The Journal of bone and joint surgery. American volume
Dec 18, 2019
BACKGROUND: The identification of surgical site infections for infection surveillance in hospitals depends on the manual abstraction of medical records and, for research purposes, depends mainly on the use of administrative or claims data. The object...
Gan to kagaku ryoho. Cancer & chemotherapy
Dec 1, 2019
BACKGROUND: Surgical site infections(SSIs)occur at a high frequency in patients after rectal cancer surgery and are readily aggravated. Therefore, prophylactic measures for infections based on the evaluation of the patient's perioperative risk are ve...
There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data le...
IEEE transactions on neural networks and learning systems
Oct 1, 2019
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unla...
OBJECTIVE: Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspe...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducte...
BMC medical informatics and decision making
May 24, 2019
BACKGROUND: Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the...
In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients' records, mined from the database of a high complex...
BACKGROUND: Machine-learning can elucidate complex relationships/provide insight to important variables for large datasets. This study aimed to develop an accurate model to predict neonatal surgical site infections (SSI) using different statistical m...
PURPOSE: To define the incidence of healthcare-associated ventriculitis and meningitis (HAVM) in the neuro-ICU and to identify HAVM risk factors using tree-based machine learning (ML) algorithms.