AIMC Topic: Humans

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Treatment effect prediction with adversarial deep learning using electronic health records.

BMC medical informatics and decision making
BACKGROUND: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized cl...

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.

BMC bioinformatics
BACKGROUND: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology...

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational ...

Cross-modality deep learning: Contouring of MRI data from annotated CT data only.

Medical physics
PURPOSE: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-segmentation algorithms for organs-at-risk and radiation targets. Current practice of manual segmentation is subjective and time-consuming. While deep l...

The Prevalence of Euthyroid Hypertriiodothyroninemia in Newly Diagnosed Multiple Myeloma and its Clinical Characteristics.

Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
OBJECTIVE: To evaluate the prevalence of euthyroid hypertriiodothyroninemia and/or hyperthyroxinemia and its clinical characteristics in multiple myeloma (MM) patients.

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Neurobiology of aging
Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learni...

Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: A multicentre study.

International journal of medical informatics
BACKGROUND: Recent advances in artificial intelligence (AI) have shown great promise in detecting some diseases based on medical images. Most studies developed AI diagnostic systems only using eligible images. However, in real-world settings, ineligi...

Semi-supervised disentangled framework for transferable named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale la...

Greedy auto-augmentation for n-shot learning using deep neural networks.

Neural networks : the official journal of the International Neural Network Society
The goal of n-shot learning is the classification of input data from small datasets. This type of learning is challenging in neural networks, which typically need a high number of data during the training process. Recent advancements in data augmenta...