A5089485363- Radiomics and Machine Learning in Medical Imaging
- Topic Modeling
- Biomedical Text Mining and Ontologies
- Lung Cancer Diagnosis and Treatment
- Natural Language Processing Techniques
- Medical Coding and Health Information
- Electronic Health Records Systems
- Machine Learning in Healthcare
- Traumatic Brain Injury and Neurovascular Disturbances
- Radiology practices and education
- Semantic Web and Ontologies
- Advanced Radiotherapy Techniques
Maastro Clinic
2018-2024
Maastricht University Medical Centre
2018-2024
Maastricht University
2020-2021
Manual cohort building from radiology reports can be tedious. Natural Language Processing (NLP) used for automated building. In this study, we have developed and validated an NLP approach based on deep learning (DL) to select lung cancer a thoracic disease management group cohort. 4064 (CT PET/CT) of reported between 2014 2016 were used. These anonymised, cleaned, text normalized split into training, testing, validation set. External was performed the MIMIC-III clinical database. We three DL...
Rising incidence and mortality of cancer have led to an incremental amount research in the field. To learn from preexisting data, it has become important capture maximum information related disease type, stage, treatment, outcomes. Medical imaging reports are rich this kind but only present as free text. The extraction such unstructured text is labor-intensive. use Natural Language Processing (NLP) tools extract radiology can make less time-consuming well more effective. In study, we...
Background Natural language processing (NLP) is thought to be a promising solution extract and store concepts from free text in structured manner for data mining purposes. This also true radiology reports, which still consist mostly of text. Accurate complete reports are very important clinical decision support, instance, oncological staging. As such, NLP can tool structure the content report, thereby increasing report’s value. Objective study describes implementation validation an N-stage...
Abstract Background In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially oncological staging need to be stage treat a patient, as well population-level surveillance outcome assessment. To support extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built quantify T-stage pulmonary tumors according tumor node metastasis (TNM) classification. This structuring tool...
Abstract Natural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete accurate for clinical staging of, instance, pulmonary oncology. A computed tomography (CT) or positron emission (PET)-CT scan of great importance in tumor staging, NLP may additional value the report when able extract T N stage 8th tumor–node–metastasis (TNM) classification system. The purpose this study...
Performing image feature extraction in radiation oncology is often dependent on the organ and tumor delineations provided by clinical staff. These delineation names are free text DICOM metadata fields resulting undefined information, which requires effort to use large-scale efforts. In this work we present a scale-able solution overcome these naming convention challenges with REST service using Semantic Web technology convert information linked data. As proof of concept an open source...
<sec> <title>BACKGROUND</title> The International Classification of Diseases (ICD), developed by the WHO, standardizes health condition coding to support healthcare policy, research, and billing, but AI automation, while promising, still underperforms compared human accuracy lacks explainability needed for adoption in medical settings. </sec> <title>OBJECTIVE</title> potential Large Language Models (LLMs) is explored assisting coders Diseases-10 (ICD-10) coding. study aims on augmenting...
The International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy lacks explainability needed for adoption in medical settings. potential large language models assisting coders ICD-10 was explored through development a computer-assisted system. This study aimed augment...
Material and MethodsComplete data of 65 patients (pts), including overall, locoregional relapse distant metastasis-free survival (OS, LRFS, DMFS) information were available.Pts received 41.4Gy in 18 fr (2.3 Gy/fr) delivering ART concomitant boost on the residual GTV last 6 (3 Gy/fr, Dmean: 45.6Gy).Chemotherapy consisted oxaliplatin (OXA) 100 mg/m 2 days -14, 0 (start RT), +14, 5-fluorouracil (5-FU) 200 /d from day -14 to end RT.Uni-and multi-variable Cox regression models for OS, LRFS DMFS...
<sec> <title>BACKGROUND</title> Natural language processing (NLP) is thought to be a promising solution extract and store concepts from free text in structured manner for data mining purposes. This also true radiology reports, which still consist mostly of text. Accurate complete reports are very important clinical decision support, instance, oncological staging. As such, NLP can tool structure the content report, thereby increasing report’s value. </sec> <title>OBJECTIVE</title> study...