Search Results for author: Amanuel Alambo

Found 11 papers, 1 papers with code

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

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

no code implementations9 Apr 2021 Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth

In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).

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.

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 +6

Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak

no code implementations30 Jul 2020 Amanuel Alambo, Manas Gaur, Krishnaprasad Thirunarayan

Further, apart from providing informative content to the public, the incessant media coverage of COVID-19 crisis in terms of news broadcasts, published articles and sharing of information on social media have had the undesired snowballing effect on stress levels (further elevating depression and drug use) due to uncertain future.

Informativeness Semantic Parsing +1

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

no code implementations18 Aug 2019 Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, K. Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth

Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.

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