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Skip search results from other journals and go to results- 2 JMIR Formative Research
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The initialization phase resulted in an initial fact schema and corresponding annotation guideline, documented in a web-based, versioned collaboration platform (Confluence, Atlassian). For this phase, the annotation guideline development dataset was used.
The goal of the next phase, quality improvement, was to iteratively improve the fact schema and systematically revise the annotation guideline.
J Med Internet Res 2025;27:e68427
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Our goal is to evaluate whether relieving the emphasis on entity boundary improves annotation speed while maintaining the overall quality of the produced labels. Thus, we compared the traditional (boundary-strict) annotation method against 2 proposed boundary-free approaches: lenient span and point annotation. Figure 1 presents a comparative example of each annotation method.
Traditional annotation requires precise annotation of each NE’s exact start and end positions.
JMIR Med Inform 2024;12:e59680
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Indeed, one study of NER annotation speed found it can take between 10 and 30 seconds per sentence for experts to annotate named entities [8]. The gold-standard annotated Bio Semantics corpus is composed of 163,219 sentences, which implies an optimal annotation time of over 11 weeks at 40 hours per week (453.39 h) [20].
JMIR AI 2024;3:e52095
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Biomedical text analysis is commonly burdened by the need for manual data review and annotation, which is costly and time-consuming. Artificial intelligence (AI) tools, including large language models (LLMs) such as Chat GPT (Open AI) [1], could reduce this burden by allowing scientists to leverage vast amounts of text data (including medical records and public data) with short written prompts as annotation instructions [2].
J Med Internet Res 2024;26:e52499
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Although prior studies have used NLP to identify problematic opioid use from EHRs [21-26], few have described an annotation process and none have reported documentation patterns for OUD-relevant information within clinical notes.
JMIR Ment Health 2024;11:e53366
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This was used to initiate the development of annotation guidelines. These guidelines were drafted to ensure consistent annotation by multiple annotators. Upon extraction, these documents were preannotated with pain terms (labeled as a mention of pain, as seen in Table 3) from the lexicon and loaded into an annotation tool, Med CAT [19], for manual verification and annotation of these mentions of pain.
JMIR Form Res 2023;7:e45849
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Development of a COVID-19–Related Anti-Asian Tweet Data Set: Quantitative Study
In the next sections, we will explain the details of data collection, data annotation, and content analytics. We will also present and compare the results of the experiment with the ML models, which are designed to detect stigmatizing language against people of Asian descent.
As already discussed, creating such a data set is challenging for various reasons. We devised an iterative method for selecting a final set of tweets that we manually annotated.
JMIR Form Res 2023;7:e40403
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Our work provides a formalized approach for developing annotation data for suicide and social media data. We have discussed the implications of this research as they relate to the development of rigorous and validated frameworks for assessing mental health on the web.
JMIR Ment Health 2021;8(11):e24471
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TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations
Similarly, to the annotation of the ADE corpus, the Arizona disease corpus (AZDC) annotation guidelines [41] focused on the annotation of the diseases, also covering syndromes, illnesses, and disorders.
JMIR Public Health Surveill 2017;3(2):e24
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