Characteristics of Patient Records and Clinical Corpora

Dalianis, Hercules

doi:10.1007/978-3-319-78503-5_4

Cited by 25 publications

(25 citation statements)

References 27 publications

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“…The data configuration had structured data (i.e., gender, birth date, start date, end date, and icd-10 code) and only one free-text data field for the discharge summary. The texts from both datasets shared some already known characteristics related to clinical narratives in general [ 49 ], such as uncertainty, redundancy (often due to copy and paste), high use of acronyms and medical jargon, misspellings, fragmented sentences, punctuation issues, and incorrect lower- and uppercasing. Some examples of text are presented in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

et al. 2022

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Background The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. Methods In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. Results This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores. Conclusion The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

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Section: Methodsmentioning

confidence: 99%

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

et al. 2022

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“…First, clinical texts are information-dense and communicate concepts between physicians with as few words as possible. Second, because of the high workload, there is a higher chance of spelling and grammatical errors and incomplete sentences during creation 11 . Finally, health-care professionals tend to use many abbreviations in their writing, which necessitates word disambiguation before extracting features, e.g., "MR" can refer to the English-language general word mister (Mr.), mental retardation, magnetic resonance, or mitral regurgitation, depending on the context 12 .…”

Section: What Is Clinical Nlp?mentioning

confidence: 99%

Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence

Khosravi

Rouzrokh

Erickson

2022

Journal of Bone and Joint Surgery

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Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinical notes, surgical notes, and medication instructions), and researchers need data to be in computable form (structured) to extract meaningful relationships involving variables that can influence patient outcomes. Clinical natural language processing (NLP) is the field of extracting structured data from unstructured text documents in EHRs. Clinical text has several characteristics that mandate the use of special techniques to extract structured information from them compared with generic NLP methods. In this article, we define clinical NLP models, introduce different methods of information extraction from unstructured data using NLP, and describe the basic technical aspects of how deep learning-based NLP models work. We conclude by noting the challenges of working with clinical NLP models and summarizing the general steps needed to launch an NLP project.

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“…In [9], the differences between medical and standard texts are presented and the problems that may arise when extracting information from medical texts are pointed out. The characteristics of clinical reports from the corpus of medical reports in Sweden taken in 2014-15, are presented in [10]. Normalization is the first step used in the classification and labeling of the medical terms.…”

Section: Related Workmentioning

confidence: 99%

Automated labeling of terms in medical reports in Serbian

2020

Turk J Elec Eng & Comp Sci

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Nowadays, many electronic health reports (EHRs) are stored daily. They consist of the structured part and of an unstructured section written in natural language. Due to the limited time for medical examination, EHRs are short reports which often contain errors and abbreviations. Therefore it is a challenge to process an EHR and extract knowledge from this part of the text for different purposes. This paper compares the results of three proposed methods for automatic labeling of medical terms in unstructured parts of EHRs. All words are categorized as words within the medical domain (symptoms, diagnoses, therapies, anatomy, specialties etc.) and those beyond the medical domain (numbers, places, stop words etc.). The first method is based on dictionaries of medical terms, the second on the training set, and the third on the training set and rules. The results of application of different methodologies to reduce a word to its basic form (pure, prefix, stem) are given for each of the methods. The paper shows that in labeling medical terms, the methods based on medical dictionaries (diagnosis, symptoms, medications etc.) do not produce best results, therefore it is better to use manually annotated part of the data set as a model. A significant number of words (17.36%) in medical reports are abbreviations and errors, so for better results, we should focus on creating rules to solve this problem. Better results are obtained for supervised methods compared to the dictionary-based method (with relative improvement of 42.82%). The inclusion of the algorithm for processing errors and abbreviations increased the results (with a relative improvement of 4.21%) and gave the largest F1-measure (0.9082). The advantage of the proposed method is that the use of rules for processing errors and abbreviations provides good results regardless of how the word is reduced to its basic form.

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Characteristics of Patient Records and Clinical Corpora

Cited by 25 publications

References 27 publications

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence

Automated labeling of terms in medical reports in Serbian

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