AIMC Topic: Machine Learning

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Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI.

Computational and mathematical methods in medicine
Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with...

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

PloS one
The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied ...

Deep Learning to Classify Radiology Free-Text Reports.

Radiology
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) ...

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.

Journal of computer-aided molecular design
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org...

An introduction and overview of machine learning in neurosurgical care.

Acta neurochirurgica
BACKGROUND: Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the c...

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Molecular pharmaceutics
Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of t...

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

Journal of biomedical informatics
BACKGROUND: Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fie...

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

Current opinion in chemical biology
Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the...

A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making.

Journal of biomedical informatics
From the perspective of clinical decision-making in a Medical IoT-based healthcare system, achieving effective and efficient analysis of long-term health data for supporting wise clinical decision-making is an extremely important objective, but deter...

Graph-based representation of behavior in detection and prediction of daily living activities.

Computers in biology and medicine
Various surveillance systems capture signs of human activities of daily living (ADLs) and store multimodal information as time line behavioral records. In this paper, we present a novel approach to the analysis of a behavioral record used in a survei...