AIMC Topic: Retrospective Studies

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Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes.

Studies in health technology and informatics
Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can help determine which patients are at greater risk of hospital readmission for early intervention. Ou...

Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer.

World journal of gastroenterology
BACKGROUND: Bleeding is one of the major complications after endoscopic submucosal dissection (ESD) in early gastric cancer (EGC) patients. There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.

Early home discharge after robot-assisted coronary artery bypass grafting.

Interactive cardiovascular and thoracic surgery
OBJECTIVES: Robot-assisted coronary artery bypass grafting (CABG) has been developed as a less invasive alternative for conventional CABG to enhance postoperative recovery, patient satisfaction and early discharge to home. Furthermore, it may provide...

Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images scalable deep learning.

World journal of gastroenterology
BACKGROUND: Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective.

Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Studies in health technology and informatics
Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molec...

Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.

Studies in health technology and informatics
OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children.

A Machine Learning-Based Predictive Model to Identify Patients Who Failed to Attend a Follow-up Visit for Diabetes Care After Recommendations From a National Screening Program.

Diabetes care
OBJECTIVE: Reportedly, two-thirds of the patients who were positive for diabetes during screening failed to attend a follow-up visit for diabetes care in Japan. We aimed to develop a machine-learning model for predicting people's failure to attend a ...

The Results of Peritoneal Re-Approximation Methods on Symptomatic Lymphocele Formation in Robot-Assisted Laparoscopic Radical Prostatectomy and Extended Pelvic Lymphadenectomy.

Archivos espanoles de urologia
INTRODUCTION: To evlauate role of peritoneal re-approximation methods in the prevention of symphtomatic lymphocele formation in patients underwent transperitoneal robot-assisted laparoscopic prostatectomy (tRALP) and extendeded pelvic lympadenoctomy ...

Efficacy of robot-assisted core decompression combined with human umbilical cord-derived mesenchymal stem cell transplantation for osteonecrosis of the femoral head.

European review for medical and pharmacological sciences
OBJECTIVE: The aim of this paper was to evaluate the effects of robot-assisted core decompression combined with human umbilical cord-derived mesenchymal stem cell (hUC-MSC) transplantation for early-stage osteonecrosis of the femoral head (ONFH).