Data Mining And Business Analytics - 1352 Words

MIS 5375 580 SU15 Data Mining Business Analytics Midterm Exam Summer 2015 by Tamma Shanthipriya A00128661 DATA MINING AND BUSINESS ANALYTICS Data Mining is the computerized acknowledgment of diverse patterns in extensive data sets that are past analysis. It utilizes diverse mathematic algorithms to locate the right information as well as foresee the probability of future events. Some key properties that I learned in this topic are: †¢ discovery of useful patterns †¢ predictions of their future outcomes †¢ analysis on larger datasets †¢ useful data from them With increasing data the storage of the data must also be increased, which is a problem. So, data is stored or recorded in the form of computer data bases which makes easy to access the right data at any given point of time. To extract the right data from all these present volumes of data, usually certain traditional way of data analysis like regression analysis, cluster analysis, numerical taxonomy, multi-dimensional analysis, time series analysis , estimation outcome analysis and many more are used. Both data mining and data analysis are a subset of Business Intelligence which also includes data management systems, data warehouses and Online analytic processing(OLAP). To manage the mountains of information, the data is put away in a warehouse of information accumulated from different sources, including corporate databases, compressed data from interior frameworks, and information from outerShow MoreRelatedBusiness Intelligence, Business, And Data Mining1544 Words   |  7 Pages1. Introduction to Business Intelligence, Business Analytics and Data Mining Business Intelligence Business Intelligence is a process which includes different technologies and methods process for analysing data and presenting information which is helpful for top level management.BI includes various tools, application, and methodologies that enable organizations to collect data from internal and external sources, prepare that for analysis develop and run queries against the data and generate differentRead MoreStatistical Analysis : The Big Data Analytics1399 Words   |  6 PagesThe big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. 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Affirmative Action Are Effective Models For Younger...

There are also counterarguments for the position that affirmative action provides young people, and really minority, with a great role model. One scholar argues: Moreover, I doubt very much that individuals who reach top positions through affirmative action are effective models for younger members of their race or sex. What, after all, do they model? A black vice president who got her job through affirmative action is not necessarily a model of how to rise through the corporate meritocracy. She may be a model of how affirmative action can work for the people who find or put themselves in the right place at the right time (Thomas, Jr, 1990). This is also a valid viewpoint. Not everyone who is a placed into the workforce because of†¦show more content†¦A positive role model for how to â€Å"rise through corporate meritocracy† is when they see someone they can relate to work the hard to reach their desired position. This role model had to beat out everyone, no matter the circumstances or backgrounds of the other employees. This person can teach those younger than him or her that hard work will take his or her mentees to the positions they would like to reach in Corporate America; being held back because of things someone cannot control such as race and gender is an excuse. Because of these and other reasons, changes in affirmative action are in order. White people’s views are not all their fault. There are a number of companies that hired minority workers just because the businesses needed to be more diverse. These workers were not hired because they were qualified. Seeing this happen numerous of ti mes, it makes sense why Whites feel that minorities do not deserve to be beneficiaries of affirmative action. Minority workers do not work hard for the positions they have while Whites do. It is not fair in their eyes. That is why many Whites have expressed anger or disapproval of affirmative action programs in the workplace. â€Å"More than 40% of the total sample of non-Southern whites, or nearly one in every two, expresses anger toward affirmative action†¦Some 98% of Southerners-nearly all-are resentful toward affirmative action† (Knight, et al., 1997). With numbers like these,

