AIMC Topic: Machine Learning

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Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares.

Mathematical biosciences and engineering : MBE
A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect ...

AttOmics: attention-based architecture for diagnosis and prognosis from omics data.

Bioinformatics (Oxford, England)
MOTIVATION: The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially t...

Transfer learning for drug-target interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learnin...

Prediction of all-cause mortality for chronic kidney disease patients using four models of machine learning.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
BACKGROUND: The prediction tools developed from general population data to predict all-cause mortality are not adapted to chronic kidney disease (CKD) patients, because this population displays a higher mortality risk. This study aimed to create a cl...

Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.

Studies in health technology and informatics
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techn...

Combining NLP and Machine Learning for Differential Diagnosis of COPD Exacerbation Using Emergency Room Data.

Studies in health technology and informatics
Chronic Obstructive Pulmonary Disease (COPD) exacerbation exhibits a set of overlapping symptoms with various forms of cardiovascular disease, which makes its early identification challenging. Timely identification of the underlying condition that ca...

Data Quality in Healthcare for the Purpose of Artificial Intelligence: A Case Study on ECG Digitalization.

Studies in health technology and informatics
The quantity of data generated within healthcare is increasing exponentially. Following this development, the interest of using data driven methodologies such as machine learning is on a steady rise. However, the quality of the data also needs to be ...

Performance of Artificial Intelligence in Predicting Future Depression Levels.

Studies in health technology and informatics
Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively i...

Data Quality Estimation Via Model Performance: Machine Learning as a Validation Tool.

Studies in health technology and informatics
In our recent study, the attempt to classify neurosurgical operative reports into routinely used expert-derived classes exhibited an F-score not exceeding 0.74. This study aimed to test how improving the classifier (target variable) affected the shor...

A Conceptual Framework to Predict Disease Progressions in Patients with Chronic Kidney Disease, Using Machine Learning and Process Mining.

Studies in health technology and informatics
Process Mining is a technique looking into the analysis and mining of existing process flow. On the other hand, Machine Learning is a data science field and a sub-branch of Artificial Intelligence with the main purpose of replicating human behavior t...