AIMC Topic: Female

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Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes of the Disease: A Derivation and Validation Study Using Machine Learning.

Hepatology (Baltimore, Md.)
Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a prediction model and compare its performance to existing surrogate markers. The mod...

A novel prediction method for lymph node involvement in endometrial cancer: machine learning.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. I...

Presence of gammaherpesvirus BoHV-4 in endometrial cytology samples is not associated with subclinical endometritis diagnosed at artificial insemination in dairy cows.

Veterinary microbiology
In the past, bovine herpesvirus 4 (BoHV-4) has been suggested to be associated with metritis and endometritis. However, not many field studies investigated the association between BoHV-4 and subclinical endometritis (SCE). In the present study, the a...

Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Although 3D echocardiography (3DE) circumvents many limitations of 2D echocardiography by allowing direct measurements of left ventricular (LV) mass, it is seldom used in clinical practice due to time-consuming analysis. A recently develo...

Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

Medical physics
PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logistic regression and decision trees) in predictive modeling of post-RT treatment failure and adverse eve...

Using natural language processing and machine learning to identify breast cancer local recurrence.

BMC bioinformatics
BACKGROUND: Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cance...

Employing AgNPs doped amidoxime-modified polyacrylonitrile (PAN-oxime) nanofibers for target induced strand displacement-based electrochemical aptasensing of CA125 in ovarian cancer patients.

Materials science & engineering. C, Materials for biological applications
In this study, a high-performance biosensing nanoplatform based on amidoxime-modified polyacrylonitrile nanofibers decorated with Ag nanoparticles (AgNPs-PAN-oxime NFs) is described. The AgNPs-PAN-oxime NFs were prepared by the combination of electro...

The analysis of the effects of acute rheumatic fever in childhood on cardiac disease with data mining.

International journal of medical informatics
BACKGROUND: Acute rheumatic fever (ARF) is an important disease that is frequently seen in Turkey, it is necessary to develop solutions to cure the disease. It is believed that new data analysis methods may be applied to this disease, and this may be...