ArticleComputing in cardiology2015
A Visualization of Evolving Clinical Sentiment Using Vector Representations of Clinical Notes.
Article in Computing in cardiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Article
- Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.Scientific reports · 2023Article
- Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms.Annals of translational medicine · 2022Article
- Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials.Frontiers in digital health · 2022Article
- Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.Computational and mathematical methods in medicine · 2021Article
- Efficient goal attainment and engagement in a care manager system using unstructured notes.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Assessing the Readability of Freely Available ICU Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.BMC medical informatics and decision making · 2018Article
- Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients.PloS one · 2018Article
- Joint Learning of Representations of Medical Concepts and Words from EHR Data.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
- A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities.Data in brief · 2017Article
- Machine Learning and Decision Support in Critical Care.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Our objective in this paper was to visualize the evolution of clinical language and sentiment with respect to several common population-level categories including: time in the hospital, age, mortality, gender and race. Our analysis utilized seven years of unstructured free text notes from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database. The text data was partitioned by category and used to generate several high dimensional vector space representations. We generated visualizations of the vector spaces using Distributed Stochastic Neighbor Embedding (tSNE) and Principal Component Analysis (PCA). We also investigated representative words from clusters in the vector space. Lastly, we inferred the general sentiment of the clinical notes toward each parameter by gauging the average distance between positive and negative keywords and all other terms in the space. We found intriguing differences in the sentiment of clinical notes over time, outcome, and demographic features. We noted a decrease in the homogeneity and complexity of clusters over time for patients with poor outcomes. We also found greater positive sentiment for females, unmarried patients, and patients of African ethnicity.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.