AIMC Topic: International Classification of Diseases
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Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning.
Journal of the American Medical Informatics Association : JAMIA
Mar 28, 2016
OBJECTIVE: To help cancer registrars efficiently and accurately identify reportable cancer cases.
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.
Journal of the American Medical Informatics Association : JAMIA
Mar 28, 2016
OBJECTIVE: Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby...
A method for modeling co-occurrence propensity of clinical codes with application to ICD-10-PCS auto-coding.
Journal of the American Medical Informatics Association : JAMIA
Feb 17, 2016
OBJECTIVE: Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely re...
COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on ...
Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record.
Digestive diseases and sciences
Nov 4, 2015
BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computa...
Triglycerides
Alanine Transaminase
Aspartate Aminotransferases
International Classification of Diseases
Logistic Models
United States
Diabetes Mellitus
Non-alcoholic Fatty Liver Disease
Data Collection
Natural Language Processing
Biopsy
Electronic Health Records
Middle Aged
Female
Prevalence
Humans
Cohort Studies
Algorithms
Aged
Adult
Male
[Comparison of ICD 10 and AIS with the Development of a Method for Automated Conversion].
Zeitschrift fur Orthopadie und Unfallchirurgie
Oct 15, 2015
BACKGROUND: Most of the current scores and outcome prediction calculations in traumatology are based on the Abbreviated Injury Scale (AIS). However, this is not routinely used for documentation and coding of injuries in many countries, including Germ...
A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies.
IEEE/ACM transactions on computational biology and bioinformatics
Sep 18, 2015
Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently a...
Automatic ICD-10 classification of cancers from free-text death certificates.
International journal of medical informatics
Aug 13, 2015
OBJECTIVE: Death certificates provide an invaluable source for cancer mortality statistics; however, this value can only be realised if accurate, quantitative data can be extracted from certificates--an aim hampered by both the volume and variable na...
Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso.
Journal of biomedical informatics
Dec 9, 2014
Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are capt...
Performance of Open-Source Large Language Models to Extract Symptoms from Clinical Notes.
Studies in health technology and informatics
Aug 7, 2025
In this study, we examined how well the open-source foundational large language models (LLMs) can extract symptoms and signs (S&S), along with their corresponding ICD-10 codes, from clinical notes found in the public MTSamples dataset. The dataset co...
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