Automated machine learning for healthcare and clinical notes analysis
Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …
impact has been astonishing. To accelerate embedding ML in more applications and …
A survey of automated International Classification of Diseases coding: development, challenges, and applications
The International Classification of Diseases (ICD) is an international standard and tool for
epidemiological investigation, health management, and clinical diagnosis with a …
epidemiological investigation, health management, and clinical diagnosis with a …
[KNIHA][B] Clinical text mining: Secondary use of electronic patient records
H Dalianis - 2018 - library.oapen.org
Hercules Dalianis Secondary Use of Electronic Patient Records Page 1 Hercules Dalianis
Clinical Text Mining Secondary Use of Electronic Patient Records Page 2 Clinical Text …
Clinical Text Mining Secondary Use of Electronic Patient Records Page 2 Clinical Text …
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
Background Clinical text classification is an fundamental problem in medical natural
language processing. Existing studies have cocnventionally focused on rules or knowledge …
language processing. Existing studies have cocnventionally focused on rules or knowledge …
Bridging the vocabulary gap between health seekers and healthcare knowledge
The vocabulary gap between health seekers and providers has hindered the cross-system
operability and the inter-user reusability. To bridge this gap, this paper presents a novel …
operability and the inter-user reusability. To bridge this gap, this paper presents a novel …
A unified review of deep learning for automated medical coding
Automated medical coding, an essential task for healthcare operation and delivery, makes
unstructured data manageable by predicting medical codes from clinical documents. Recent …
unstructured data manageable by predicting medical codes from clinical documents. Recent …
Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning
Background Electronic health records are invaluable for medical research, but much of the
information is recorded as unstructured free text which is time-consuming to review …
information is recorded as unstructured free text which is time-consuming to review …
Automatic diagnosis coding of radiology reports: a comparison of deep learning and conventional classification methods
Diagnosis autocoding services and research intend to both improve the productivity of
clinical coders and the accuracy of the coding. It is an important step in data analysis for …
clinical coders and the accuracy of the coding. It is an important step in data analysis for …
[PDF][PDF] Classification of optical coherence tomography using convolutional neural networks
This article describes a classification model of optical coherence tomography images using
convolution neural network. The dataset used was the Labeled Optical Coherence …
convolution neural network. The dataset used was the Labeled Optical Coherence …
Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries
Effective coding of patient records in hospitals is an essential requirement for epidemiology,
billing, and managing insurance claims. The prevalent practice of manual coding, carried …
billing, and managing insurance claims. The prevalent practice of manual coding, carried …