Automated lead toxicity prediction using computational modelling framework.

IF 4.7 3区 医学 Q1 MEDICAL INFORMATICS
Health Information Science and Systems Pub Date : 2023-11-20 eCollection Date: 2023-12-01 DOI:10.1007/s13755-023-00257-4
Priyanka Chaurasia, Sally I McClean, Abbas Ali Mahdi, Pratheepan Yogarajah, Jamal Akhtar Ansari, Shipra Kunwar, Mohammad Kaleem Ahmad
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引用次数: 0

Abstract

Background: Lead, an environmental toxicant, accounts for 0.6% of the global burden of disease, with the highest burden in developing countries. Lead poisoning is very much preventable with adequate and timely action. Therefore, it is important to identify factors that contribute to maternal BLL and minimise them to reduce the transfer to the foetus. Literacy and awareness related to its impact are low and the clinical establishment for biological monitoring of blood lead level (BLL) is low, costly, and time-consuming. A significant contribution to an infant's BLL load is caused by maternal lead transfer during pregnancy. This acts as the first pathway to the infant's lead exposure. The social and demographic information that includes lifestyle and environmental factors are key to maternal lead exposure.

Results: We propose a novel approach to build a computational model framework that can predict lead toxicity levels in maternal blood using a set of sociodemographic features. To illustrate our proposed approach, maternal data comprising socio-demographic features and blood samples from the pregnant woman is collected, analysed, and modelled. The computational model is built that learns from the maternal data and then predicts lead level in a pregnant woman using a set of questionnaires that relate to the maternal's social and demographic information as the first point of testing. The range of features identified in the built models can estimate the underlying function and provide an understanding of the toxicity level. Following feature selection methods, the 12-feature set obtained from the Boruta algorithm gave better prediction results (kNN = 76.84%, DT = 74.70%, and NN = 73.99%).

Conclusion: The built prediction model can be beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that in future, the proposed methodology will become a part of a screening process to assist healthcare experts at the point of evaluating the lead toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal lead exposure; hence, it could also be possible to mitigate the infant's lead exposure by reducing transfer from the pregnant woman.

使用计算模型框架的自动铅中毒预测。
背景:铅是一种环境毒物,占全球疾病负担的0.6%,发展中国家的负担最高。只要采取适当和及时的行动,铅中毒是完全可以预防的。因此,确定导致母体BLL的因素并将其最小化以减少向胎儿的转移是很重要的。人们对其影响的认知和认识较低,血铅水平(BLL)生物监测的临床设施较少、成本高且耗时长。一个显著贡献的婴儿的BLL负荷是由母亲在怀孕期间铅转移引起的。这是婴儿接触铅的第一个途径。包括生活方式和环境因素在内的社会和人口信息是孕产妇铅暴露的关键。结果:我们提出了一种新的方法来建立一个计算模型框架,可以使用一组社会人口统计学特征来预测母亲血液中的铅毒性水平。为了说明我们提出的方法,包括社会人口特征和孕妇血液样本的产妇数据被收集、分析和建模。建立计算模型,从产妇数据中学习,然后使用一套与产妇的社会和人口统计信息相关的问卷作为第一个测试点来预测孕妇的铅水平。在建立的模型中确定的特征范围可以估计潜在的功能,并提供对毒性水平的理解。在特征选择方法中,Boruta算法得到的12个特征集的预测效果更好(kNN = 76.84%, DT = 74.70%, NN = 73.99%)。结论:所建立的预测模型有助于改善护理点,从而降低成本和风险。预计在未来,拟议的方法将成为筛选过程的一部分,以协助保健专家评估孕妇的铅毒性水平。筛查呈阳性的妇女可以得到一系列便利,包括初步咨询,然后转到保健中心作进一步诊断。可采取措施减少产妇铅接触;因此,也有可能通过减少孕妇的铅转移来减轻婴儿的铅暴露。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.30
自引率
5.00%
发文量
30
期刊介绍: Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.
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