Automated clinical coding: what, why, and where we are?
Clinical coding is the task of transforming medical information in a patient's health records
into structured codes so that they can be used for statistical analysis. This is a cognitive and …
into structured codes so that they can be used for statistical analysis. This is a cognitive and …
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 …
BERT-XML: Large scale automated ICD coding using BERT pretraining
Z Zhang, J Liu, N Razavian - arXiv preprint arXiv:2006.03685, 2020 - arxiv.org
Clinical interactions are initially recorded and documented in free text medical notes. ICD
coding is the task of classifying and coding all diagnoses, symptoms and procedures …
coding is the task of classifying and coding all diagnoses, symptoms and procedures …
DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification
The great number of online scientific publications on cancer research makes large scale
data mining possible. The hallmarks or characteristics of cancer can be used to distinguish …
data mining possible. The hallmarks or characteristics of cancer can be used to distinguish …
Characterizing the value of information in medical notes
Machine learning models depend on the quality of input data. As electronic health records
are widely adopted, the amount of data in health care is growing, along with complaints …
are widely adopted, the amount of data in health care is growing, along with complaints …
Model distillation for faithful explanations of medical code predictions
Z Wood-Doughty, I Cachola… - Proceedings of the 21st …, 2022 - aclanthology.org
Abstract Machine learning models that offer excellent predictive performance often lack the
interpretability necessary to support integrated human machine decision-making. In clinical …
interpretability necessary to support integrated human machine decision-making. In clinical …
Representation learning for electronic health records
WH Weng, P Szolovits - arXiv preprint arXiv:1909.09248, 2019 - arxiv.org
Information in electronic health records (EHR), such as clinical narratives, examination
reports, lab measurements, demographics, and other patient encounter entries, can be …
reports, lab measurements, demographics, and other patient encounter entries, can be …
Term extraction from medical documents using word embeddings
M Bay, D Bruneß, M Herold, C Schulze… - 2020 6th IEEE …, 2021 - ieeexplore.ieee.org
In this paper we present a new method for the extraction of discipline-specific terms from
medical documents. Due to the small text corpora and the specific nature of medical …
medical documents. Due to the small text corpora and the specific nature of medical …
Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review
Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is
labour-intensive and error-prone, which has motivated research towards full automation of …
labour-intensive and error-prone, which has motivated research towards full automation of …
Automatic Coding at Scale: Design and Deployment of a Nationwide System for Normalizing Referrals in the Chilean Public Healthcare System
The disease coding task involves assigning a unique identifier from a controlled vocabulary
to each disease mentioned in a clinical document. This task is relevant since it allows …
to each disease mentioned in a clinical document. This task is relevant since it allows …