Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
Neural natural language processing for unstructured data in electronic health records: a review
Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …
Explainable prediction of medical codes from clinical text
Clinical notes are text documents that are created by clinicians for each patient encounter.
They are typically accompanied by medical codes, which describe the diagnosis and …
They are typically accompanied by medical codes, which describe the diagnosis and …
Limitations of transformers on clinical text classification
Bidirectional Encoder Representations from Transformers (BERT) and BERT-based
approaches are the current state-of-the-art in many natural language processing (NLP) …
approaches are the current state-of-the-art in many natural language processing (NLP) …
A label attention model for ICD coding from clinical text
ICD coding is a process of assigning the International Classification of Disease diagnosis
codes to clinical/medical notes documented by health professionals (eg clinicians). This …
codes to clinical/medical notes documented by health professionals (eg clinicians). This …
[HTML][HTML] Few-shot and zero-shot multi-label learning for structured label spaces
A Rios, R Kavuluru - Proceedings of the Conference on Empirical …, 2018 - ncbi.nlm.nih.gov
Large multi-label datasets contain labels that occur thousands of times (frequent group),
those that occur only a few times (few-shot group), and labels that never appear in the …
those that occur only a few times (few-shot group), and labels that never appear in the …
ICD coding from clinical text using multi-filter residual convolutional neural network
Automated ICD coding, which assigns the International Classification of Disease codes to
patient visits, has attracted much research attention since it can save time and labor for …
patient visits, has attracted much research attention since it can save time and labor for …
[HTML][HTML] Readmission prediction using deep learning on electronic health records
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF)
patients that pose significant health risks and escalate care cost. In order to reduce …
patients that pose significant health risks and escalate care cost. In order to reduce …
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 …
HyperCore: Hyperbolic and co-graph representation for automatic ICD coding
Abstract The International Classification of Diseases (ICD) provides a standardized way for
classifying diseases, which endows each disease with a unique code. ICD coding aims to …
classifying diseases, which endows each disease with a unique code. ICD coding aims to …