AIMC Topic: Models, Theoretical

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Pedestrian's risk-based negotiation model for self-driving vehicles to get the right of way.

Accident; analysis and prevention
Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are pro...

Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life.

BMC medical informatics and decision making
BACKGROUND: This paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been...

Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome.

BMC medical informatics and decision making
BACKGROUND: Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effe...

A study on quality assessment of the surface EEG signal based on fuzzy comprehensive evaluation method.

Computer assisted surgery (Abingdon, England)
Surface EEG (Electroencephalography) signal is vulnerable to interference due to its characteristics and sampling methods. So it is of great importance to evaluate the collected EEG signal prior to use. Traditional methods usually use the impedance b...

Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.

BMC medical informatics and decision making
BACKGROUND: Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient...

Machine learning models to predict disease progression among veterans with hepatitis C virus.

PloS one
BACKGROUND: Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. C...

Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.

BMC bioinformatics
BACKGROUND: Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma prot...

Task activations produce spurious but systematic inflation of task functional connectivity estimates.

NeuroImage
Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (...

Predicting adverse drug reactions through interpretable deep learning framework.

BMC bioinformatics
BACKGROUND: Adverse drug reactions (ADRs) are unintended and harmful reactions caused by normal uses of drugs. Predicting and preventing ADRs in the early stage of the drug development pipeline can help to enhance drug safety and reduce financial cos...

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands.

PloS one
Grasp affordances in robotics represent different ways to grasp an object involving a variety of factors from vision to hand control. A model of grasp affordances that is able to scale across different objects, features and domains is needed to provi...