AIMC Topic: International Classification of Diseases

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Term Candidate Generation to Enrich Clinical Terminologies with Large Language Models.

Studies in health technology and informatics
Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigation, we report on how such a resource can be leveraged to identify additional term candidates for a ...

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Studies in health technology and informatics
Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complai...

Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Annotated language resources are essential for supervised machine learning methods. In the clinical domain, such data sets can boost use-case specific natural language processing services. In this work, we have analyzed a clinical problem list table ...

Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have developed several predictive models by learning disease representations to forecast the potential diagnosi...

Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment.

Studies in health technology and informatics
Clinical information systems have become large repositories for semi-structured and partly annotated electronic health record data, which have reached a critical mass that makes them interesting for supervised data-driven neural network approaches. W...

Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natura...

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

The journal of trauma and acute care surgery
BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrati...

A Deep Learning Framework for Automated ICD-10 Coding.

Studies in health technology and informatics
The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a pati...

Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients.

Studies in health technology and informatics
Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Observation Screening Scale (DOSS) or the Confusion Assessment Method (CAM). The aim of this work is t...

Supervised Learning for the ICD-10 Coding of French Clinical Narratives.

Studies in health technology and informatics
Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automa...