Qualitative analysis of manual annotations of clinical text with SNOMED CT

Miñarro-Giménez, José Antonio; Martínez-Costa, Catalina; Karlsson, Daniel; Schulz, Stefan; Gøeg, Kirstine Rosenbeck

doi:10.1371/journal.pone.0209547

Cited by 16 publications

(14 citation statements)

References 17 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research aim to identify these codes from medical free texts or to map their domain ontological systems to standards for better interoperability. Miñarro-Giménez et al 35 represented the example of manual annotation of medical free texts with SNOMED CT codes by experts. The limitations of this approach are connected mainly with human factor and specializations of recruited experts.…”

Section: Terminology Codes Assignmentmentioning

confidence: 99%

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

Lenivtceva

Kopanitsa

2021

Methods Inf Med

View full text Add to dashboard Cite

Background The larger part of essential medical knowledge is stored as free text which is complicated to process. Standardization of medical narratives is an important task for data exchange, integration, and semantic interoperability. Objectives The article aims to develop the end-to-end pipeline for structuring Russian free-text allergy anamnesis using international standards. Methods The pipeline for free-text data standardization is based on FHIR (Fast Healthcare Interoperability Resources) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) to ensure semantic interoperability. The pipeline solves common tasks such as data preprocessing, classification, categorization, entities extraction, and semantic codes assignment. Machine learning methods, rule-based, and dictionary-based approaches were used to compose the pipeline. The pipeline was evaluated on 166 randomly chosen medical records. Results AllergyIntolerance resource was used to represent allergy anamnesis. The module for data preprocessing included the dictionary with over 90,000 words, including specific medication terms, and more than 20 regular expressions for errors correction, classification, and categorization modules resulted in four dictionaries with allergy terms (total 2,675 terms), which were mapped to SNOMED CT concepts. F-scores for different steps are: 0.945 for filtering, 0.90 to 0.96 for allergy categorization, 0.90 and 0.93 for allergens reactions extraction, respectively. The allergy terminology coverage is more than 95%. Conclusion The proposed pipeline is a step to ensure semantic interoperability of Russian free-text medical records and could be effective in standardization systems for further data exchange and integration.

show abstract

Section: Terminology Codes Assignmentmentioning

confidence: 99%

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

Lenivtceva

Kopanitsa

2021

Methods Inf Med

View full text Add to dashboard Cite

show abstract

“…Quantitative morphologic measurements of the arterial vessel wall and plaques based on MRVWI have been proven to have good reproducibility ( Mandell et al, 2017 ; Saba et al, 2018 ; Zhang et al, 2018 ) and suggested to be imaging markers to monitor the progression and regression of ischemic stroke during medical management or drug development ( Adams et al, 2004 ; Minarro-Gimenez et al, 2018 ). However, quantitative measurements are currently of limited use in clinical practice because manual segmentation of the vessel wall and plaque is labor intensive and requires continuous training of personnel ( Qiao et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Xiong

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

PurposeTo develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI).MethodsMRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall. Two-dimensional (2D) cross-sectional slices reconstructed from all plaques and 7 main arterial segments of 115 patients were used to build and optimize the deep learning model. The model performance was evaluated on the remaining nine-patient test set using the Dice similarity coefficient (DSC) and average surface distance (ASD).ResultsThe proposed automatic segmentation method demonstrated satisfactory agreement with the manual method, with DSCs of 93.8% for lumen contours and 86.0% for outer wall contours, which were higher than those obtained from the traditional U-Net, Attention U-Net, and Inception U-Net on the same nine-subject test set. And all the ASD values were less than 0.198 mm. The Bland–Altman plots and scatter plots also showed that there was a good agreement between the methods. All intraclass correlation coefficient values between the automatic method and manual method were greater than 0.780, and greater than that between two manual reads.ConclusionThe proposed deep learning-based automatic segmentation method achieved good consistency with the manual methods in the segmentation of arterial vessel wall and plaque and is even more accurate than manual results, hence improved the convenience of arterial morphological quantification.

show abstract

“…Other classification systems exist for annotation of veterinary clinical notes covering a much wider range of diagnoses and syndromes [ 6 , 7 ] but may also be incompletely used at point of care requiring additional search methods (such as key word matching) and manual reading to detect syndrome occurrence [ 8 ], restricting their utility for real-time surveillance. Additionally, regardless of who applies them, clinical coding systems may fall victim to inaccurate, inconsistent and incomplete coding by users [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs

et al. 2021

View full text Add to dashboard Cite

A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the ‘gastroenteric’ MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived ‘gastroenteric’ MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.

show abstract

Qualitative analysis of manual annotations of clinical text with SNOMED CT

Cited by 16 publications

References 17 publications

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

The Pipeline for Standardizing Russian Unstructured Allergy Anamnesis Using FHIR AllergyIntolerance Resource

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs

Contact Info

Product

Resources

About