AIMC Topic: Machine Learning

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A machine learning approach for computation of cardiovascular intrinsic frequencies.

PloS one
Analysis of cardiovascular waveforms provides valuable clinical information about the state of health and disease. The intrinsic frequency (IF) method is a recently introduced framework that uses a single arterial pressure waveform to extract physiol...

Machine learning approaches for predicting Cracking Tolerance Index (CTIndex) of asphalt concrete containing reclaimed asphalt pavement.

PloS one
One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete's crack resistance is represe...

Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.

GigaScience
BACKGROUND: Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of featu...

Computerized analysis of facial expression reveals objective indices of blunted facial affect.

European archives of psychiatry and clinical neuroscience
Blunted affect is associated with severe mental illness, particularly schizophrenia. Mechanisms of blunted affect are poorly understood, potentially due to a lack of phenomenological clarity. Here, we examine clinician rated blunted affect and comput...

Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse.

Epidemiology (Cambridge, Mass.)
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning method...

Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review.

Journal of biomedical informatics
INTRODUCTION: The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systemati...

Human-like systematic generalization through a meta-learning neural network.

Nature
The power of human language and thought arises from systematic compositionality-the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn famously argued that artificial neural networks lack this cap...

PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates.

Journal of cellular biochemistry
The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane-bound proteins and are involved in the movement of ions, small and macromolecules across bio-membranes. The importa...

On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications.

Sensors (Basel, Switzerland)
The integration of Deep Learning (DL) models with the HoloLens2 Augmented Reality (AR) headset has enormous potential for real-time AR medical applications. Currently, most applications execute the models on an external server that communicates with ...

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

Oncogene
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better inte...