Birmingham text annotations12/3/2023 ![]() Raters first reviewed concepts in the neuro-ontology of neurological concepts and then were instructed to find all neurological concepts in the neurology notes. Annotator 1 (A1) was a senior neurologist, Annotator 2 (A2) was a pre-medical student majoring in neuroscience, and Annotator 3 (A3) was a third-year medical student. ![]() Three annotators participated in the research. In addition to the agreement between human annotators, we examine the agreement between human annotators and a machine annotator based on a convolutional neural network. In this paper, we examine inter-rater agreement for text-span identification of neurological concepts in notes from electronic health records. ![]() Other deep learning approaches, including neural networks based on bidirectional encoder representations from transformers (BERT), show promise for automated clinical concept extraction. developed a convolutional neural network that matches input phrases to concepts in the Human Phenotype Ontology with high accuracy. Neural networks are being used for concept recognition with increasing success. Rule-based systems such as cTAKES and MetaMap generally have accuracy and recall between 0.38 and 0.66. Approaches to high throughput clinical concept extraction have included rule-based systems, traditional machine learning algorithms, deep learning algorithms, and hybrid methods that combine algorithms. The goal of high throughput phenotyping is to use natural language processing (NLP) to automate the annotation process. Identified sources of disagreement between coders included human errors (lack of applicable medical knowledge, lack of recognition of abbreviations for concepts, and general carelessness), annotation guideline flaws (under specified and unclear guidelines), ontology flaws (polysemy of coded concepts), interface term issues (inconsistent categorization of clinical jargon), and language issues (interpretation difficulties due to use of ellipsis, anaphora, paraphrasing, and other linguistic concepts). Another study of SNOMED CT coding of ophthalmology notes yielded low levels of inter-rater agreement ranging from 33 to 64 percent. A study conducted on the agreement for SNOMED CT codes between coders from three professional coding companies yielded about 50 percent agreement for exact matches with slightly higher agreement when adjusted for near matches. Agreement between human raters for annotation in clinical text is often low. This is a slow and error-prone process for human annotators. In this example, an annotator highlights the term ataxic, then maps it to the concept ataxia, and the UMLS CUI C0004134. Patient movements were ataxic ⇒ ataxia ⇒ UMLS CUI: C0004134 free text ⇒ clinical concept ⇒ machine readable code This is a two-step process that involves identifying appropriate text spans in narratives and then mapping the text spans to target concepts in an ontology. Clinical phenotyping of patients involves the conversion of free text into clinical concepts from an ontology. The signs and symptoms of patients (part of the patient phenotype) are generally recorded as free text in progress notes, admission notes, and discharge summaries. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators.Įxtracting medical concepts from electronic health records is key to precision medicine. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators, but one that was lower than human inter-rater agreement. Inter-rater agreement between the three annotators was high for text span and category label. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. Extracting clinical concepts from free text is tedious and time-consuming. Once extracted, signs and symptoms can be made computable by mapping to clinical concepts in an ontology. The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine.
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