Natural language processing and modeling of clinical disease trajectories across brain disorders
Natural language processing and modeling of clinical disease trajectories across brain disorders
复制标题
跨脑部疾病的临床疾病轨迹的自然语言处理和建模
DOI：
10.1101/2022.09.22.22280158
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发表时间：
2022
影响因子：
--
通讯作者：
M. Swertz
中科院分区：
文献类型：
--
作者：
Nienke J Mekkes;Minke Groot;S. Wehrens;Eric Hoekstra;Megan K Herbert;M. Brummer;Dennis D Wever;Netherlands Neurogenetics;Database Consortium;Bart J. L. Eggen;A. Rozemuller;Inge Huitinga;I. Holtman;Shared first;Jörg Hamann;E. Boddeke;M. Swertz
包括神经退行性疾病和精神疾病在内的脑部疾病通常难以诊断和研究，这是由于临床和病理异质性、疾病之间临床表现的重叠以及频繁的合并症、篡改药物开发和基础研究。因此，显然需要数据驱动的方法来解开这些复杂的疾病。在这里，我们建立了一个计算管道来处理来自捐赠者的临床总结，这些捐赠者患有由荷兰脑银行进行神经病理学诊断的各种脑部疾病。首先，我们确定并定义了90个交叉障碍的体征和症状，包括认知、运动、感觉、精神和一般领域。其次，我们训练和优化了自然语言处理（NLP）模型，以识别来自NBB捐赠者的大量临床总结的单个句子中的这些体征和症状，从而产生时间疾病轨迹。第三，我们研究了罕见和复杂痴呆、α-突触核蛋白病、额颞叶痴呆亚型和精神疾病的时间表现和生存情况。最后，我们训练了一个递归神经网络来预测神经病理诊断。总之，这种综合方法产生了一种非常独特的资源，可以促进交叉疾病的研究。
Brain disorders, including neurodegenerative diseases, and mental illnesses, are often difficult to diagnose and study due to clinical and pathological heterogeneity, overlap in clinical manifestations between disorders, and frequent comorbidities, tampering drug development and fundamental research. Hence, there is a clear need for data-driven approaches to disentangle these complex disorders. Here, we established a computational pipeline to process clinical summaries from donors with a wide range of brain disorders that were neuro-pathologically diagnosed by the Netherlands Brain Bank. First, we identified and defined 90 cross-disorder signs and symptoms within cognitive, motor, sensory, psychiatric, and general domains. Second, we trained and optimized natural language processing (NLP) models to identify these signs and symptoms in individual sentences of the extensive clinical summaries from donors of the NBB, resulting in temporal disease trajectories. Third, we studied the temporal manifestation and survival profiles across rare and complex dementias, alpha-synucleinopathies, frontotemporal dementia subtypes, and mental illnesses. Lastly, we trained a recurrent neural network to predict the Neuropathological Diagnosis. Taken together, this integrated approach resulted in a highly unique resource that can facilitate research into cross-disorder symptomatology.
影响因子：
2.3
作者：
Ivleva，Elena I.;Turkozer，Halide B.;Sweeney，John A.
通讯作者：
Sweeney，John A.