Predictive and exposome analytics: A case study of asthma exacerbation management

Alkobaisi, Shayma; Bae, Wookeun; Horak, Matthew; Narayanappa, Sada; Lee, Jongwon; AbuKhousa, Eman; Park, Choon-Sik; Bae, Da Jung

doi:10.3233/ais-190540

Cited by 7 publications

(8 citation statements)

References 68 publications

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“…In this study, we base our forecasts on a simplified version of the individual-based asthma risk zoning method proposed in [ 52 ]. The method allows for classification of a patient’s condition into several zones based on the patient’s own historical distribution of PEFR values.…”

Section: Methodsmentioning

confidence: 99%

“…The objective of the inference engine will be to predict when a patient is in danger of entering the risk zone, which is understood to be a potential medical emergency where severe airway narrowing is likely to occur and immediate action may be necessary. Therefore, as discussed in [ 52 ] it is important for doctors and patients together to analyze the patient’s PEFR distribution as it relates to the patient’s actual health condition and take care to modify the

cutoff as necessary. Accordingly, the system is able to evaluate the susceptibility of each individual to an asthma exacerbation, on an individual basis as the value of the exposure to variables changes over time.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

Bae

Alkobaisi

Horak

et al. 2022

Life

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The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

Bae

Alkobaisi

Horak

et al. 2022

Life

View full text Add to dashboard Cite

show abstract

“…In this study, we applied our forecasts to a simplified version of individual-specific exacerbation zoning method proposed by Alkobaisi in 2019 [ 23 ] that classifies a patient’s exacerbation level based on their own historical distribution of PEFR values.…”

Section: Methodsmentioning

confidence: 99%

“…Using machine learning-based inference engines, we attempted to predict the instances at which the patient had a risk of entering the red zone, which would likely pose a medical emergency. As described by Alkobaisi [ 23 ], it is important for doctors and patients to analyze each patient’s PEFR distribution, as it relates to their personal health condition, and actively aim to modify the 80/20 cutoff on an individual basis.…”

Section: Methodsmentioning

confidence: 99%

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data

et al. 2021

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Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recently, various machine-learning algorithms were introduced to provide mechanistic insights about the heterogeneity of clustered data pertaining to the symptoms of each asthma patient and potential environmental risk factors. However, there is limited information on the performance of these machine learning tools. In this study, we compared the performance of ten machine-learning techniques. Using an advanced method of imbalanced sampling (IS), we improved the performance of nine conventional machine learning techniques predicting the association between exposure level to indoor air quality and change in patients’ peak expiratory flow rate (PEFR). We then proposed a deep learning method of transfer learning (TL) for further improvement in prediction accuracy. Our selected final prediction techniques (TL1_IS or TL2-IS) achieved a balanced accuracy median (interquartile range) of 66(56~76) % for TL1_IS and 68(63~78) % for TL2_IS. Precision levels for TL1_IS and TL2_IS were 68(62~72) % and 66(62~69) % while sensitivity levels were 58(50~67) % and 59(51~80) % from 25 patients which were approximately 1.08 (accuracy, precision) to 1.28 (sensitivity) times increased in terms of performance outcomes, compared to NN_IS. Our results indicate that the transfer machine learning technique with imbalanced sampling is a powerful tool to predict the change in PEFR due to exposure to indoor air including the concentration of particulate matter of 2.5 μm and carbon dioxide. This modeling technique is even applicable with small-sized or imbalanced dataset, which represents a personalized, real-world setting.

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“…Therefore, real-time monitoring of IAQ in buildings is crucial to detect unhealthy situations. IAQ is critical people spending most of their lives indoors, e.g., the elderly, disabled, infants, and chronic patients, and low air quality poses a major health threat to these individuals [6,21,37].…”

Section: Introductionmentioning

confidence: 99%

A low-cost air quality monitoring system based on Internet of Things for smart homes

Taştan

2022

AIS

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Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.

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Predictive and exposome analytics: A case study of asthma exacerbation management

Cited by 7 publications

References 68 publications

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data

A low-cost air quality monitoring system based on Internet of Things for smart homes

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