Search Results for author: Ankita Agarwal

Found 4 papers, 1 papers with code

Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

no code implementations30 May 2023 Ankita Agarwal, Tanvi Banerjee, William L. Romine, Krishnaprasad Thirunarayan, Lingwei Chen, Mia Cajita

These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1, 200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health).

Predicting Patient Outcomes

Predicting Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing

no code implementations28 Mar 2023 Ankita Agarwal, Tanvi Banerjee, Joy Gockel, Saniya LeBlanc, Joe Walker, John Middendorf

In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions.

Feature Importance

Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure

no code implementations14 Apr 2022 Ankita Agarwal, Krishnaprasad Thirunarayan, William L. Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee

Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients.

Leveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic

1 code implementation31 Oct 2020 Ankita Agarwal, Preetham Salehundam, Swati Padhee, William L. Romine, Tanvi Banerjee

In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020, and explored topic modeling to identify the most discussed topics and themes during this period in our data set.

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