Skip to main content
SimilarExport ZIP
100%

Visualization

✓ Published0🌍 Public
TThanaporn-sk
Last edited Mar 9, 2017
Created on Mar 9, 2017

This interactive scatter plot visualizes patient mortality probability against age, with the size of each dot representing the patient’s length of stay in the ICU. The visualization, built with D3.js, uses the `d3.tip` library to display tooltips on hover and `d3.behavior.zoom` for panning and zooming. A clickable legend allows filtering by ICU type, while filled dots indicate deceased patients. The data comes from the MIMIC ICU dataset and was used with XGBoost to predict mortality rates. The rendering uses SVG with animation for interactive transitions.

AI-generated description

Interactive Scatter Plot/Legend

Description

This scatter plot was built to help visualize probability of mortality associated with patients at the Beth Israel Deaconess Medical Center between 2001 and 2012, based on their anonymized medical records.

Interpretation

Hovering over each dot shows information associated with each patient. By clicking on the legend (divided by ICU), one can see if the patient is deceased or not (filled represents mortality) and compare that against what our model's predicted probability.

Data

The data used is a subset of the MIMIC ICU Data, which is publicly available. XGBoost was used to predict the mortality rate.

Inspired by

Built with blockbuilder.org

forked from <a href='http://bl.ocks.org/floofydugong/'>floofydugong</a>'s block: <a href='http://bl.ocks.org/floofydugong/9c94ab01d8c3ed8ea3821d4a7e119b07'>Interactive Scatter Plot/Legend</a>

forked from <a href='http://bl.ocks.org/wqm9425/'>wqm9425</a>'s block: <a href='http://bl.ocks.org/wqm9425/91c2fdea14d6a3dc0cf64631f5bf3706'>Interactive Scatter Plot/Legend</a>

gpl-3.0 Licensed

Similar vizzes

Loading thumbnail…

Visualization

This interactive scatter plot visualizes patient mortality probability data from the Beth Israel Deaconess Medical Center’s MIMIC ICU dataset, showing age against predicted mortality probability with circle size representing ICU length of stay. The code uses D3.js with `d3.svg.axis`, `d3.behavior.zoom`, and `d3-tip` for tooltips, rendering circles as SVG elements. A clickable legend filters the display by ICU unit, while filled versus unfilled markers indicate actual patient mortality via the `hospital_expire_flag` field. The CSV data includes anonymized patient records with age, stay duration, and care unit classifications.

AAjayyy
92% match
Loading thumbnail…

Visualization

This interactive scatter plot visualizes patient mortality probability at Beth Israel Deaconess Medical Center, using age, ICU length of stay, and predicted mortality from anonymized MIMIC ICU data. Dots are colored by ICU unit, with size reflecting length of stay, and hovering reveals tooltips with patient details. The view supports zooming and panning via `d3.behavior.zoom`, with a legend that filters dots by care unit on click, including toggling between deceased (filled) and surviving patients. Built with D3.js, the chart displays SVG circles and axes, with buttons to reset zoom or switch the x-axis to length of stay.

AAndresAnayaIsaza
90% match
Loading thumbnail…

Visualization

This interactive scatter plot visualizes ICU patient mortality risk at Beth Israel Deaconess Medical Center (2001–2012). Each dot is a patient, positioned by age and length of stay, with color indicating ICU type. Hovering reveals patient details, while clicking the legend toggles mortality status—filled dots represent deceased patients—and compares actual outcomes with XGBoost-predicted probabilities. The visualization is rendered in SVG with animation, using publicly available MIMIC ICU data.

GGerardoFurtado
89% match
Loading thumbnail…

Fork of Bonus ICE Scatter plot using D3.js

This example visualizes heart patient statistics across countries using an interactive 3D scatter plot. It displays two traces: death cases in red and active cases in blue, plotted against country name and patient category. The visualization uses Plotly.js within an HTML page, loading data from a CSV file via D3.js’s `d3.csv` function. D3’s `unpack` helper extracts columns for the x, y, and z axes, while Plotly’s `newPlot` renders the scatter3d chart with custom marker styling and opacity.

SSurikanti Swathi
73% match
Loading thumbnail…

Fork of Deaths Scatter plot

This scatter plot displays deaths per country, with each red circle representing a nation’s total mortality count. The data, sourced from the UCI Machine Learning Repository's Auto MPG dataset, is loaded via d3.csv and scaled using d3.scaleLinear and d3.scalePoint. The SVG rendering uses d3.axisLeft and d3.axisBottom for axes, with circle opacity set to 0.3 for visual overlap clarity.

AAimanoshi
70% match
Loading thumbnail…

Mortality Scatterplot

This scatterplot visualizes COVID-19 deaths per 10 million people across Denmark, Norway, and Sweden over time, with each country color-coded and a slider allowing users to scrub through dates. The chart draws trailing paths behind each data point to show temporal progression, and hovering reveals a tooltip with details. Built with D3 v7, it uses `d3.csv` to load data, `d3.group` to organize by country, and `d3.scaleTime`/`d3.scaleLinear` for axes, with SVG circles for points and an HTML range input for animation.

Kkfhujosefsson@dons.usfca.edu
70% match
Loading thumbnail…

ICE 7 Reusable D3 Scatter Plot

This example presents a reusable scatter plot of COVID-19 data, mapping countries along the x-axis and death counts along the y-axis. It utilizes the D3.js library (v7) within an SVG rendering context, demonstrating a modular component pattern via the `scatterPlot()` function. The main script asynchronously loads a CSV dataset using `d3.csv`, parses death values as numbers, and then invokes the chart with configurable accessors, margins, and point size. The visualization adapts to the full browser window by setting its width and height from `window.innerWidth` and `window.innerHeight`.

MM Hema
69% match
Loading thumbnail…

Fork of ICE-7 : Reusable Scatter Plot

This visualization uses D3 to render a reusable scatter plot of COVID-19 data, plotting each country’s population against itself while encoding the dataset’s SkinThickness column through symbol shapes. The chart displays axes for both variables, with points positioned using `scaleLinear` and `extent` for domains. The code relies on `d3.scaleOrdinal` to map symbol types and `d3.symbol` to generate the path data, while `axisLeft` and `axisBottom` add the scales. Data is loaded as CSV via `d3.csv`, and the plot is rendered as SVG with a custom margin and size configuration.

MM Hema
69% match