Related Experiment Video
Updated: Jul 6, 2026
07:31
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Improving the reliability of medical software by predicting the dangerous software modules
Vili Podgorelec1, Marjan Hericko, Matjaz B Juric
1University of Maribor--FERI, Institute of Informatics, Smetanova ulica 17, SI-2000 Maribor, Slovenia. vili.podgorelec@uni-mb.si
Journal of Medical Systems
|April 21, 2005
Summary
This study introduces a machine learning approach to predict dangerous software modules in medical systems early in development. This helps designers focus testing efforts, enhancing overall software reliability.
Area of Science:
- Computer Science
- Software Engineering
- Medical Informatics
Background:
- Modern medical systems heavily rely on software, making software reliability crucial.
- Traditional software reliability models require failure data unavailable during early development stages.
- Analyzing past projects offers valuable insights for improving new medical software.
Purpose of the Study:
- To develop a predictive method for identifying potentially dangerous software modules in medical systems during development.
- To leverage machine learning techniques for analyzing completed software modules to inform new projects.
- To enable targeted testing efforts for enhanced medical software reliability.
Main Methods:
- Utilizing machine learning techniques to analyze software modules from completed projects.
- Developing a predictive model based on historical software data.
- Applying the model to predict high-risk modules in ongoing medical software development.
Main Results:
- The proposed method successfully predicts potentially dangerous software modules.
- Early identification of risky modules allows for proactive risk management.
- The approach facilitates more efficient allocation of testing resources.
Conclusions:
- Machine learning offers a viable solution for software reliability analysis in early development phases.
- Predictive modeling enhances the reliability of medical software systems.
- Targeted testing based on predictions leads to improved software quality and safety.
Related Concept Videos
01:29Issues And Trends In Healthcare Delivery System
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
01:25Errors occurring during blood pressure monitoring
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
Several factors...
01:19Pharmacovigilance
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
01:12Types of Errors: Detection and Minimization
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
01:26Pharmaceutical Poisoning: Potential Scenarios
Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
01:24Automated Microbial Diagnostics
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...