AIMC Topic: Deep Learning

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Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.

Neuro-oncology Nov 4, 2019
BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hype...
Image Processing, Computer-Assisted Glioma Brain Neoplasms Deep Learning Postoperative Care Tumor Burden Automation Longitudinal Studies Magnetic Resonance Imaging Algorithms Humans Prognosis
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Reporting on deep learning algorithms in health care.

The Lancet. Digital health Nov 1, 2019
Delivery of Health Care Humans Models, Statistical Deep Learning Algorithms Bias
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High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.

GigaScience Nov 1, 2019
BACKGROUND: Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genome...
Flowers Deep Learning Databases, Nucleic Acid Genotype Gene Expression Regulation, Plant Genetic Association Studies Triticum
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Deep learning for clustering of multivariate clinical patient trajectories with missing values.

GigaScience Nov 1, 2019
BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratifica...
Parkinson Disease Alzheimer Disease Deep Learning Models, Neurological Databases, Factual Male Disease Progression Precision Medicine Female Humans
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RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

GigaScience Nov 1, 2019
BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentat...
Image Processing, Computer-Assisted Plant Roots Deep Learning
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Power play.

Nature Nov 1, 2019
Deep Learning
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Cohort selection for clinical trials using deep learning models.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural langu...
Natural Language Processing Patient Selection Clinical Trials as Topic Data Mining Neural Networks, Computer Humans Deep Learning
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Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might sol...
Natural Language Processing Patient Selection Clinical Trials as Topic Classification Data Mining Deep Learning Logistic Models Neural Networks, Computer Machine Learning Humans
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Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data represent...
Deep Learning Support Vector Machine Humans Neural Networks, Computer Data Mining Clinical Trials as Topic Natural Language Processing Patient Selection
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Clinical trial cohort selection based on multi-level rule-based natural language processing system.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2019
OBJECTIVE: Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients' eligibil...
Humans Data Mining Clinical Trials as Topic Natural Language Processing Patient Selection Neural Networks, Computer Deep Learning
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