AIMC Topic: Machine Learning

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[Artificial intelligence in psychiatry-an overview].

Der Nervenarzt
Artificial intelligence and the underlying methods of machine learning and neuronal networks (NN) have made dramatic progress in recent years and have allowed computers to reach superhuman performance in domains that used to be thought of as uniquely...

Artificial intelligence in radiology.

Nature reviews. Cancer
Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in th...

Machine learning approach for early detection of autism by combining questionnaire and home video screening.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtaine...

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.

Toxicological sciences : an official journal of the Society of Toxicology
Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to ou...

Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational heal...

Learned protein embeddings for machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enab...

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.

Journal of digital imaging
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch ...

Implementation of Enterprise Imaging Strategy at a Chinese Tertiary Hospital.

Journal of digital imaging
Medical images have become increasingly important in clinical practice and medical research, and the need to manage images at the hospital level has become urgent in China. To unify patient identification in examinations from different medical specia...