Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI:10.3389/fninf.2024.1378281
Alba Gómez-Valadés, Rafael Martínez-Tomás, Sara García-Herranz, Atle Bjørnerud, Mariano Rincón
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Abstract

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system's ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2  = 0.830, only below bagging, F2BAG  = 0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system's versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.

在人群筛查中通过神经心理学测试及早发现轻度认知障碍:整合本体论和机器学习的决策支持系统。
用于检测轻度认知障碍(MCI)的机器学习(ML)方法正在逐渐普及,以管理大量处理过的信息。然而,ML 算法的黑箱性质和数据的异质性可能会导致不同研究的解释各不相同。为了避免这种情况,在本提案中,我们提出了一个决策支持系统的设计方案,该系统将使用语义网络规则语言(SWRL)表示的机器学习模型集成到一个具有神经心理测试专业知识的本体(NIO 本体)中。该系统检测 MCI 受试者的能力在一个包含 520 项西班牙语神经心理学评估的数据库中进行了评估,并与其他成熟的 ML 方法进行了比较。使用 F2 系数来最小化假阴性,结果表明该系统的性能与其他成熟的 ML 方法类似(F2TE2 = 0.830,仅低于袋式方法,F2BAG = 0.832),同时还表现出其他重要属性,如解释能力和数据标准化,由于本体论部分的存在,该系统可实现通用框架。另一方面,该系统的多功能性和易用性也通过三个附加用例得到了体现:即使采集阶段不完整(病例记录有缺失值),也能对新病例进行评估;将新数据库纳入集成系统;利用本体功能将不同领域联系起来。这使得该系统成为支持医生和神经心理学家进行基于人群的筛查以早期发现 MCI 的有用工具。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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