Drug Related Problems Free Essays

Drug related problem Drug related problems (DRPs) are prevalent and causing considerable patient morbidity and mortality. Many of these DRPs are preventable through following the guidelines and rational drug used. There are many factors controlling the DRP occurrence such as patient age, disease status, drug characteristics, etc. We will write a custom essay sample on Drug Related Problems or any similar topic only for you Order Now High risk factors 1. Elderly (gt; 65 years); due to age related changes in pharmacodynamic and pharmacokinetics 2. Acute diseases such as acute renal failure, sepsis, etc†¦ 3. Patients with many chronic diseases; diabetes, hypertension, heart disease, liver problem, AIDS, etc†¦ 4. Patients with renal impairment or haemodialysis 5. Patients in special situations; pregnancy, lactation, 6. Certain diseases and their medications: cancer, diabetes, heart failure. 7. Polypharmacy (taken many drugs gt; 5 drugs) 8. Drug; certain drug classes are commonly involved e. g. Warfarin, insulin, digoxin, TCAs, etc†¦ Classification of drug-related problems 1. Inappropriate drug choice: Unjustified deviation from management guidelines consensus therapeutic can worsen the condition. Deviations that are based on the patient’s individual treatment goal and risk factors are not considered to be DRPs (e. g. Antibiotic used for viral infection. Furosemide prescribed for patient with hypokalemia). 2. Lack of necessary drug: Either one or more drugs are missing according to established guidelines or a medical problem is being treated with too little of the appropriate drug (under-prescribed) or appropriate drugs may be not used for maximum effectiveness. Moreover, duration of treatment may be too short which can lead to incomplete treatment. Deviations from guidelines that are based on the patient’s individual treatment goals and risk factors are not considered to be DRPs (e. g. B-blockers in heart failure or post-MI, stop diuretic before edema treated or loop diuretic used only for resistant edema). 3. Unnecessary drug and Duplication: A drug is unnecessary if the indication is no longer present, with continuation/prolonged use or double prescription of two or more drugs from the same therapeutic group or gives the same result. This intensifies their therapeutic effect and side effects. Duplication also can occur when more than one physician prescribes medications to a single patient or when a patient takes over-the-counter drugs with the same active ingredient (e. g. Long-term antibiotic prescribed for simple infection. Used of Ibuprofen and diclofenac concomitantly. 4. Incomplete medication history taking: Inappropriate integration of patient’s medical history can lead to many interactions due to lack of patient’s information such as hypersensitivity â€Å"medication allergy†, other diseases, OTC or herbal and medication used (e. g. Patient has allergy to penicillin). 5. Inappropriate dose or regimen: Dosing too high (overdose) or too low dose. Suboptimal dosing (including dosing time and formulation) according to established national/international guidelines, including frequency of dosing or duration of therapy. Deviations that are based on the patient’s individual treatment goal and risk factors are not considered to be DRPs (e. g. too high ACE inhibitor dose prescribed in relation to kidney function. Too low paracetamol dose use in relation to symptom-giving arthritis). . Adverse drug reaction (ADR): Any noxious, unintended, and undesired effect of a drug, which occurs at doses in humans for prophylaxis, diagnosis, or therapy (e. g. orthostatic hypotension happens with blood pressure lowering drug or intolerance dry cough due to ACE inhibitor). 7. Interaction: Drug–drug interaction, drug-food interaction, drug-disease interaction, drug-herbal, etc†¦ An interaction is occurring when the effe ct of a drug is changed by the presence of another drug, food, drink, herbal or some environmental chemical agent. Drug combinations with intended overall effect are not considered to be DRP (e. g. Drug–drug interaction; Furosemide and digitalis (increased effect/toxicity of digitalis with hypokalemia). Drug-food interaction; Amiodarone and Grapefruit, Grapefruit and Simvastatin (increase drug serum concentrations), or Tetracycline and calcium. Drug-disease interaction; used NSAIDs in chronic renal impairment). 8. Discontinuation of needed medication: Some times discontinuation of medication without reasonable medical indication can lead to therapeutic failure or a problem in treatment plan. In addition, stop some medications before controlling the disease or have good monitoring method can lead to failure in treatment plan (e. g. discontinue antibiotic before finishing its therapeutic course or stop Heparin injection before or just on start of Warfarin). 9. Contraindication: the used of some drugs are prohibited for some patients because to harmful risks of using these drugs are exceeding the benefits of their effect (e. g. ACE inhibitor uses in treating high blood pressure in a pregnant lady or using sulfa-drug in G6PD deficiency patients). 10. Abrupt stoppage medication: for certain medications, abrupt stopping can exacerbate the problem or lead to complications related to drug. The stopping process should be gradually (e. g. abruptly stopping B-blocker in MI patients or stopping Corticosteroid suddenly). 11. Untreated medical conditions: can lead to worsening of the disease or may lead to more serious problems (e. g. untreated dyslipidemia in patients with other risk factors). 12. Lack of necessary monitoring: Monitoring with respect to effects and toxicity of drugs is not done or does not adhere to guidelines (e. . INR for Warfarin. Thyroid function tests in patients taking levothyroxine 13. Others: In general, DRPs that do not belong to aforementioned categories. References 1. Bemt P and Egberts A (2007) Drug-related problems: definitions and classification. Journal of European Association of hospital Pharmacists (EAHP), 13, pp 62-64. 1. Lee S, Schwemm A, Reist J, Cantrell M, Andreski M, Doucette W, Chrischilles E and F arris K (2009) Pharmacists’ and pharmacy students’ ability to identify drug-related problems using TIMER (tool to improve medications in the elderly via review). American Journal of Pharmaceutical Education, 73, 3, pp 52-62 2. PCNE Classification for drug related problems (2006) Pharmaceutical Care Network Europe Foundation. Available from World Wide Web: http://www. pcne. org/dokumenter/DRP/PCNE %20classification%20V5. 01. pdf 2. Ruscin M (2009) Drug-Related Problems in the Elderly. Merck, Available from World Wide Web: http://www. merck. com/mmpe/sec23/ch341/ch341e. html 3. Ruths S, Viktil KK, Blix HS. Classification of drug-related problems. Tidsskr Nor Leageforen 2007; 127: 3073–6 Prescription Auditing Sheet Patients Name Age: years Drug related problem: |Inappropriate drug choice |Lack of necessary drug | |Unnecessary drug and Duplication |Incomplete medication history taking | |5. Inappropriate dose or regimen |6. Adverse drug reaction | |7. Interaction |8. Discontinuation of needed medication | |9. Contraindication |10. Abrupt stoppage medication | |11. Untreated medical condition |12. Lack of necessary monitoring | |13. Others | | Specification of the problem (and intervention if any): )†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. ( )†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. ( )†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. ( )†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã ¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. ( )†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. Patient Resume Age: Sex: Drug Allergies: Medical History – – – – – – – Medication History – – – – – Laboratory Data base How to cite Drug Related Problems, Papers