Intelligent Medical Cases Creation Using Medical Events
Abstract
different location. This record fails to gain an overall picture
of the patients health condition. The medical cases creation
system delivers accessible and accurate information of a patient
to health-care providers. This system will be able to retrieve the
complete medical background of the patient with a significant
advantage in treatment and diagnosis by imputing missing
attribute values and removing noise present in the patient record.
The fundamental function of medical cases is to record, monitor,
retrieve, analyze and predict all event and cases which are
an encounter between patient and the health-care system. The
purpose of the dissertation is to capture medical knowledge by
a computer which can aid doctors to determine a diagnosis.
The proposed framework is based on clustering technique and
imputation measure. Clustering techniques help in matching
particular pattern among patients attributes present in records.
We then focus on imputation measure which is used to fix
the missing attribute values present in the medical records and
finally, the multiple sets of results are combined to yield a single
inference.
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PDFReferences
Li Peng, Zhang Ting-ting, LiangTian-ge and Zhang Kai-hui, “Missing
Value Imputation Method Based on Density Clustering and Grey Relational Analysis, International Journal of Multimedia and Ubiquitous
Engineering 2015.
ing Tian, Bing Yu, Dan Yu and Shilong Ma, “Missing data analyses:
a hybrid multiple imputation algorithm using Gray System Theory and
entropy based on clustering, Springer Science+Business Media New York
Ritu Chauhan and Harleen Kaur, “A Knowledge Driven Model: Extract Knowledge from High Dimensional Medical Databases, International Conference on Machine Intelligence and Research and Advancement,2013.
D. L. Hudson, “Development of Health Diagnostics Based on Personalized Medical Models, International Conference of the IEEE Engineering
in Medicine and Biology Society (EMBC), 2015.
Taranath N.L., Shanthakumar B Patil, Premajyothi Patil and
C.K.Subbaraya, “Medical Decision Support System for the Missing
Data using Data Mining, IEEE International Conference on Electrical,
Computer and Communication Technologies (ICECCT), 2015.
Ruilin Pan, Tingsheng Yang, Jianhua Cao, Ke Lu and Zhanchao Zhang,
“Missing data imputation by K nearest neighbours based on grey relational structure and mutual information, Springer Science and Business
Media New York, 2015.
Erkki Pesonen, Matti Eskelinen, Martti Juhola, “Treatment of missing
data values in a neural network based decision support system for acute
abdominal pain, Elsevier Science, Artificial Intelligence in Medicine 13,
Ponrudee Netisopakul and Waranyu Saapajit, “Prediagnosis Doctor Simulation Using Case-Based Techniques, IEEE World Congress on Computer
Science and Information Engineering, 2009.
Kamran Farooq, Peipei Yang, Amir Hussain, Kaizhu Huang, Calum
MacRae, Chris Eckl and Warner Slack, “Efficient clinical decision making
by learning from missing clinical data, IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), 2013.
Rupa Jagannathan and Sanja Petrovic, “Dealing with Missing Values
in a Clinical Case-Based Reasoning System, 2nd IEEE International
Conference on Computer Science and Information Technology, 2013.
Jinsung Yoon, Camelia Davtyan, MD, FACP, and Mihaela van der
Schaar, “Discovery and Clinical Decision Support for Personalized
Healthcare” IEEE Journal of Biomedical and Health Informatics, 2015.
Hisao Ishbuch, Aluhu-0 Miyazalu and Hideo Tanaka, “Neural-NetworkBased Diagnosis Systems for Incomplete Data with Missing Inputs, IEEE
World Congress on Computational Intelligence, 1994.
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