Diagnosis and risk prediction of dilated cardiomyopathy in the era of big data and genomics
A Sammani, AF Baas, FW Asselbergs… - Journal of clinical …, 2021 - mdpi.com
Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening
ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically …
ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically …
Multi-label text classification based on semantic-sensitive graph convolutional network
D Zeng, E Zha, J Kuang, Y Shen - Knowledge-Based Systems, 2024 - Elsevier
Abstract Multi-Label Text Classification (MLTC) is an important but challenging task in the
field of natural language processing. In this paper, we propose a novel method, Semantic …
field of natural language processing. In this paper, we propose a novel method, Semantic …
Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
Standard reference terminology of diagnoses and risk factors is crucial for billing,
epidemiological studies, and inter/intranational comparisons of diseases. The International …
epidemiological studies, and inter/intranational comparisons of diseases. The International …
Automatic identification of patients with unexplained left ventricular hypertrophy in electronic health record data to improve targeted treatment and family screening
Background Unexplained Left Ventricular Hypertrophy (ULVH) may be caused by genetic
and non-genetic etiologies (eg, sarcomere variants, cardiac amyloid, or Anderson-Fabry's …
and non-genetic etiologies (eg, sarcomere variants, cardiac amyloid, or Anderson-Fabry's …
Multimodal learning for cardiovascular risk prediction using EHR data
A Bagheri, TKJ Groenhof, WB Veldhuis… - arXiv preprint arXiv …, 2020 - arxiv.org
Electronic health records (EHRs) contain structured and unstructured data of significant
clinical and research value. Various machine learning approaches have been developed to …
clinical and research value. Various machine learning approaches have been developed to …
[HTML][HTML] Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Objective Observational studies using electronic health record (EHR) databases often face
challenges due to unspecific clinical codes that can obscure detailed medical information …
challenges due to unspecific clinical codes that can obscure detailed medical information …
Improving clinical documentation: automatic inference of ICD-10 codes from patient notes using BERT model
Electronic health records provide a vast amount of text health data written by physicians as
patient clinical notes. The world health organization released the international classification …
patient clinical notes. The world health organization released the international classification …
Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X‐Ray Reports
A Bagheri, TKJ Groenhof… - Journal of healthcare …, 2021 - Wiley Online Library
Background and Objective. Electronic health records (EHRs) contain free‐text information
on symptoms, diagnosis, treatment, and prognosis of diseases. However, this potential …
on symptoms, diagnosis, treatment, and prognosis of diseases. However, this potential …
Automatic icd-10 codes association to diagnosis: Bulgarian case
This paper presents an approach for the automatic association of diagnoses in Bulgarian
language to ICD-10 codes. Since this task is currently performed manually by medical …
language to ICD-10 codes. Since this task is currently performed manually by medical …
Unstructured data in predictive process monitoring: lexicographic and semantic mapping to ICD-9-CM codes for the home hospitalization service
The large availability of hospital administrative and clinical data has encouraged the
application of Process Mining techniques to the healthcare domain. Predictive Process …
application of Process Mining techniques to the healthcare domain. Predictive Process …