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This paper presents a temporal pattern mining method for medical data. It modifies the mining algorithms proposed by Batal et al. to incorporate with ranged relations. Experimental results demonstrate that the proposed method could generate frequent patterns with abstracted time ranges embedded in their temporal relations.
Electronic medical record (EMR) system has become increasingly more important in developed countries due to its convenience and efficiency in medical information storage, management and analysis. However, one of the main limitations of EMR lies in that the clinical data for patients cannot be exchanged among different medical institutions. Recently, the cloud-based clinic system, featuring in lower...
Medical institutes use Electronic Medical Record (EMR) to record a series of medical events, including diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Plenty of data mining technologies are applied in the EMR data set for knowledge discovery, which is precious to medical practice. The knowledge found is conducive to develop treatment plans, improve...
A drug can be used to deal more than one diseases and to deal an illness often need a combination of more than one drugs. This paper present how to discover a pattern of a combination of medicines related to a diagnosis of diseases using FP-Growth one of frequent pattern mining algorithm. We use FP-Growth because it has better performance than Apriori and Eclat. Data is collected from outpatients...
The amount of data being collected and stored is huge and is expanding at a vivid pace at both the national and international level. Health care organizations correspondingly generate a large volume of information every day. The health care industry is rich in information but it needs to discover hidden relationships and patterns in this data. This paper intends to use data mining techniques to discover...
Migraine is a common disease throughout the world. Not only does it affect the life of people tremendously, but it also leads to high costs, e.g. due to inability to work or various required drug-taking cycles for finding the best drug for a patient. Solving the latter aspect could help to improve the life of patients and decrease the impact of the other consequences. Therefore, in this paper, we...
Off-label drug use refers to prescribing marketed medications for the indications that are not included in their FDA-approved labeling information. Off-label drug use is quite common in clinical practice and inevitable to some extent. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information...
The paradigm of drug discovery has moved from finding new drugs that exhibit therapeutic properties for a disease to reusing existing approved drugs for a newer disease. The association between a drug and a disease involves a complex network of targets and pathways. In order to provide new insights, there has been a constant need for sophisticated tools that have the potential to discover new associations...
Drug repositioning represents the application of known drugs for new indications and plays an important role in healthcare research and industry. With its increasing value in drug development, multiple approaches have been applied in its exercise, basically classified as drug-based and diseasebased approaches. Our study adopted a disease-based approach and utilized Adverse Drug Reactions (ADRs) as...
Taking care and maintenance of a healthy population is the Strategy of each country. Information and communication technologies in the health care system have led to many changes in order to improve the quality of health care services to patients, rational spending time and reduce costs. In the booming field of IT research, the reach of drug delivery, information on grouping of similar drugs has been...
Designing medication recommendation system is a need for the fast growing world. In this fast growing world, the need for the application which recommend a medication led to a doctor friendly and hospital free atmosphere for all users all over the world. In this paper an unified extraction system with stanford parser is used for extraction of medical terms. Then K-means clustering algorithm clusters...
Clinical pathway is important for improving medical quality, reducing cost and regulating resource. However, a static, non-adaptive clinical pathway designed by experts with limited data can be hardly implemented in practice. Thus, mining the execution clinical pathway from various historical data is meaningful. Existing works focus on applying either process mining or clustering methods on medical...
We investigate the problem of finding unknown associations between 'concepts' in a given text corpus. A 'concept' is an entity, which is referred to by a phrase or multiple phrases (in case an entity has several names or synonyms, e.g. "illness", "disease"), while an 'association' is a relationship defined in a particular domain. The pairwise associations computation poses major...
BioAssay involves the use of live animal or plant (in vivo) or tissue or cell (in vitro) to determine the biological activity of a substance, such as hormone or drug, which plays a key role especially in evaluation of clinical efficacy for drug development. How to better utilize the biological information contained in the BioAssay for systematical identification of novel biological associations is...
Tibetan Medicine plays an important role in traditional Chinese Medical Science. For the inheritance of Tibetan medical science and disease prevention, the main method is to summarize and study the medication and diagnostic rules by using data mining technology, which is still in the early stage. In this paper, firstly, a standard knowledge base for plateau stomach illness has been constructed by...
We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The...
Drug-drug interaction (DDI) detection is an important issue of pharmacovigilance. Currently, approaches proposed to detection DDIs are mainly focused on data sources such as spontaneous reporting systems, electronic health records, chemical/pharmacological databases, and biomedical literatures. However, those data sources are limited either by low reporting ratio, access issue, or long publication...
The small number of patients enrolled in clinical trials to test new drugs and the relatively short trial durations make it paramount to monitor drugs' effectiveness and risks after they are approved by the regulatory agency. A thorough evaluation of a drug's effectiveness, side effects, and social and economic influences can prevent serious health damage to the public and shed light on new drug discovery...
Diabetic retinopathy (DR) is a leading cause of blindness and common complication of diabetes. Many diabetic patients take antihypertensive drugs to prevent cardiovascular problems, but these drugs may have unintended consequences on eyesight. Six common classes of antihypertensive drug are angiotensin converting enzyme (ACE) inhibitors, alpha blockers, angiotensin receptor blockers (ARBs), β-blockers,...
Millions of patients are affected by adverse drug reactions (ADRs) every year. It represents a substantial burden on healthcare resources. Pharmacovigilance using text and data analytics has drawn substantial attention in the recent years. These techniques are mainly extracting the associations between drugs and ADRs using data sources such as spontaneous reporting systems, electronic health records,...
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