AIMC Topic: Humans

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Predictors of adherence to nicotine replacement therapy: Machine learning evidence that perceived need predicts medication use.

Drug and alcohol dependence
BACKGROUND: Nonadherence to smoking cessation medication is a frequent problem. Identifying pre-quit predictors of nonadherence may help explain nonadherence and suggest tailored interventions to address it.

ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Journal of chemical information and modeling
Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with develop active TB disease (ATB), while others remain latently infected (LTBI) during their lifetime, which has a completely different clinica...

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models.

Journal of chemical information and modeling
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem...

Towards an ontology for soft robots: what is soft?

Bioinspiration & biomimetics
The advent of soft robotics represents a profound change in the forms robots will take in the future. However, this revolutionary change has already yielded such a diverse collection of robots that attempts at defining this group do not reflect many ...

Development and validation of deep learning algorithms for scoliosis screening using back images.

Communications biology
Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposur...

A machine learning approach for the prediction of pulmonary hypertension.

PloS one
BACKGROUND: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, w...

Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability.

Science and engineering ethics
This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence (AI) technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed st...

Review of outcome measures in PARO robot intervention studies for dementia care.

Geriatric nursing (New York, N.Y.)
The aim of this study was to describe interventions for PARO, as well as the outcomes evaluated and found following use of PARO, and to identify outcome measures in PARO intervention studies for older adults with dementia. Multiple databases (Web of ...

Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based funct...

Robot-based play-drama intervention may improve the narrative abilities of Chinese-speaking preschoolers with autism spectrum disorder.

Research in developmental disabilities
BACKGROUND: Children with autism spectrum disorder (ASD) have deficits in their narrative skills and gestural communication. Very few intervention studies have been conducted with the aim of improving these skills.