Search Results for author: William L. Romine

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

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.

Predicting Early Indicators of Cognitive Decline from Verbal Utterances

no code implementations19 Nov 2020 Swati Padhee, Anurag Illendula, Megan Sadler, Valerie L. Shalin, Tanvi Banerjee, Krishnaprasad Thirunarayan, William L. Romine

Our experiments show that a combination of contextual and psycholinguistic features extracted by a Support Vector Machine improved distinguishing the verbal utterances of elderly controls, people with MCI, possible AD, and probable AD.

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.