Browsing by Author "Zhang, Zhan"

Now showing 1 - 5 of 5
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    Annotation and Information Extraction of Consumer-Friendly Health Articles for Enhancing Laboratory Test Reporting
    (American Medical Informatics Association, 2024-01-11) He, Zhe; Tian, Shubo; Erdengasileng, Arslan; Hanna, Karim; Gong, Yang; Zhang, Zhan; Luo, Xiao; Lustria, Mia Liza A.; Engineering Technology, Purdue School of Engineering and Technology
    Viewing laboratory test results is patients' most frequent activity when accessing patient portals, but lab results can be very confusing for patients. Previous research has explored various ways to present lab results, but few have attempted to provide tailored information support based on individual patient's medical context. In this study, we collected and annotated interpretations of textual lab result in 251 health articles about laboratory tests from AHealthyMe.com. Then we evaluated transformer-based language models including BioBERT, ClinicalBERT, RoBERTa, and PubMedBERT for recognizing key terms and their types. Using BioPortal's term search API, we mapped the annotated terms to concepts in major controlled terminologies. Results showed that PubMedBERT achieved the best F1 on both strict and lenient matching criteria. SNOMED CT had the best coverage of the terms, followed by LOINC and ICD-10-CM. This work lays the foundation for enhancing the presentation of lab results in patient portals by providing patients with contextualized interpretations of their lab results and individualized question prompts that they can, in turn, refer to during physician consults.
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    Attention Mechanism with BERT for Content Annotation and Categorization of Pregnancy-Related Questions on a Community Q&A Site
    (IEEE, 2020-12) Luo, Xiao; Ding, Haoran; Tang, Matthew; Gandhi, Priyanka; Zhang, Zhan; He, Zhe; Engineering Technology, School of Engineering and Technology
    In recent years, the social web has been increasingly used for health information seeking, sharing, and subsequent health-related research. Women often use the Internet or social networking sites to seek information related to pregnancy in different stages. They may ask questions about birth control, trying to conceive, labor, or taking care of a newborn or baby. Classifying different types of questions about pregnancy information (e.g., before, during, and after pregnancy) can inform the design of social media and professional websites for pregnancy education and support. This research aims to investigate the attention mechanism built-in or added on top of the BERT model in classifying and annotating the pregnancy-related questions posted on a community Q&A site. We evaluated two BERT-based models and compared them against the traditional machine learning models for question classification. Most importantly, we investigated two attention mechanisms: the built-in self-attention mechanism of BERT and the additional attention layer on top of BERT for relevant term annotation. The classification performance showed that the BERT-based models worked better than the traditional models, and BERT with an additional attention layer can achieve higher overall precision than the basic BERT model. The results also showed that both attention mechanisms work differently on annotating relevant content, and they could serve as feature selection methods for text mining in general.
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    Biostatistics and Health Data Science, School of Medicine
    (JMIR, 2021-11-25) Zhang, Zhan; Kmoth, Lukas; Luo, Xiao; He, Zhe; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Personal clinical data, such as laboratory test results, are increasingly being made available to patients via patient portals. However, laboratory test results are presented in a way that is difficult for patients to interpret and use. Furthermore, the indications of laboratory test results may vary among patients with different characteristics and from different medical contexts. To date, little is known about how to design patient-centered technology to facilitate the interpretation of laboratory test results. Objective: The aim of this study is to explore design considerations for supporting patient-centered communication and comprehension of laboratory test results, as well as discussions between patients and health care providers. Methods: We conducted a user-centered, multicomponent design research consisting of user studies, an iterative prototype design, and pilot user evaluations, to explore design concepts and considerations that are useful for supporting patients in not only viewing but also interpreting and acting upon laboratory test results. Results: The user study results informed the iterative design of a system prototype, which had several interactive features: using graphical representations and clear takeaway messages to convey the concerning nature of the results; enabling users to annotate laboratory test reports; clarifying medical jargon using nontechnical verbiage and allowing users to interact with the medical terms (eg, saving, favoriting, or sorting); and providing pertinent and reliable information to help patients comprehend test results within their medical context. The results of a pilot user evaluation with 8 patients showed that the new patient-facing system was perceived as useful in not only presenting laboratory test results to patients in a meaningful way but also facilitating in situ patient-provider interactions. Conclusions: We draw on our findings to discuss design implications for supporting patient-centered communication of laboratory test results and how to make technology support informative, trustworthy, and empathetic.
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    Hands-Free Electronic Documentation in Emergency Care Work Through Smart Glasses
    (Springer, 2022-02) Zhang, Zhan; Luo, Xiao; Harris, Richard; George, Susanna; Finkelstein, Jack; Computer Information and Graphics Technology, School of Engineering and Technology
    As U.S. healthcare system moves towards digitization, Electronic Health Records (EHRs) are increasingly adopted by medical providers. However, EHR documentation is not only time-consuming but also difficult to complete in real-time, leading to delayed, missing, or erroneous data entry. This challenge is more evident in time-critical and hands-busy clinical domains, such as Emergency Medical Services (EMS). In recent years, smart glasses have gained momentum in supporting various aspects of clinical care. However, limited research has examined the potential of smart glasses in automating electronic documentation during fast-paced medical work. In this paper, we report the design, development, and preliminary evaluations of a novel system combining smart glasses and EHRs and leveraging natural language processing (NLP) techniques to enable hands-free, real-time documentation in the context of EMS care. Although optimization is needed, our system prototype represents a substantive departure from the status quo in the documentation technology for emergency care providers, and has a high potential to enable real-time documentation while accounting for care providers’ cognitive and physical constraints imposed by the time-critical medical environment.
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    Pregnancy-Related Information Seeking in Online Health Communities: A Qualitative Study
    (Springer, 2021) Lu, Yu; Zhang, Zhan; Min, Katherine; Luo, Xiao; He, Zhe; Engineering Technology, School of Engineering and Technology
    Pregnancy often imposes risks on women's health. Consumers are increasingly turning to online resources (e.g., online health communities) to look for pregnancy-related information for better care management. To inform design opportunities for online support interventions, it is critical to thoroughly understand consumers' information needs throughout the entire course of pregnancy including three main stages: pre-pregnancy, during-pregnancy, and postpartum. In this study, we present a content analysis of pregnancy-related question posts on Yahoo! Answers to examine how they formulated their inquiries, and the types of replies that information seekers received. This analysis revealed 14 main types of information needs, most of which were "stage-based". We also found that peers from online health communities provided a variety of support, including affirmation of pregnancy, opinions or suggestions, health information, personal experience, and reference to health providers' service. Insights derived from the findings are drawn to discuss design opportunities for tailoring informatics interventions to support consumers' information needs at different pregnancy stages.