Mining Medical Databases Using Proposed Incremental Association Rules Algorithm (PIA)

2008 ◽  
Author(s):  
Laila Elfangary ◽  
Walid Adly Atteya
Keyword(s):  
Association Rules ◽  
Medical Databases

scholarly journals Mining association rules with improved semantics in medical databases

2001 ◽  
Vol 21 (1-3) ◽  
pp. 241-245 ◽  
Author(s):  
Miguel Delgado ◽  
Daniel Sánchez ◽  
Marı́a J Martı́n-Bautista ◽  
Marı́a-Amparo Vila
Keyword(s):  
Association Rules ◽  
Medical Databases ◽  
Mining Association Rules

DETECTING CRITICAL LEAST ASSOCIATION RULES IN MEDICAL DATABASES

2012 ◽  
Vol 09 ◽  
pp. 464-479 ◽  
Author(s):  
ZAILANI ABDULLAH ◽  
TUTUT HERAWAN ◽  
MUSTAFA MAT DERIS
Keyword(s):  
Breast Cancer ◽  
Association Rules ◽  
Mathematical Formulation ◽  
Medical Data ◽  
Emission Computed Tomography ◽  
Proton Emission ◽  
Database Mining ◽  
Scalable Algorithm ◽  
Medical Databases ◽  
Medical Dataset

Least association rules are corresponded to the rarity or irregularity relationship among itemset in database. Mining these rules is very difficult and rarely focused since it always involves with infrequent and exceptional cases. In certain medical data, detecting these rules is very critical and most valuable. However, mathematical formulation and evaluation of the new proposed measurement are not really impressive. Therefore, in this paper we applied our novel measurement called Critical Relative Support (CRS) to mine the critical least association rules from medical dataset. We also employed our scalable algorithm called Significant Least Pattern Growth algorithm (SLP-Growth) to mine the respective association rules. Experiment with two benchmarked medical datasets, Breast Cancer and Cardiac Single Proton Emission Computed Tomography (SPECT) Images proves that CRS can be used to detect to the pertinent rules and thus verify its scalability.

The Stress Respond, Related To the Opioid Abstinence And Detoxification: literature review

2014 ◽  
Vol 21 (1) ◽  
pp. 86-90
Author(s):  
Robertas Badaras ◽  
Gabija Dragelytė ◽  
Indrė Vaitekonytė ◽  
Juozas Ivaškevičius ◽  
Jūratė Šipylaitė
Keyword(s):  
Opioid Dependence ◽  
Financial Resources ◽  
Successful Completion ◽  
Minimum Risk ◽  
Medical Databases ◽  
Dosing Regimens ◽  
Naltrexone Treatment ◽  
Dopaminergic Pathways ◽  
Pituitary Adrenal Axis ◽  
Opioid Detoxification

Materials and Methods. Published articles on the opioid abuse and methods of opioid detoxification were identified by searching medical databases, using corresponding literature and were also searched manually for applicable papers. The search was limited to articles published from 1985 through 2014. Results. Opioid dependence determine pathophysiologic changes in the dopaminergic pathways of the organism, as well as the alterations in the stress-responsive hypothalamic-pituitary-adrenal axis. The usage of opioid antagonists in the early stages of withdrawal, can lead the effectiveness of opioid detoxification to 100%. Rapid opioid detoxification do not remove all the symptoms of abstinence. Negative aspects, concerning the procedure, while using prevention, can be reduced to the minimum risk. Rapid opioid detoxification, comparing it with Ultrarapid opioid detoxification procedure, diverges as less financial resources and a lower risk containing technique. Conclusions. Use of antagonists may reduce the duration of withdrawal, thus reducing the overall severity of withdrawal and increasing the chances of successful completion. This technique facilitates commencement of naltrexone treatment. Dosing regimens used in clinical trials vary. Subsequent results do not correlate with the methods of detoxification.

How Useful Can Be Data Mining For A Continuos Speech Therapist’s Education?

2014 ◽  
Vol 1 (1) ◽  
pp. 339-342
Author(s):  
Mirela Danubianu ◽  
Dragos Mircea Danubianu
Keyword(s):  
Data Mining ◽  
Information And Communication Technology ◽  
Association Rules ◽  
Communication Technology ◽  
Speech Therapy ◽  
Proper Treatment ◽  
Speech Impairments ◽  
Information And Communication ◽  
Specific Education

AbstractSpeech therapy can be viewed as a business in logopaedic area that aims to offer services for correcting language. A proper treatment of speech impairments ensures improved efficiency of therapy, so, in order to do that, a therapist must continuously learn how to adjust its therapy methods to patient's characteristics. Using Information and Communication Technology in this area allowed collecting a lot of data regarding various aspects of treatment. These data can be used for a data mining process in order to find useful and usable patterns and models which help therapists to improve its specific education. Clustering, classification or association rules can provide unexpected information which help to complete therapist's knowledge and to adapt the therapy to patient's needs.

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