scholarly journals A java based simulator with user interface to simulate ventilated patients

2015 ◽  
Vol 1 (1) ◽  
pp. 423-427 ◽  
Author(s):  
P. Stehle ◽  
T. Lehmann ◽  
D. Redmond ◽  
K. Möller ◽  
J. Kretschmer
Keyword(s):  
Decision Support ◽  
Test Bench ◽  
Respiratory Mechanics ◽  
Medical Decision ◽  
Mechanically Ventilated ◽  
Medical Decision Support ◽  
Life Saving ◽  
Clinical Scenarios ◽  
Routine Basis ◽  
Medical Decision Support Systems

AbstractMechanical ventilation is a life-saving intervention, which despite its use on a routine basis, poses the risk of inflicting further damage to the lung tissue if ventilator settings are chosen inappropriately. Medical decision support systems may help to prevent such injuries while providing the optimal settings to reach a defined clinical goal. In order to develop and verify decision support algorithms, a test bench simulating a patient’s behaviour is needed. We propose a Java based system that allows simulation of respiratory mechanics, gas exchange and cardiovascular dynamics of a mechanically ventilated patient. The implemented models are allowed to interact and are interchangeable enabling the simulation of various clinical scenarios. Model simulations are running in real-time and show physiologically plausible results.

The Good, the Bad and the Ugly in App Diagnosis: Outcomes and Implications by Example

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 38-38
Author(s):  
Sorana D. Bolboacă ◽  
◽  
Adriana Elena Bulboacă ◽  
◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision ◽  
Medical Decision ◽  
Normal Body Mass Index ◽  
Disk Herniation ◽  
Scientific Evaluation ◽  
Medical Decision Support ◽  
Lower Back ◽  
Clinical Scenarios ◽  
Medical Decision Support Systems

"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is generally not accompanied by a scientific evaluation of the performances, even if they are freely available or not. Two clinical scenarios were created, and Doctor31 retrieved the diagnosis decisions. First scenario: man, 29 years old, and three symptoms: dysphagia, weight loss (normal body mass index), and tiredness. Second scenario: women, 47 years old with L5-S1 disk herniation, abnormal anti-TPO antibodies, lower back pain (burning sensations), constipation, and tiredness. The outcome possible effects and implications, as well as vulnerabilities induced on the used, are highlighted and discussed. "

Medical Outcome Prediction for Intensive Care Unit Patients

2012 ◽  
pp. 251-262
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Medical Decision ◽  
Adult Intensive Care Unit ◽  
Medical Decision Support ◽  
Elimination Algorithm ◽  
Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

Medical Outcome Prediction for Intensive Care Unit Patients

2012 ◽  
pp. 1068-1079
Author(s):  
Simone A. Ludwig ◽  
Stefanie Roos ◽  
Monique Frize ◽  
Nicole Yu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Medical Decision ◽  
Adult Intensive Care Unit ◽  
Medical Decision Support ◽  
Elimination Algorithm ◽  
Medical Decision Support Systems

The rate of people dying from medical errors in hospitals each year is very high. Errors that frequently occur during the course of providing health care are adverse drug events and improper transfusions, surgical injuries and wrong-site surgery, suicides, restraint-related injuries or death, falls, burns, pressure ulcers, and mistaken patient identities. Medical decision support systems play an increasingly important role in medical practice. By assisting physicians in making clinical decisions, medical decision support systems improve the quality of medical care. Two approaches have been investigated for the prediction of medical outcomes: “hours of ventilation” and the “mortality rate” in the adult intensive care unit. The first approach is based on neural networks with the weight-elimination algorithm, and the second is based on genetic programming. Both approaches are compared to commonly used machine learning algorithms. Results show that both algorithms developed score well for the outcomes selected.

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