AIMC Topic: Neural Networks, Computer

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Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thus obscuring the identification of vulnerable older adults in need of additional medical and social s...

Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

American journal of clinical oncology
OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for ...

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks.

Journal of digital imaging
Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should be avoided when possible. Avoiding such exposure requires positioning and other maneuvers by techno...

A review of computational models of basic rule learning: The neural-symbolic debate and beyond.

Psychonomic bulletin & review
We present a critical review of computational models of generalization of simple grammar-like rules, such as ABA and ABB. In particular, we focus on models attempting to account for the empirical results of Marcus et al. (Science, 283(5398), 77-80 19...

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Journal of digital imaging
Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of ...

Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Journal of digital imaging
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However...

Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation.

Journal of digital imaging
To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect...

Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and clinical parameters.

Journal of hypertension
BACKGROUND: Cardiovascular disease, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. CHD is not entirely predicted by classic risk factors; however, they are preventable. Facing this major problem, the de...

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network.

Journal of digital imaging
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segm...

DeepIsoFun: a deep domain adaptation approach to predict isoform functions.

Bioinformatics (Oxford, England)
MOTIVATION: Isoforms are mRNAs produced from the same gene locus by alternative splicing and may have different functions. Although gene functions have been studied extensively, little is known about the specific functions of isoforms. Recently, some...