Skip to main content
SimilarExport ZIP
100%

Visualization

✓ Published0🌍 Public
GGerardoFurtado
Last edited Apr 20, 2018
Created on Apr 20, 2018

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.

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>

gpl-3.0 Licensed

Similar vizzes

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
93% match
Loading thumbnail…

ICE-6 Impact of Cancer Deaths 2019

This interactive visualization displays cancer mortality trends in the United States from 1990 to 2019, plotting individual yearly death counts for fifteen distinct cancer types as colored dots. The scatterplot uses SVG circles rendered through React and D3, with each cancer type assigned a unique color and an interactive tooltip revealing exact death numbers on hover. Built with D3 scales (`scaleTime`, `scaleLinear`), axes (`axisLeft`, `axisBottom`), and event handlers, the chart reads from a CSV dataset filtered for USA records, making temporal patterns across cancer categories immediately comparable.

AAsrar Al Qassem
69% 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
69% match
Loading thumbnail…

Fork of ICE-6

This visualization displays the relationship between age and BMI for insured individuals, showing a bar chart of hospitalized COVID-confirmed patients per county. The data comes from a CSV file loaded via `d3.csv`, with the first fifteen records rendered as horizontal bars. The chart uses React for component management and D3 v5’s `scaleBand`, `scaleLinear`, and `max` functions to map county names and patient counts. The SVG output includes manually drawn axes with tick marks and labels, while the grid lines are styled in red.

Hhm0421
68% match
Loading thumbnail…

Bonus ICE - 3D Bar chart

This 3D bar chart visualizes patient mortality across seven causes of death—ranging from heart disease to malaria—for India, Pakistan, and the United States between 1990 and 2022. The visualization uses the Highcharts library with its `highcharts-3d.js` extension, configuring options like `alpha`, `beta`, and `viewDistance` to create a tilted, perspective view. Each country’s data is rendered as a separate series with stacked columns, plotted against categorical x-axis labels and a y-axis measuring total death cases. The chart’s depth settings and `groupZPadding` enhance the three-dimensional separation of the grouped bars.

SSurikanti Swathi
68% match
Loading thumbnail…

Reusable D3 Scatter Plot

This animated scatter plot cycles through cancer types—liver, breast, prostate, and thyroid—displaying death rates over time using the Iris Dataset-inspired structure. Built with reusable D3.js components and SVG rendering, the visualization updates every 2 seconds, transitioning between datasets while updating the title and axis labels. Symbols vary by cancer type (circle, square, star) and axes represent years and number of deaths, with smooth transitions on each refresh.

AAsrar Al Qassem
67% match
Loading thumbnail…

Reusable D3 Scatter Plot

This interactive scatter plot visualizes cancer mortality trends in the USA, cycling through liver, breast, and prostate cancer datasets. Built with D3 v6 as a reusable chart, it animates between cancer types every 3 seconds, updating the y-axis and point symbols (star, square, circle) accordingly. The chart leverages SVG rendering and custom symbol mappings to distinguish cancer types, with axes and margins configured for readability.

Bbhavana.bayyapu@gmail.com
67% match
Loading thumbnail…

Visualizing correlations between heart disease patients' data

This interactive scatter plot lets users explore correlations among heart disease patients by selecting any two metrics from dropdown menus, which map to the x- and y-axes. Each point represents a patient, colored by heart disease status, from a CSV dataset. Built with D3 v7, it uses scaleLinear, axisBottom, axisLeft, and transition().delay().duration() for animated point placement. The chart renders in SVG with adjustable margins and axis labels.

AAshtone Onyango
67% match