AIMC Topic: Female

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Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.

JMIR research protocols
BACKGROUND: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient rep...

Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

BMC urology
OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning.

BMC medicine
BACKGROUND: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.

Theranostics
The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensit...

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study.

Frontiers in public health
BACKGROUND: Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigor...

Established machine learning models to predict readmission for elderly patients with ischemic heart disease.

Kardiologia polska
BACKGROUND: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studi...

Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features.

Sensors (Basel, Switzerland)
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into ...

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.

BMC cancer
BACKGROUND AND AIMS: The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatur...

A powerful machine learning approach to identify interactions of differentially abundant gut microbial subsets in patients with metastatic and non-metastatic pancreatic cancer.

Gut microbes
Pancreatic cancer has a dismal prognosis, as it is often diagnosed at stage IV of the disease and is characterized by metastatic spread. Gut microbiota and its metabolites have been suggested to influence the metastatic spread by modulating the host ...

Machine Learning Based Prediction of Post-operative Infrarenal Endograft Apposition for Abdominal Aortic Aneurysms.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: Challenging infrarenal aortic neck characteristics have been associated with an increased risk of type Ia endoleak after endovascular aneurysm repair (EVAR). Short apposition (< 10 mm circumferential shortest apposition length [SAL]) on th...