scholarly journals Medication Use and the Risk of Newly Diagnosed Diabetes in Patients with Epilepsy

2020 ◽  
Vol 32 (2) ◽  
pp. 93-108 ◽  
Author(s):  
Sheng-Feng Sung ◽  
Pei-Ju Lee ◽  
Cheng-Yang Hsieh ◽  
Wan-Lun Zheng
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Clinical Problem ◽  
Future Research ◽  
Newly Diagnosed ◽  
Rule Mining ◽  
Diagnosed Diabetes ◽  
Patients With Epilepsy ◽  
Newly Diagnosed Diabetes

Epilepsy is a common neurological disorder that affects millions of people worldwide. Patients with epilepsy generally require long-term antiepileptic therapy and many of them receive polypharmacy. Certain medications, including older-generation antiepileptic drugs, have been known to predispose patients to developing diabetes. Although data mining techniques have become widely used in healthcare, they have seldom been applied in this clinical problem. Here, the authors used association rule mining to discover drugs or drug combinations that may be associated with newly diagnosed diabetes. Their findings indicate in addition to the most common culprits such as phenytoin and valproic acid, prescriptions containing carbamazepine, oxcarbazepine, or lamotrigine may be related to the development of newly diagnosed diabetes. These mined rules are useful as guidance to both clinical practice and future research.

Cytotoxicity Analysis of Nanoparticles by Association Rule Mining

2021 ◽  
Author(s):  
Gulsah Gul ◽  
Ramazan Yildirim ◽  
Nazar Ileri-Ercan
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Long Term Effects ◽  
Rule Mining ◽  
Efficient Delivery ◽  
Intracellular Targets

Understanding the toxicity behavior of NPs is of great importance to ensure efficient delivery to intracellular targets without causing cytotoxicity, to measure the long-term effects of nanoparticles (NPs), and to...

scholarly journals Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

2021 ◽  
Vol 7 (2) ◽  
pp. 128
Author(s):  
Siriporn Sawangarreerak ◽  
Putthiporn Thanathamathee
Keyword(s):  
Association Rule ◽  
Open Innovation ◽  
Association Rule Mining ◽  
Financial Statements ◽  
Frequent Pattern ◽  
Financial Ratios ◽  
Rule Mining ◽  
Business Income ◽  
Fraudulent Financial Statements

Identifying fraudulent financial statements is important in open innovation to help users analyze financial statements and make investment decisions. It also helps users be aware of the occurrence of fraud in financial statements by considering the associated pattern. This study aimed to find associated fraud patterns in financial ratios from financial statements on the Stock Exchange of Thailand using discretization of the financial ratios and frequent pattern growth (FP-Growth) association rule mining to find associated patterns. We found nine associated patterns in financial ratios related to fraudulent financial statements. This study is different from others that have analyzed the occurrence of fraud by using mathematics for each financial item. Moreover, this study discovered six financial items related to fraud: (1) gross profit, (2) primary business income, (3) ratio of primary business income to total assets, (4) ratio of capitals and reserves to total debt, (5) ratio of long-term debt to total capital and reserves, and (6) ratio of accounts receivable to primary business income. The three other financial items that were different from other studies to be focused on were (1) ratio of gross profit to primary business profit, (2) ratio of long-term debt to total assets, and (3) total assets.

scholarly journals Impact of elevated HbA1c on long-term mortality in patients presenting with acute myocardial infarction in daily clinical practice: insights from a ‘real world’ prospective registry of the Zwolle Myocardial Infarction Study Group

2019 ◽  
Vol 9 (6) ◽  
pp. 616-625 ◽  
Author(s):  
Renicus S Hermanides ◽  
Mark W Kennedy ◽  
Elvin Kedhi ◽  
Peter R van Dijk ◽  
Jorik R Timmer ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Clinical Outcome ◽  
Newly Diagnosed ◽  
Diagnosed Diabetes ◽  
Newly Diagnosed Diabetes ◽  
Long Term Mortality ◽  
Diabetes Patients

