Search Results for author: Tanvi Banerjee

Found 16 papers, 4 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.

Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization

no code implementations2 Apr 2022 Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita

In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization.

Abstractive Text Summarization

Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature

1 code implementation30 Mar 2022 Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

While transformer-based encoder-decoder models in a vanilla source document-to-summary setting have been extensively studied for abstractive summarization in different domains, their major limitations continue to be entity hallucination (a phenomenon where generated summaries constitute entities not related to or present in source article(s)) and factual inconsistency.

Abstractive Text Summarization Document Summarization

Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits

no code implementations24 Nov 2020 Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Arvind Subramaniam, Daniel M. Abrams, Gary K. Nave Jr., Nirmish Shah

Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.

COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study

no code implementations20 Nov 2020 Amanuel Alambo, Swati Padhee, Tanvi Banerjee, Krishnaprasad Thirunarayan

This has been exacerbated by social isolation during the pandemic and the social stigma associated with mental health and substance use disorders, making people reluctant to share their struggles and seek help.

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.

Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles

no code implementations3 Nov 2020 Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis.

Abstractive Text Summarization Document Summarization +7

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.

Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures

no code implementations31 Jan 2019 William Romine, Tanvi Banerjee, Garrett Goodman

We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring.

Sleep Quality

Early hospital mortality prediction using vital signals

1 code implementation18 Mar 2018 Reza Sadeghi, Tanvi Banerjee, William Romine

In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients.

Mortality Prediction

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

no code implementations16 Oct 2017 Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter.

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