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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
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 Thermoelectric Power Factor of Bismuth Telluride During Laser Powder Bed Fusion Additive Manufacturing
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.
Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure
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
In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization.
Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature
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.
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples
Irregularly sampled time series data are common in a variety of fields.
Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits
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
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
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
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis.
Leveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic
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.
Measuring Pain in Sickle Cell Disease using Clinical Text
Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in humans.
Sleep quality prediction in caregivers using physiological signals
To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage.
Ranked #1 on
Sleep Quality
on 100 sleep nights of 8 caregivers
Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures
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.
Early hospital mortality prediction using vital signals
In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients.
Ranked #1 on
Mortality Prediction
on MIMIC-III
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
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.
