Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

Tang, Minmeng; Li, Xinwei

doi:10.1016/j.atmosenv.2024.120733

Atmospheric Environment

2024

DOI: 10.1016/j.atmosenv.2024.120733

|View full text |Cite

Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

Minmeng Tang,

Xinwei Li

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach

Maekawa,

de Sá Martins,

Nakamura

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important. Gaining insights into these factors through an explainable, probabilistic approach can enhance improved management. A Bayesian network (BN) is well-suited for this purpose. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A BN model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The BN model was also compared to four machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. The model demonstrated generalisation abilities while being less complex and more explainable than other machine learning models. Key factors associated with recurrence of anxiety symptomatology were: “Not being able to stop or control worrying”; “Becoming easily annoyed or irritable”; “Trouble relaxing”; and “depressive symptomatology severity”. These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology.

show abstract

Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach

Maekawa,

de Sá Martins,

Nakamura

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

Cited by 1 publication

References 40 publications

Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach

Understanding Risk Factors of Recurrent Anxiety Symptomatology in an Older Population with Mild to Severe Depressive Symptoms: A Bayesian Approach

Contact Info

Product

Resources

About