Karabacak Mert, Jain Ankita, Jagtiani Pemla, Hickman Zachary L, Dams-O'Connor Kristen, Margetis Konstantinos
Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.
School of Medicine, New York Medical College, Valhalla, New York, USA.
Neurotrauma Rep. 2024 Mar 6;5(1):203-214. doi: 10.1089/neur.2023.0102. eCollection 2024.
Traumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.
创伤性脑损伤（TBI）已从一个相对冷门的话题发展成为一个受到广泛科学关注和大众兴趣的领域。TBI研究的范围和重点已经发生了变化，研究趋势也随着公众和科学界的兴趣而改变。本研究有两个主要目标：第一，确定TBI研究中的主要主题；第二，通过评估这些主题当前的热度或衰退情况，描绘出“热门”和“冷门”感兴趣领域。热门话题在绝对数量上可能会被其他更大的TBI研究领域所掩盖，但正迅速获得关注。同样，冷门话题可能为研究人员重新审视未解决的问题提供机会。我们利用BERTopic，一种基于先进自然语言处理（NLP）的技术，来分析自1990年以来发表的TBI研究文章。这种方法通过提取代表每篇文章核心主题的独特关键词集，有助于识别关键主题。利用这些主题的概率，我们训练线性回归模型来检测随时间的趋势，识别出相关性增加（热门）或减少（冷门）的主题。此外，我们针对当前十年（2020年代）TBI研究中观察到的趋势进行了具体分析。我们的主题建模分析将42422篇文章归类为27个不同的主题。出现频率最高的10个主题是：“康复”、“TBI的分子机制”、“脑震荡”、“重复性头部撞击”、“手术干预”、“生物标志物”、“颅内压”、“创伤后神经退行性变”、“慢性创伤性脑病”和“爆炸所致TBI”，而我们的趋势分析表明，当前十年最热门的主题是“基因组学”、“性激素”和“扩散张量成像”，而热度下降的主题是“创伤后睡眠”、“感觉功能”和“高渗疗法”。本研究突出了TBI研究的动态性质，并强调了该领域重点的不断变化。我们分析的结果有助于识别新兴的感兴趣主题以及几乎没有新研究报道的领域。通过利用NLP有效地综合和分析大量与TBI相关的学术文献，我们展示了机器学习技术在理解和指导未来研究前景方面的潜力。这种方法为其他医学学科的类似分析奠定了基础，提供了深刻的见解和进一步探索的机会。
