

Journal of pediatric urology
INTRODUCTION: Robot-assisted laparoscopic pyeloplasty (RALP) is a safe and efficacious option for repair of UPJO. We hypothesize that redo-RALP is technically more difficult but has comparable outcomes to primary RALP.

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.

PloS one
PURPOSE: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for referable thoracic abnormalities on chest X-ray (CXR) using a consecutively collected multicenter hea...