Tuesday, 21 February 2017

Benefits of data extraction for the healthcare system

Benefits of data extraction for the healthcare system

When people think of data extraction, they have to understand that is the process of information retrieval, which extract automatically structured information from semi-structured or unstructured web data sources. The companies that do data extraction provide for clients specific information available on different web pages. The Internet is a limitless source of information, and through this process, people from all domains can have access to useful knowledge. The same is with the healthcare system, which has to be concerned with providing patients quality services. They have to deal with poor documentation, and this has a huge impact on the way they provide services, so they have to do their best and try to obtain the needed information. If doctors confront with a lack of complete documentation in a case, they are not able to proper care the patients. The goal of data scraping in this situation is to provide accurate and sufficient information for correct billing and coding the services provided to patients.

The persons that are working in the healthcare system have to review in some situations hundred of pages long documents, for knowing how to deal with a case, and they have to be sure that the ones that contain useful information will be protected for being destroyed or lost in the future. A data mining company has the capability to automatically manage and capture the information from such documents. It helps doctors and healthcare specialists to reduce their dependency on manual data entry, and this helps them to become more efficient. If it is used a data scraping system, data is brought faster and doctors are able to make decisions more effectively. In addition, the healthcare system can collaborate with a company that is able to gather data from patients, to see how a certain type of drug reacts and what side effects it has.

Data mining companies can provide specific tools that can help specialists extract handwritten information. They are based on a character recognition technology that includes a continuously learning network that improves constantly. This assures people that they will obtain an increased level of accuracy. These tools transform the way clinics and hospitals manage and collect data. They are the key for the healthcare system to meet federal guidelines on patient privacy. When such a system is used by a hospital or clinic, it benefits from extraction, classification and management of the patient data. This classification makes the extraction process easier, because when a specialist needs information for a certain case he will have access to them in a fast and effective way. An important aspect in the healthcare system is that specialists have to be able to extract data from surveys. A data scraping company has all the tools needed for processing the information from a test or survey. The processing of this type of information is based on optical mark recognition technology and this helps at extracting the data from checkboxes more easily. The medical system has recorded an improved efficiency in providing quality services for patients since it began to use data scrapping.

Source: http://www.amazines.com/article_detail.cfm/6196290?articleid=6196290

Benefits of data extraction for the healthcare system

Benefits of data extraction for the healthcare system

When people think of data extraction, they have to understand that is the process of information retrieval, which extract automatically structured information from semi-structured or unstructured web data sources. The companies that do data extraction provide for clients specific information available on different web pages. The Internet is a limitless source of information, and through this process, people from all domains can have access to useful knowledge. The same is with the healthcare system, which has to be concerned with providing patients quality services. They have to deal with poor documentation, and this has a huge impact on the way they provide services, so they have to do their best and try to obtain the needed information. If doctors confront with a lack of complete documentation in a case, they are not able to proper care the patients. The goal of data scraping in this situation is to provide accurate and sufficient information for correct billing and coding the services provided to patients.

The persons that are working in the healthcare system have to review in some situations hundred of pages long documents, for knowing how to deal with a case, and they have to be sure that the ones that contain useful information will be protected for being destroyed or lost in the future. A data mining company has the capability to automatically manage and capture the information from such documents. It helps doctors and healthcare specialists to reduce their dependency on manual data entry, and this helps them to become more efficient. If it is used a data scraping system, data is brought faster and doctors are able to make decisions more effectively. In addition, the healthcare system can collaborate with a company that is able to gather data from patients, to see how a certain type of drug reacts and what side effects it has.

Data mining companies can provide specific tools that can help specialists extract handwritten information. They are based on a character recognition technology that includes a continuously learning network that improves constantly. This assures people that they will obtain an increased level of accuracy. These tools transform the way clinics and hospitals manage and collect data. They are the key for the healthcare system to meet federal guidelines on patient privacy. When such a system is used by a hospital or clinic, it benefits from extraction, classification and management of the patient data. This classification makes the extraction process easier, because when a specialist needs information for a certain case he will have access to them in a fast and effective way. An important aspect in the healthcare system is that specialists have to be able to extract data from surveys. A data scraping company has all the tools needed for processing the information from a test or survey. The processing of this type of information is based on optical mark recognition technology and this helps at extracting the data from checkboxes more easily. The medical system has recorded an improved efficiency in providing quality services for patients since it began to use data scrapping.

