AIMC Topic: Machine Learning

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Machine learning-based analyses support the existence of species complexes for and .

Parasitology
Human strongyloidiasis is a serious disease mostly attributable to Strongyloides stercoralis and to a lesser extent Strongyloides fuelleborni, a parasite mainly of non-human primates. The role of animals as reservoirs of human-infecting Strongyloides...

Predicting dengue importation into Europe, using machine learning and model-agnostic methods.

Scientific reports
The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and i...

Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer's disease and cancers.

BMC medical informatics and decision making
BACKGROUND: Predictive patient stratification is greatly emerging, because it allows us to prospectively identify which patients will benefit from what interventions before their condition worsens. In the biomedical research, a number of stratificati...

Artificial intelligence: improving the efficiency of cardiovascular imaging.

Expert review of medical devices
INTRODUCTION: Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or p...

Computing schizophrenia: ethical challenges for machine learning in psychiatry.

Psychological medicine
Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these...

Introduction to deep learning: minimum essence required to launch a research.

Japanese journal of radiology
In the present article, we provide an overview on the basics of deep learning in terms of technical aspects and steps required to launch a deep learning research. Deep learning is a branch of artificial intelligence, which has been attracting interes...

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND: Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave...

Role of Artificial Intelligence and Machine Learning in Nanosafety.

Small (Weinheim an der Bergstrasse, Germany)
Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. N...

A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images.

International journal of computer assisted radiology and surgery
PURPOSE: The evaluation of fetal lung maturity is critical for clinical practice since the lung immaturity is an important cause of neonatal morbidity and mortality. For the evaluation of the development of fetal lung maturation degree, our study est...

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models.

Environmental toxicology and chemistry
The soil adsorption coefficient (K ) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to...