scholarly journals Prediction of Different Diseases and Development of a Clinical Decision Support System using Naïve Bayes Classifier

2020 ◽  
Vol 8 (5) ◽  
pp. 8-13
Author(s):  
Fatema Tuz Zohra
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Naive Bayes ◽  
Clinical Decision ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

scholarly journals Cranky comments: detecting clinical decision support malfunctions through free-text override reasons

2018 ◽  
Vol 26 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Skye Aaron ◽  
Dustin S McEvoy ◽  
Soumi Ray ◽  
Thu-Trang T Hickman ◽  
Adam Wright
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Naive Bayes ◽  
Word List ◽  
Clinical Decision ◽  
Naïve Bayes ◽  
Free Text ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Abstract Background Rule-base clinical decision support alerts are known to malfunction, but tools for discovering malfunctions are limited. Objective Investigate whether user override comments can be used to discover malfunctions. Methods We manually classified all rules in our database with at least 10 override comments into 3 categories based on a sample of override comments: “broken,” “not broken, but could be improved,” and “not broken.” We used 3 methods (frequency of comments, cranky word list heuristic, and a Naïve Bayes classifier trained on a sample of comments) to automatically rank rules based on features of their override comments. We evaluated each ranking using the manual classification as truth. Results Of the rules investigated, 62 were broken, 13 could be improved, and the remaining 45 were not broken. Frequency of comments performed worse than a random ranking, with precision at 20 of 8 and AUC = 0.487. The cranky comments heuristic performed better with precision at 20 of 16 and AUC = 0.723. The Naïve Bayes classifier had precision at 20 of 17 and AUC = 0.738. Discussion Override comments uncovered malfunctions in 26% of all rules active in our system. This is a lower bound on total malfunctions and much higher than expected. Even for low-resource organizations, reviewing comments identified by the cranky word list heuristic may be an effective and feasible way of finding broken alerts. Conclusion Override comments are a rich data source for finding alerts that are broken or could be improved. If possible, we recommend monitoring all override comments on a regular basis.

scholarly journals Klasifikasi Data Antroprometri Individu Menggunakan Algoritma Naïve Bayes Classifier

2021 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Johnson Sihombing
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Python Programming ◽  
Data Frequency

With the development of advances in computer technology today, most companies and organizations need a decision support system based on information systems, where the information is generally stored in the form of documents / text that is not structured. In this regard, a system for text management that is integrated with the decision support system is needed. One of them is the use of text data classification for anthropometric case studies of several samples. Anthropometry is a measurement of a person's body dimensions. The object of research is gender, first name, and height of a person. The research aims to determine the ratio of the number and height probability level of the number of men and women based on the input into an application using the Naïve Bayes Classifier method. The implementation design uses the Python programming language. The results showed that the height classification data frequency of women was more than the height classification data for men. And the number of height probability of a woman's body is greater than the number of height probability of a man's body.

1309-P: Utilizing a Clinical Decision Support System for Hypoglycemia Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

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