Latent Dirichlet Allocation Modeling LDA: Relationship between and source, Journal, Publisher.

Structural Topic Modeling (STM) incorporates metadata into the topic modeling framework. In STM, metadata can be entered in the topic model in two ways: topical PREVALENCE and topical CONTENT. Metadata covariates for topical PREVALENCE allow the observed metadata to affect the FREQUENCY with which a TOPIC is DISCUSSED... An example of topical PREVALENCE comparison: metadata covariate -> SURVIVAL. Reference: https://cran.r-project.org/web/packages/stm/vignettes/stmVignette.pdf

The Wordfish is a Poisson scaling model that estimates one-dimension word and document positions using MAXIMUM LIKELIHOOD (Slapin and Proksch, 2008). Both the estimated position of words and the positions of the documents can be plotted. Data is from the Joseph Paul Cohen Ph.D. project. Text data of Clinical Notes of patients affected by COVID-19 and other lung illnesses. Reference: https://github.com/ieee8023/covid-chestxray-dataset

Structural Topic Modeling (STM) incorporates metadata into the topic modeling framework. In STM, metadata can be entered in the topic model in two ways: topical PREVALENCE and topical CONTENT. Metadata covariates for topical PREVALENCE allow the observed metadata to affect the FREQUENCY with which a TOPIC is DISCUSSED... An example of topical PREVALENCE comparison: metadata covariate -> SURVIVAL. Reference: https://cran.r-project.org/web/packages/stm/vignettes/stmVignette.pdf

Visualizing relationships among words. Words are arranged into an enhanced network, or “graph”. Data is from the Joseph Paul Cohen Ph.D. project (https://github.com/ieee8023/covid-chestxray-dataset). Text data of Clinical Notes of patients affected by COVID-19 and other lung illnesses.

Visualize relationships among words simultaneously. Words can be arranged into an enhanced network, or “graph”. Data is from the COVID-19 Open Research Dataset Challenge (CORD-19); an AI KAGGLE challenge with AI2, CZI, MSR, Georgetown, NIH & The White House. METADATA FULL Dataset is available here: https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge.

We wish to visualize all of the relationships among words simultaneously. To do so, words can be arranged into a network, or “graph”, arranged in a combination of connected nodes. Text data is from the Joseph Paul Cohen Ph.D. project. The basic idea is to explore and analyze the text in the Clinical Notes of patients affected by the COVID-19 and/or other possible related illnesses. Text Mining/NLP techniques are unleashed in order to distill actionable insight that could help Healthcare professional to fight the coronavirus pandemic.

Latent Dirichlet Allocation (LDA) TOPIC ANALYSIS. Data is from the Joseph Paul Cohen Ph.D. project. The basic idea is to explore and analyze text data in the Clinical Notes of patients affected by the COVID-19 and/or other illnesses. Text Mining/NLP techniques are unleashed in order to distill more actionable insight that could help Healthcare professional to fight the coronavirus pandemic.

Latent Dirichlet Allocation (LDA) TOPIC ANALYSIS. Data is from the Joseph Paul Cohen Ph.D. project. The basic idea is to explore and analyze text data in the Clinical Notes of patients affected by the COVID-19 and/or other illnesses. Text Mining/NLP techniques are unleashed in order to distill more actionable insight that could help Healthcare professional to fight the coronavirus pandemic.

Comparison Word Cloud built with patients´ Clinical Notes text data and NRC Emotion Lexicon. The dataset is from the Joseph Paul Cohen Ph.D. project. The basic idea is to explore and analyze text data in the Clinical Notes of patients affected by the COVID-19 and/or other illnesses. Text Mining/NLP techniques are unleashed in order to distill more actionable insight that could help Healthcare professional to fight the coronavirus pandemic: TOPIC ANALYSIS - PART 1.

The Sentiment Word Cloud was constructed using the dataset is from the Joseph Paul Cohen Ph.D. project. The basic idea is to explore and analyze text data in the Clinical Notes of patients affected by the COVID-19 and/or other illnesses. Text Mining/NLP techniques are unleashed in order to distill more actionable insight that could help Healthcare professional to fight the coronavirus pandemic: TOPIC ANALYSIS - PART 1.

Combination of "rules" or factors that reduce the patient´s survival likelihood: SURVIVAL-N.

The best combination of "rules" or factors that, if fulfilled could increase the likelihood of a patient to survive: SURVIVAL-Y.