Application of Statistical Analysis and Machine Learning to Identify Infants’ Abnormal Suckling Behavior

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Phuong Truong;Erin Walsh;Vanessa P. Scott;Michelle Leff;Alice Chen;James Friend
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引用次数: 0

Abstract

Objective: Identify infants with abnormal suckling behavior from simple non-nutritive suckling devices.Background: While it is well known breastfeeding is beneficial to the health of both mothers and infants, breastfeeding ceases in 75 percent of mother-child dyads by 6 months. The current standard of care lacks objective measurements to screen infant suckling abnormalities within the first few days of life, a critical time to establish milk supply and successful breastfeeding practices.Materials and Methods: A non-nutritive suckling vacuum measurement system, previously developed by the authors, is used to gather data from 91 healthy full-term infants under thirty days old. Non-nutritive suckling was recorded for a duration of sixty seconds. We establish normative data for the mean suck vacuum, maximum suck vacuum, suckling frequency, burst duration, sucks per burst, and vacuum signal shape. We then apply computational methods (Mahalanobis distance, KNN) to detect anomalies in the data to identify infants with abnormal suckling. We finally provide case studies of healthy newborn infants and infants diagnosed with ankyloglossia.Results: In a series of case evaluations, we demonstrate the ability to detect abnormal suckling behavior using statistical analysis and machine learning. We evaluate cases of ankyloglossia to determine how oral dysfunction and surgical interventions affect non-nutritive suckling measurements.Conclusions: Statistical analysis (Mahalanobis Distance) and machine learning [K nearest neighbor (KNN)] can be viable approaches to rapidly interpret infant suckling measurements. Particularly in practices using the digital suck assessment with a gloved finger, it can provide a more objective, early stage screening method to identify abnormal infant suckling vacuum. This approach for identifying those at risk for breastfeeding complications is crucial to complement complex emerging clinical evaluation technology.Clinical Impact: By analyzing non-nutritive suckling using computational methods, we demonstrate the ability to detect abnormal and normal behavior in infant suckling that can inform breastfeeding intervention pathways in clinic.Clinical and Translational Impact Statement: The work serves to shed light on the lack of consensus for determining appropriate intervention pathways for infant oral dysfunction. We demonstrate using statistical analysis and machine learning that normal and abnormal infant suckling can be identified and used in determining if surgical intervention is a necessary solution to resolve infant feeding difficulties.
应用统计分析和机器学习识别婴儿异常吸吮行为
目的: 通过简单的非营养性吸吮装置识别吸吮行为异常的婴儿:通过简单的非营养性吸吮装置识别吸吮行为异常的婴儿:背景:众所周知,母乳喂养有益于母亲和婴儿的健康,但 75% 的母婴家庭在婴儿 6 个月时就停止了母乳喂养。目前的护理标准缺乏客观的测量方法来筛查婴儿出生后几天内的吸吮异常,而这正是建立奶水供应和成功母乳喂养的关键时期:作者之前开发的非营养性吸吮真空测量系统用于收集 91 名出生不到 30 天的健康足月婴儿的数据。非营养性吸吮的记录时间为六十秒。我们建立了平均吸吮真空度、最大吸吮真空度、吸吮频率、爆发持续时间、每次爆发吸吮次数和真空信号形状的标准数据。然后,我们采用计算方法(马哈罗诺比距离、KNN)检测数据中的异常情况,以识别吸吮异常的婴儿。最后,我们提供了健康新生儿和被诊断为无吮吸症婴儿的案例研究:在一系列病例评估中,我们展示了利用统计分析和机器学习检测异常吸吮行为的能力。我们对口颌畸形病例进行了评估,以确定口腔功能障碍和手术干预对非营养性吸吮测量的影响:统计分析(Mahalanobis Distance)和机器学习[K nearest neighbor (KNN)]是快速解释婴儿吸吮测量结果的可行方法。特别是在使用戴手套的手指进行数字吸吮评估的实践中,它可以提供一种更客观的早期筛查方法,以识别异常的婴儿真空吸吮。这种识别母乳喂养并发症高危人群的方法对于补充复杂的新兴临床评估技术至关重要:通过使用计算方法分析非营养性吸吮,我们展示了检测婴儿吸吮中异常和正常行为的能力,这可以为临床中的母乳喂养干预路径提供依据:这项工作有助于阐明在确定婴儿口腔功能障碍的适当干预途径方面缺乏共识的问题。我们利用统计分析和机器学习证明,可以识别正常和异常的婴儿吸吮行为,并用于确定手术干预是否是解决婴儿喂养困难的必要方案。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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