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.
In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization.
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.
Irregularly sampled time series data are common in a variety of fields.
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).
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.
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.
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis.
Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans.
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.
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.