Division for Digital Medicine and Telehealth, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria.
Department of Clinical Epidemiology, Tyrolean Federal Institute for Integrated Care, Tirol Kliniken GmbH, Innsbruck, Austria.
Stud Health Technol Inform. 2024 Aug 22;316:1709-1713. doi: 10.3233/SHTI240752.
The increasing volume of unstructured textual data in healthcare, particularly in nursing care reports, presents both challenges and opportunities for enhancing patient care and operational efficiency. This study explores the application of Latent Dirichlet Allocation (LDA) topic modeling to analyze free-text nursing narratives from inpatient stays in three different clinics, aiming to uncover the latent thematic structures within. Utilizing the R programming environment and the visualization tool LDAvis, we identified three main themes: "Patient Well-being," "Patient Mobility and Care Activities," and "Treatment and Pain Management," the latter combining two closely related but initially distinct topics due to their overlapping content. Our findings demonstrate the potential of LDA topic modeling in extracting meaningful insights from nursing narratives, which could inform patient care strategies and healthcare practices. However, the study also highlights significant challenges associated with the method, including the sensitivity to parameter settings, the lack of updates for key software packages, and concerns about reproducibility. These issues highlight the need for meticulous parameter validation and the exploration of alternative text analysis methodologies for future research. By addressing these methodological challenges and emphasizing the importance of comparative method analysis, this study contributes to the advancement of text analytics in healthcare. It opens avenues for further research aimed at developing more robust, efficient, and accessible tools for analyzing free-text data, thereby enhancing the ability of healthcare professionals to use unstructured data to improve decision making and patient outcomes.
医疗保健领域中文本数据量的不断增加，特别是在护理报告中，为提高患者护理和运营效率带来了挑战和机遇。本研究探讨了应用潜在狄利克雷分配（LDA）主题建模来分析来自三个不同诊所住院期间的自由文本护理叙述，旨在揭示其中的潜在主题结构。我们利用 R 编程环境和可视化工具 LDAvis，确定了三个主要主题：“患者健康”、“患者活动和护理活动”以及“治疗和疼痛管理”，后者将两个密切相关但最初不同的主题结合在一起，因为它们的内容重叠。我们的研究结果表明，LDA 主题建模在从护理叙述中提取有意义的见解方面具有潜力，这些见解可以为患者护理策略和医疗保健实践提供信息。然而，该研究还强调了该方法存在的一些重大挑战，包括对参数设置的敏感性、关键软件包缺乏更新以及对可重复性的关注。这些问题突出表明需要仔细验证参数，并探索替代文本分析方法，以用于未来的研究。通过解决这些方法学挑战并强调比较方法分析的重要性，本研究为医疗保健领域的文本分析的发展做出了贡献。它为进一步研究开辟了途径，旨在开发更强大、高效和易于使用的分析自由文本数据的工具，从而增强医疗保健专业人员利用非结构化数据改善决策和患者结果的能力。
