AIMC Topic: Machine Learning

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Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms.

Studies in health technology and informatics
This study aimed at testing the feasibility of neurosurgical procedures classification into 100+ classes using natural language processing and machine learning. A catboost algorithm and bidirectional recurrent neural network with a gated recurrent un...

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques.

Studies in health technology and informatics
Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospita...

Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients.

Studies in health technology and informatics
Artificial intelligence processes are increasingly being used in emergency medicine, notably for supporting clinical decisions and potentially improving healthcare services. This study investigated demographics, coagulation tests, and biochemical mar...

Integrating Human Patterns of Qualitative Coding with Machine Learning: A Pilot Study Involving Technology-Induced Error Incident Reports.

Studies in health technology and informatics
The objective of this research was to develop a reproducible method of integrating human patterns of qualitative coding with machine learning. The application of qualitative codes from the technology-induced error and safety literatures to the analys...

Substantive Interpretation of Machine Learning Solutions by the Example of Determining the Activity of the Tuberculosis Process in Individuals with Minimal Tuberculosis Residual Changes.

Studies in health technology and informatics
In this article is described an application of various machine learning (ML) methods to obtain decision rules and its interpretation to a problem of recognition of activity of the tuberculosis process. The research data base included 489 patients reg...

Conditional generative modeling for de novo protein design with hierarchical functions.

Bioinformatics (Oxford, England)
MOTIVATION: Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computatio...

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate ADMET (an abbreviation for 'absorption, distribution, metabolism, excretion and toxicity') predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple compreh...

AutoDC: an automatic machine learning framework for disease classification.

Bioinformatics (Oxford, England)
MOTIVATION: The emergence of next-generation sequencing techniques opens up tremendous opportunities for researchers to uncover the basic mechanisms of disease at the molecular level. Recently, automatic machine learning (AutoML) frameworks have been...

Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset.

Bioinformatics (Oxford, England)
MOTIVATION: Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progre...

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

Bioinformatics (Oxford, England)
MOTIVATION: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-li...