Background: Long-term clinical outcome is less well known in up to presentation persons unknown with diabetes mellitus who present with acute myocardial infarction and elevated glycosylated haemoglobin (HbA1c) levels on admission. We aimed to study the prognostic impact of deranged HbA1c at presentation on long-term mortality in patients not known with diabetes, presenting with acute myocardial infarction. Methods: A single-centre, large, prospective observational study in patients with and without known diabetes admitted to our hospital for ST-segment elevation myocardial infarction (STEMI) and non-STEMI. Newly diagnosed diabetes mellitus was defined as HbA1c of 48 mmol/l or greater and pre-diabetes mellitus was defined as HbA1c between 39 and 47 mmol/l. The primary endpoint was all-cause mortality at short (30 days) and long-term (median 52 months) follow-up. Results: Out of 7900 acute myocardial infarction patients studied, 1314 patients (17%) were known diabetes patients. Of the 6586 patients without known diabetes, 3977 (60%) had no diabetes, 2259 (34%) had pre-diabetes and 350 (5%) had newly diagnosed diabetes based on HbA1c on admission. Both short-term (3.9% vs. 7.4% vs. 6.0%, p<0.001) and long-term mortality (19% vs. 26% vs. 35%, p<0.001) for both pre-diabetes patients as well as newly diagnosed diabetes patients was poor and comparable to known diabetes patients. After multivariate analysis, newly diagnosed diabetes was independently associated with long-term mortality (hazard ratio 1.72, 95% confidence interval 1.27–2.34, P=0.001). Conclusions: In the largest study to date, newly diagnosed or pre-diabetes was present in 33% of acute myocardial infarction patients and was associated with poor long-term clinical outcome. Newly diagnosed diabetes (HbA1c ⩾48 mmol/mol) is an independent predictor of long-term mortality. More attention to early detection of diabetic status and initiation of blood glucose-lowering treatment is necessary.

Frequent Pattern Discovery and Association Rule Mining of XML Data

Data Mining ◽  
2013 ◽  
pp. 859-879
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Discovery ◽  
Frequent Pattern ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Xml Data ◽  
Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

Abstract 17910: Patients with Newly Diagnosed Diabetes Have Comparable Long Term Mortality with Known Diabetics After ST Segment Elevation Myocardial Infarction

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Bhuvnesh Aggarwal ◽  
Gautam Shah ◽  
Mandeep S Randhawa ◽  
A M Lincoff ◽  
Stephen G Ellis ◽  
...  
Keyword(s):  
Prior History ◽  
Newly Diagnosed ◽  
St Segment Elevation ◽  
St Segment ◽  
History Of ◽  
Diagnosed Diabetes ◽  
Newly Diagnosed Diabetes ◽  
Long Term Mortality

Background: A significant proportion of patients presenting with ST segment elevation myocardial infarction (STEMI) have newly diagnosed diabetes mellitus (DM). Hypothesis: Our aim was to identify patients with previously undiagnosed DM and compare their outcomes to those with known DM and without DM after STEMI. Methods: Consecutive patients undergoing primary PCI for STEMI at our center between Jan 2005 - Dec 2012 were included. Routinely performed admission Glycated hemoglobin (HbA1c) was utilized to identify patients with previously undiagnosed DM (HbA1c ≥ 6.5 and no history of DM or diabetes therapy). Patients were compared for in-hospital and long-term mortality based on follow up data from our institutional PCI registry. Results: 1,734 consecutive patients underwent primary PCI for STEMI and follow up data was available for 1,566 (90%) patients. Mean age was 60 years and 67.3% were males. A quarter of the patients (24.3%, n = 382) had prior history of DM and 8% (n=95) of the remainder had undiagnosed DM. Median follow up was 35 months. Mortality was comparable in patients with known DM and newly diagnosed DM both in hospital (11.2% vs. 12.5%, p=0.87) and at long term follow up (Figure 1, 2). Mortality was significantly worse with both groups when compared with patients with no DM (In-hospital mortality 5.6%; p<0.001 for both groups). Conclusions: One in twelve patients presenting with STEMI have previously undiagnosed DM. Cardiologists have a unique opportunity for identification and initiation of diabetic therapy in this vulnerable population. Patients with newly diagnosed DM have similar short and long-term outcomes when compared with patients with a prior history of DM.

Frequent Pattern Discovery and Association Rule Mining of XML Data

2011 ◽  
pp. 243-263 ◽  
Author(s):  
Qin Ding ◽  
Gnanasekaran Sundarraj
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Discovery ◽  
Frequent Pattern ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Xml Data ◽  
Art Research

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.

A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining

2016 ◽  
Vol 23 ◽  
pp. 120-130
Author(s):  
Manoj Kumar ◽  
Hemant Kumar Soni
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Future Research ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Business Decisions ◽  
Depth Analysis ◽  
Intelligent Tools

Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.

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