阿尔茨海默病的早期检测:言语分析的重要性

Kritesh Rauniyar, Shuvam Thakur, Aayush Nevatia, P. G. Shambharkar
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引用次数: 0

摘要

全世界有近5000万人患有阿尔茨海默病(AD),这是一种神经系统疾病。目前市场上还没有获得许可的药物可以治疗阿尔茨海默病或阻止其发展。然而,有一些治疗方法可以在早期阶段帮助调节AD。这说明早期诊断的必要性。阿尔茨海默症的一个显著症状是患者的认知能力下降。在日常琐事中,有迹象表明口译或说话能力下降。因此,自然语言处理可以成为分析患者言语的有用方法。由于计算机科学领域的快速发展,我们可以使用NLP来处理这些AD患者的语音摘录。NLP有很大的潜力帮助患有精神疾病的人得到更好的照顾。该研究采用了各种具有集成学习器的机器学习模型和深度学习模型进行比较分析,为阿尔茨海默病的进一步研究和进展设定了适当的基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Detection of Alzheimer's Disease: The Importance of Speech Analysis
Almost 50 million individuals throughout the world suffer from Alzheimer's disease (AD), a disease of the nervous system. There are no licensed medications on the market right now that can treat AD or halt its development. There are, however, treatments available that can help mediate AD in earlier stages. This demonstrates the necessity of early diagnosis. One of the notable symptoms of Alzheimer's can be in the patient's cognitive abilities. In daily chores, there is an indication of a diminished capacity for interpreting or producing speech. As a result, natural language processing can be a useful method for analyzing patient speech. Due to the rapid advancements in the field of computer science, we can use NLP to process these speech extracts from AD patients. NLP has a great deal of potential to help individuals who are suffering from mental illnesses receive better care. The study employs various Machine Learning models with ensemble learners and Deep Learning models for a comparative analysis to set a proper baseline for further research and advancements in the detection of Alzheimer's disease.
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