Source: http://www.amazines.com/article_detail.cfm/6196290?articleid=6196290

Saturday, 11 February 2017

Benefits of Predictive Analytics and Data Mining Services

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

-  Who are most likely to respond to your offer?
-  Who are most likely to ignore?
-  Who are most likely to discontinue your service?
-  How much a consumer will spend on your product?
-  Which transaction is a fraud?
-  Which insurance claim is a fraudulent?
-  What resource should I dedicate at a given time?

Benefits of Data mining include:

-  Better understanding of customer behavior propels better decision
-  Profitable customers can be spotted fast and served accordingly
-  Generate more business by reaching hidden markets
-  Target your Marketing message more effectively
-  Helps in minimizing risk and improves ROI.
-  Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
-  Improved customer service and confidence
-  Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

-  Spot the business problem or goal
-  Explore various data sources such as transaction history, user demography, catalog details, etc)
-  Extract different data patterns from the above data
-  Build a sample model based on data & problem
-  Classify data, find valuable factors, generate new variables
-  Construct a Predictive model using sample
-  Validate and Deploy this Model

Standard techniques used for it are:

-  Decision Tree
-  Multi-purpose Scaling
-  Linear Regressions
-  Logistic Regressions
-  Factor Analytics
-  Genetic Algorithms
-  Cluster Analytics
-  Product Association

Source:http://ezinearticles.com/?Benefits-of-Predictive-Analytics-and-Data-Mining-Services&id=4766989

Tuesday, 24 January 2017

Data Mining Introduction

Data Mining Introduction

Introduction

We have been "manually" extracting data in relation to the patterns they form for many years but as the volume of data and the varied sources from which we obtain it grow a more automatic approach is required.

The cause and solution to this increase in data to be processed has been because the increasing power of computer technology has increased data collection and storage. Direct hands-on data analysis has increasingly been supplemented, or even replaced entirely, by indirect, automatic data processing. Data mining is the process uncovering hidden data patterns and has been used by businesses, scientists and governments for years to produce market research reports. A primary use for data mining is to analyse patterns of behaviour.

It can be easily be divided into stages

Pre-processing

Once the objective for the data that has been deemed to be useful and able to be interpreted is known, a target data set has to be assembled. Logically data mining can only discover data patterns that already exist in the collected data, therefore the target dataset must be able to contain these patterns but small enough to be able to succeed in its objective within an acceptable time frame.

The target set then has to be cleansed. This removes sources that have noise and missing data.

The clean data is then reduced into feature vectors,(a summarized version of the raw data source) at a rate of one vector per source. The feature vectors are then split into two sets, a "training set" and a "test set". The training set is used to "train" the data mining algorithm(s), while the test set is used to verify the accuracy of any patterns found.

Data mining

Data mining commonly involves four classes of task:

Classification - Arranges the data into predefined groups. For example email could be classified as legitimate or spam.
Clustering - Arranges data in groups defined by algorithms that attempt to group similar items together
Regression - Attempts to find a function which models the data with the least error.
Association rule learning - Searches for relationships between variables. Often used in supermarkets to work out what products are frequently bought together. This information can then be used for marketing purposes.

Validation of Results

The final stage is to verify that the patterns produced by the data mining algorithms occur in the wider data set as not all patterns found by the data mining algorithms are necessarily valid.

If the patterns do not meet the required standards, then the preprocessing and data mining stages have to be re-evaluated. When the patterns meet the required standards then these patterns can be turned into knowledge.

