Control model of parallel functioning production modules as fuzzy Petri nets

Abstract The article presents the results of research of Data Mining methods with Microsoft SQL Server. Microsoft Clustering algorithm was used for improving the effectiveness of medical prevention and treatment in a cohort of patients with arterial hypertension. There are rationales for monitoring of cardiovascular risk and desire to correct the risk with Data Mining at medical decision support systems. Authors used medical and sociological monitoring data from regional clinical hospital. The segmentation of arterial hypertension patients was performed using Microsoft Clustering algorithm. As a result, a quantitative assessment of the population profile for patients with arterial hypertension was obtained. The authors presented diagrams and profiles of clusters. They were compared. The developed approach is applied for decision support at regional health information management system for reduce of cardiovascular risk.

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A Survey on Data Mining Algorithms and Techniques in Medicine

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.1.3.25 ◽

2017 ◽

Vol 1 (3) ◽

pp. 61 ◽

Cited By ~ 1

Author(s):

Kasra Madadipouya

Keyword(s):

Data Mining ◽

Decision Support ◽

Decision Support Systems ◽

Performance Metrics ◽

Support Systems ◽

Medical Decision ◽

Medical Decision Support ◽

Data Mining Algorithms ◽

Medical Decision Support Systems ◽

Mining Algorithms

Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the population, symptoms, pathology of a disease, family history, and test results, would be useful to physicians and hospitals. This paper provides an in-depth review of available data mining algorithms and techniques. In addition to that, data mining applications in medicine are discussed as well as techniques for evaluating them and available applications of performance metrics.

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Medical decision support systems and therapeutics: The role of autopilots

Clinical Pharmacology & Therapeutics ◽

10.1002/cpt.259 ◽

2015 ◽

Vol 99 (2) ◽

pp. 161-164 ◽

Cited By ~ 15

Author(s):

RL Woosley ◽

J Whyte ◽

A Mohamadi ◽

K Romero

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Medical Decision ◽

Medical Decision Support ◽

Medical Decision Support Systems

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

Studia Universitatis Babeş-Bolyai Bioethica ◽

10.24193/subbbioethica.2021.spiss.16 ◽

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. "

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Medical Outcome Prediction for Intensive Care Unit Patients

Innovations in Data Methodologies and Computational Algorithms for Medical Applications ◽

10.4018/978-1-4666-0282-3.ch015 ◽

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.

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Medical Outcome Prediction for Intensive Care Unit Patients

Computer Engineering ◽

10.4018/978-1-61350-456-7.ch419 ◽

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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