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Clinical Coding

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Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Quality reporting that relies on coded administrative data alone may not completely and accurately depict providers' performance. To assess this concern with a test case, we developed and evaluated a natural language processing (NLP) approach to iden...

Development of an Automatic Coding System for Digestive Endoscopies.

Studies in health technology and informatics
Digestive endoscopies, along with all medical procedures in France are coded with the CCAM. This task is done by the physicians, is time-consuming and requires a good knowledge of the terminology besides a medical knowledge. This method offers an aut...

Comparative Analysis of Algorithmic Approaches for Auto-Coding with ICD-10-AM and ACHI.

Studies in health technology and informatics
Clinical coding is done using ICD-10-AM (International Classification of Diseases, version 10, Australian Modification) and ACHI (Australian Classification of Health Interventions) in acute and sub-acute hospitals in Australia for funding, insurance ...

Automated ICD-9 Coding via A Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
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 manual coding is expensive, time-consuming, and inefficient. Inspired by the...

Prediction task guided representation learning of medical codes in EHR.

Journal of biomedical informatics
There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental an...

Towards automated clinical coding.

International journal of medical informatics
BACKGROUND: Patients' encounters with healthcare services must undergo clinical coding. These codes are typically derived from free-text notes. Manual clinical coding is expensive, time-consuming and prone to error. Automated clinical coding systems ...

Comparison of Natural Language Processing and Manual Coding for the Identification of Cross-Sectional Imaging Reports Suspicious for Lung Cancer.

JCO clinical cancer informatics
PURPOSE: To compare the accuracy and reliability of a natural language processing (NLP) algorithm with manual coding by radiologists, and the combination of the two methods, for the identification of patients whose computed tomography (CT) reports ra...

EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.

BMC medical informatics and decision making
BACKGROUND: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive ...

Linking Health Records with Knowledge Sources Using OWL and RDF.

Studies in health technology and informatics
This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using scheme...

A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding.

Drug safety
INTRODUCTION: Adverse drug reaction (ADR) detection in hospitals is heavily reliant on spontaneous reporting by clinical staff, with studies in the literature pointing to high rates of underreporting [1]. International Classification of Diseases, 10t...