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International Classification of Diseases

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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...

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.

Journal of digital imaging
An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concep...

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding mo...

Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury.

Neurosurgical focus
OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim...

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 ...

Evaluating the Impact of Incorrect Diabetes Coding on the Performance of Multivariable Prediction Models.

Studies in health technology and informatics
The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of...

EHR Text Categorization for Enhanced Patient-Based Document Navigation.

Studies in health technology and informatics
Patients with multiple disorders usually have long diagnosis lists, constitute by ICD-10 codes together with individual free-text descriptions. These text snippets are produced by overwriting standardized ICD-Code topics by the physicians at the poin...

Estimating a Bias in ICD Encodings for Billing Purposes.

Studies in health technology and informatics
ICD encoded diagnoses are a popular criterion for eligibility algorithms for study cohort recruitment. However, "official" ICD encoded diagnoses used for billing purposes are afflicted with a bias originating from legal issues. This work presents an ...

Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

Studies in health technology and informatics
Pathology reports are a main source of information regarding cancer diagnosis and are commonly written following semi-structured templates that include tumour localisation and behaviour. In this work, we evaluated the efficiency of support vector mac...

Determining Multiple Sclerosis Phenotype from Electronic Medical Records.

Journal of managed care & specialty pharmacy
BACKGROUND: Multiple sclerosis (MS), a central nervous system disease in which nerve signals are disrupted by scarring and demyelination, is classified into phenotypes depending on the patterns of cognitive or physical impairment progression: relapsi...