[HTML][HTML] GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification
Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …
precise computational methodologies to extract useful insights from biomedical literature …
ML-Net: multi-label classification of biomedical texts with deep neural networks
Objective In multi-label text classification, each textual document is assigned 1 or more
labels. As an important task that has broad applications in biomedicine, a number of different …
labels. As an important task that has broad applications in biomedicine, a number of different …
Automated ICD-9 coding via a deep learning approach
ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to
describe a patient's diagnosis. Accurate automated ICD-9 coding is important because …
describe a patient's diagnosis. Accurate automated ICD-9 coding is important because …
Effective convolutional attention network for multi-label clinical document classification
Multi-label document classification (MLDC) problems can be challenging, especially for long
documents with a large label set and a long-tail distribution over labels. In this paper, we …
documents with a large label set and a long-tail distribution over labels. In this paper, we …
Medical text classification using hybrid deep learning models with multihead attention
SK Prabhakar, DO Won - Computational intelligence and …, 2021 - Wiley Online Library
To unlock information present in clinical description, automatic medical text classification is
highly useful in the arena of natural language processing (NLP). For medical text …
highly useful in the arena of natural language processing (NLP). For medical text …
Performance evaluation of deep learning algorithms in biomedical document classification
B Behera, G Kumaravelan… - 2019 11th international …, 2019 - ieeexplore.ieee.org
Document classification is a prevalent task in Natural Language Processing (NLP), which
has an extensive range of applications in the biomedical domains such as biomedical …
has an extensive range of applications in the biomedical domains such as biomedical …
Convolutional neural networks for biomedical text classification: application in indexing biomedical articles
A Rios, R Kavuluru - Proceedings of the 6th ACM conference on …, 2015 - dl.acm.org
Building high accuracy text classifiers is an important task in biomedicine given the wealth of
information hidden in unstructured narratives such as research articles and clinical …
information hidden in unstructured narratives such as research articles and clinical …
Multi-label clinical document classification: Impact of label-density
Objective The goal of this work is the classification of Electronic Health Records using
Natural Language Techniques. Electronic Health Records (EHRs) convey valuable clinical …
Natural Language Techniques. Electronic Health Records (EHRs) convey valuable clinical …
Initializing neural networks for hierarchical multi-label text classification
S Baker, A Korhonen - 2017 - repository.cam.ac.uk
Many tasks in the biomedical domain require the assignment of one or more predefined
labels to input text, where the labels are a part of a hierarchical structure (such as a …
labels to input text, where the labels are a part of a hierarchical structure (such as a …
Medical coding classification by leveraging inter-code relationships
Medical coding or classification is the process of transforming information contained in
patient medical records into standard predefined medical codes. There are several …
patient medical records into standard predefined medical codes. There are several …