AIMC Topic: Risk Factors

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Detection of factors affecting kidney function using machine learning methods.

Scientific reports
Due to the increasing prevalence of chronic kidney disease and its high mortality rate, study of risk factors affecting the progression of the disease is of great importance. Here in this work, we aim to develop a framework for using machine learning...

A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma.

Medical physics
BACKGROUND: Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment deci...

A Machine Learning Model for Prediction of Amputation in Diabetics.

Journal of diabetes science and technology
BACKGROUND: Diabetic foot ulcer (DFU) and the resulting lower extremity amputation are associated with a poor survival prognosis. The objective of this study is to generate a model for predicting the probability of major amputation in hospitalized pa...

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury.

Advances in kidney disease and health
Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention an...

Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods.

Diabetes & metabolic syndrome
BACKGROUND & AIM: Diabetes mellitus has become one of the out brakes causing major health issues in developing countries like India. The need for leveraging technology is felt in diabetes management. The main objective of this work is to deploy machi...

Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach.

BMC public health
BACKGROUND: Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of...

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis.

Frontiers in immunology
BACKGROUND: Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches.

Scientific reports
Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has been limited for lower-middle-income countries, especially Vietnam. This study aims to develop pred...

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Cancer science
Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based mo...

Long-term oncologic outcomes of robot-assisted radical cystectomy: update series from a high-volume robotic center beyond 10 years of follow-up.

Journal of robotic surgery
Long-term oncologic data on patients undergoing robot-assisted radical cystectomy (RARC) for non-metastatic bladder cancer (BCa) are limited. The purpose of this study is to describe long-term oncologic outcomes of patients receiving robotic radical ...