AIMC Topic: Machine Learning

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A murdered teen, two million tweets and an experiment to fight gun violence.

Nature
Researchers are using AI to decode the language of Chicago gangs. Next they’ll look for opportunities to intervene before online aggression turns deadly.
In the middle of the day on 11 April 2014, a hooded gunman ambushed Gakirah Barnes on the str...

Advancing In-Hospital Clinical Deterioration Prediction Models.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Early warning systems lack robust evidence that they improve patients' outcomes, possibly because of their limitation of predicting binary rather than time-to-event outcomes.

Rise of the machines? Machine learning approaches and mental health: opportunities and challenges.

The British journal of psychiatry : the journal of mental science
Machine learning methods are being increasingly applied to physical healthcare. In this article we describe some of the potential benefits, challenges and limitations of this approach in a mental health context. We provide a number of examples where ...

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.

Toxicological sciences : an official journal of the Society of Toxicology
Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute o...

Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk.

JAMA psychiatry
IMPORTANCE: Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear.

Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain.

Pain
Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various timesca...

pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.

Bioinformatics (Oxford, England)
SUMMARY: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and diffi...