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Related Concept Videos

Modified Boxplots00:57

Modified Boxplots

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
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Boxplot01:12

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Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
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Dosage Regimen Designs: Nomograms and Tabulations01:23

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Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
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Multiple Bar Graph01:07

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Interpreting Run Charts01:25

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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Antibiotic Selection00:57

Antibiotic Selection

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Related Experiment Video

Updated: Mar 4, 2026

High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity
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Design of a Visual Interface for Comparing Antibiotics Using Rainbow Boxes.

Rosy Tsopra1, Shérazade Kinouani2, Alain Venot1

  • 1LIMICS, INSERM UMRS 1142, Université Paris 13, UPMC Université Paris 6, Paris, France.

Studies in Health Technology and Informatics
|April 21, 2017
PubMed
Summary

Doctors can now better choose antibiotics using a novel visualization technique. This approach aids in selecting optimal antibiotic prescriptions, improving patient care and reducing negative impacts from non-optimal drug use.

Keywords:
Antibiotic prescriptionClinical Decision Support SystemUsabilityVisual analytics

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Area of Science:

  • Medical Informatics
  • Clinical Pharmacology
  • Health Informatics

Background:

  • Non-optimal antibiotic prescribing negatively impacts patients and populations.
  • Clinical practice guidelines are not consistently followed due to unclear rationales.
  • Difficulty in understanding antibiotic recommendations hinders adherence.

Purpose of the Study:

  • To propose a new method for presenting antibiotic properties to aid clinical decision-making.
  • To enable doctors to compare antibiotics and select the most appropriate one.
  • To improve the clarity and understanding of antibiotic recommendations.

Main Methods:

  • Utilized and extended rainbow boxes, a novel overlapping set visualization technique.
  • Presented antibiotic properties visually for comparison.
  • Tested the approach on 11 clinical scenarios for urinary tract infections.

Main Results:

  • 10 out of 11 doctors found the approach interesting and useful in clinical practice.
  • The visualization method demonstrated potential for improving antibiotic selection.
  • The approach was assessed for simplicity, interest, and utility by medical professionals.

Conclusions:

  • The proposed visualization technique shows promise for enhancing antibiotic prescribing practices.
  • Further studies are needed to validate these preliminary findings.
  • This approach could lead to more optimal antibiotic use in clinical settings.