Source : http://ezinearticles.com/?Data-Mining-Introduction&id=2731583

Wednesday, 11 January 2017

Data Mining - Efficient in Detecting and Solving the Fraud Cases

Data Mining - Efficient in Detecting and Solving the Fraud Cases

Data mining can be considered to be the crucial process of dragging out accurate and probably useful details from the data. This application uses analytical as well as visualization technology in order to explore and represent content in a specific format, which is easily engulfed by a layman. It is widely used in a variety of profiling exercises, such as detection of fraud, scientific discovery, surveys and marketing research. Data management has applications in various monetary sectors, health sectors, bio-informatics, social network data research, business intelligence etc. This module is mainly used by corporate personals in order to understand the behavior of customers. With its help, they can analyze the purchasing pattern of clients and can thus expand their market strategy. Various financial institutions and banking sectors use this module in order to detect the credit card fraud cases, by recognizing the process involved in false transactions. Data management is correlated to expertise and talent plays a vital role in running such kind of function. This is the reason, why it is usually referred as craft rather than science.

The main role of data mining is to provide analytical mindset into the conduct of a particular company, determining the historical data. For this, unknown external events and fretful activities are also considered. On the imperious level, it is more complicated mainly for regulatory bodies for forecasting various activities in advance and taking necessary measures in preventing illegal events in future. Overall, data management can be defined as the process of extracting motifs from data. It is mainly used to unwrap motifs in data, but more often, it is carried out on samples of the content. And if the samples are not of good representation then the data mining procedure will be ineffective. It is unable to discover designs, if they are present in the larger part of data. However, verification and validation of information can be carried out with the help of such kind of module.

Source:http://ezinearticles.com/?Data-Mining---Efficient-in-Detecting-and-Solving-the-Fraud-Cases&id=4378613

Monday, 2 January 2017

Data Mining

Data Mining

Data mining is the retrieving of hidden information from data using algorithms. Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. It is basically a technical and mathematical process that involves the use of software and specially designed programs. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data mining is gaining a lot of importance because of its vast applicability. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. It is basically an extension of some statistical methods like regression. However, the use of some advanced technologies makes it a decision making tool as well. Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more.

Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining.

Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms.

Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.

Source : http://ezinearticles.com/?Data-Mining&id=196652

Wednesday, 28 December 2016

Data Mining - Retrieving Information From Data

Data Mining - Retrieving Information From Data

Data mining definition is the process of retrieving information from data. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information.

It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. One must know data mining definition so that he can be in a position to make data.

The method of data mining has been there for so many centuries and it is used up to date. There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem. These methods are never used now days because a lot of people have advanced and technology has really changed the entire system.

With the coming up or with the introduction of computers and technology, it becomes very fast and easy to save information. Computers have made work easier and one can be able to expand more knowledge about data crawling and learn on how data is stored and processed through computer science.

Computer science is a course that sharpens one skill and expands more about data crawling and the definition of what data mining means. By studying computer science one can be in a position to know: clustering, support vector machines and decision trees there are some of the units that are found on computer science.

It's all about all this and this knowledge must be applied here. Government institutions, small scale business and supermarkets use data.

The main reason most companies use data mining is because data assist in the collection of information and observations that a company goes through in their daily activity. Such information is very vital in any companies profile and needs to be checked and updated for future reference just in case something happens.

Businesses which use data crawling focus mainly on return of investments, and they are able to know whether they are making a profit or a loss within a very short period. If the company or the business is making a profit they can be in a position to give customers an offer on the product in which they are selling so that the business can be a position to make more profit in an organization, this is very vital in human resource departments it helps in identifying the character traits of a person in terms of job performance.

Most people who use this method believe that is ethically neutral. The way it is being used nowadays raises a lot of questions about security and privacy of its members. Data mining needs good data preparation which can be in a position to uncover different types of information especially those that require privacy.

A very common way in this occurs is through data aggregation.

Data aggregation is when information is retrieved from different sources and is usually put together so that one can be in a position to be analyze one by one and this helps information to be very secure. So if one is collecting data it is vital for one to know the following:

    How will one use the data that he is collecting?
    Who will mine the data and use the data.
    Is the data very secure when am out can someone come and access it.
    How can one update the data when information is needed
    If the computer crashes do I have any backup somewhere.

It is important for one to be very careful with documents which deal with company's personal information so that information cannot easily be manipulated.

source : http://ezinearticles.com/?Data-Mining---Retrieving-Information-From-Data&id=5054887