AIMC Topic: Clinical Coding
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Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text.
Journal of biomedical informatics
Feb 26, 2018
We address the assignment of ICD-10 codes for causes of death by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We leverage a deep n...
Assigning clinical codes with data-driven concept representation on Dutch clinical free text.
Journal of biomedical informatics
Apr 8, 2017
Clinical codes are used for public reporting purposes, are fundamental to determining public financing for hospitals, and form the basis for reimbursement claims to insurance providers. They are assigned to a patient stay to reflect the diagnosis and...
Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
Journal of the American Medical Informatics Association : JAMIA
Aug 7, 2016
OBJECTIVE: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, a...
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...
Assessing the Utility of Automatic Cancer Registry Notifications Data Extraction from Free-Text Pathology Reports.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
Cancer Registries record cancer data by reading and interpreting pathology cancer specimen reports. For some Registries this can be a manual process, which is labour and time intensive and subject to errors. A system for automatic extraction of cance...
Using ontologies to improve semantic interoperability in health data.
Journal of innovation in health informatics
Jul 10, 2015
The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as s...
Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.
Journal of the American Medical Informatics Association : JAMIA
Jun 1, 2025
OBJECTIVE: Diagnosis codes documented in electronic health records (EHR) are often relied upon to clinically phenotype patients for biomedical research. However, these diagnoses can be incomplete and inaccurate, leading to false negatives when search...
From Admission to Discharge: Leveraging NLP for Upstream Primary Coding with SNOMED CT.
Journal of medical systems
May 22, 2025
This study aims to describe implementing a SNOMED CT-coded health problem (HP) list at Hospital Clínic de Barcelona. The project focuses on enhancing the accuracy and efficiency of clinical coding by automating the process from patient admission, whi...
Exploring the Potential of GPT-4 in Creating Billing Codes from Clinic Notes.
Studies in health technology and informatics
May 15, 2025
Creating standardized billing codes from clinic notes is challenging due to the complexity of over 22,000 codes and the unstructured nature of medical records. This paper investigates how well GPT-4, can automate CPT/HCPCS codes generation. To assess...
Optimizing Automated KCD Coding: A Retrieval-Verification Approach.
Studies in health technology and informatics
May 15, 2025
This study proposes a two-step Retrieval-Verification system for automating the assignment of Korean Standard Classification of Diseases (KCD) codes to free-text diagnoses. The system uses SapBERT-XLMR for initial retrieval, followed by Llama 3.1 for...
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