Ruoting Li

Fitts Ind & Syst Eng Grad Temp

Works (4)

Updated: May 24th, 2024 05:00

2024 journal article

Temporal pattern mining for knowledge discovery in the early prediction of septic shock

PATTERN RECOGNITION, 151.

By: R. Li*, J. Agor* & O. Ozaltin n 

author keywords: Temporal pattern mining; Feature selection; Electronic health records; Knowledge discovery; Sepsis
Sources: Web Of Science, NC State University Libraries
Added: May 20, 2024

2023 article

Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses

Li, R., Tobey, M., Mayorga, M. E., Caltagirone, S., & Ozaltin, O. Y. (2023, February 22). M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT, Vol. 2.

By: R. Li n, M. Tobey n, M. Mayorga n , S. Caltagirone & O. Ozaltin n 

author keywords: human trafficking; massage businesses; online customer reviews; Natural Language Processing; ensemble learning
TL;DR: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: March 20, 2023

2022 article

Interpretable models for the automated detection of human trafficking in illicit massage businesses

Tobey, M., Li, R., Ozaltin, O. Y., Mayorga, M. E., & Caltagirone, S. (2022, August 28). IISE TRANSACTIONS, Vol. 8.

By: M. Tobey n, R. Li n, O. Ozaltin n , M. Mayorga n  & S. Caltagirone

author keywords: Human trafficking; illicit massage; interpretable machine learning; risk scores; decision trees
TL;DR: This work uses findings from primary stakeholder interviews with law enforcement, regulatory bodies, legitimate massage practitioners, and subject-matter experts from nonprofit organizations to identify data sources and potential indicators of illicit massage businesses (IMBs). (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: September 26, 2022

2022 journal article

Septic shock prediction and knowledge discovery through temporal pattern mining

ARTIFICIAL INTELLIGENCE IN MEDICINE, 132.

By: J. Agor *, R. Li n & O. Ozaltin n 

author keywords: Temporal pattern mining; Sepsis; Electronic health records; Prediction; Pattern selection
MeSH headings : Critical Care; Electronic Health Records; Humans; Knowledge Discovery; Sepsis / diagnosis; Sepsis / therapy; Shock, Septic / diagnosis; Shock, Septic / therapy
TL;DR: This framework proposes a framework to find a small number of relevant temporal patterns in electronic health records for the early prediction of septic shock and finds that model-based feature selection approaches PST3 and PST4 yield the best prediction performance among these techniques. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: October 17, 2022

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