AIMC Topic: Morbidity

Clear Filters Showing 21 to 30 of 46 articles

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Academic radiology
RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural ...

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

Journal of cellular and molecular medicine
There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death,...

Ninety-day morbidity of robot-assisted redo surgery for recurrent rectal prolapse, mesh erosion and pelvic pain: lessons learned from 9 years' experience in a tertiary referral centre.

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland
AIM: With increasing follow-up of patients treated with minimally invasive ventral mesh rectopexy (VMR) more redo surgery can be expected for recurrent rectal prolapse, mesh erosion and pelvic pain. The aim of this study is to evaluate the 90-day mor...

Robot assisted minimally invasive esophagectomy: safety, perioperative morbidity and short-term oncological outcome-a single institution experience.

Journal of robotic surgery
Robot assisted minimally invasive esophagectomy (RAMIE) has evolved over the past decade to become procedure of choice at many centers all over the world. The objective of this study is to present our experience of robot assisted minimally invasive e...

Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.

PloS one
BACKGROUND: Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavele...

Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: Traditional risk scores improved the definition of the initial therapeutic strategy in acute coronary syndrome (ACS), but they were not designed for predicting long-term individual risks and costs. In parallel, attempts to directly predic...

Identification of Patients with Heart Failure in Large Datasets.

Heart failure clinics
Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of case...

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI.

Clinical research in cardiology : official journal of the German Cardiac Society
BACKGROUND: Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indicat...

A Machine Learning Approach to Management of Heart Failure Populations.

JACC. Heart failure
BACKGROUND: Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies.

Innovations in research and clinical care using patient-generated health data.

CA: a cancer journal for clinicians
Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and t...