AIMC Topic: Datasets as Topic

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Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks.

Primer on machine learning: utilization of large data set analyses to individualize pain management.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both s...

Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks.

Journal of digital imaging
Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National ...

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.

Journal of digital imaging
We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review o...

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq.

Nucleic acids research
ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragm...

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Journal of digital imaging
Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort t...

Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs.

Journal of digital imaging
Develop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded...

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...

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.

Nucleic acids research
Protein-protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predic...