Data mining in Medicine
Data mining in medicine is an information extraction activity with the goal of uncovering concealed patterns and facts within extensive databases. It involves the automated extraction of predictive information, aiding in decision-making and estimation. Knowledge Discovery in Databases (KDD) is a process that aims to find valid, useful, and understandable patterns in data - a fundamental aspect of data mining. The KDD process encompasses various techniques such as exploratory statistics, visualization, neural networks, and more. Data mining methodology includes the CRISP-DM model, which stands for CRoss-Industry Standard Process for Data Mining. The adoption of data mining is driven by advancements in technology, including cheaper and faster storage, memory, and processors. These advancements enable the processing of large datasets and the application of diverse data mining techniques for knowledge representation and modeling.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
WHAT IS DATA MINING? Data Mining is An information extraction activity whose goal is to discover hidden facts and patterns contained in large databases. The use of tools to extract nuggets of useful information & patterns in bodies of data for use in decision support and estimation. The automated extraction of hidden predictive information databases. from (large)
WHAT IS KDD? Knowledge Discovery in Databases (KDD) is a process that aims at finding : valid useful novel understandable patterns in data . KDD comes originally from AI Data Mining is a part of KDD In the praxis KDD and Data Mining are used as synonyms
What is wrong with conventional statistical methods ? Manual hypothesis testing: Not practical with large numbers of variables User-driven User specifies variables, functional form and type of interaction: User intervention may influence resulting models Assumptions on linearity, probability distribution, etc. May not be valid Datasets collected with statistical analysis in mind Not always the case in practice
Why now? 1. Cheaper, larger, and faster disk storage: You can now put all your large database on disk 2. Cheaper, larger, and faster memory: You may even be able to accommodate it all in memory 3. Cheaper, more capable, and faster processors: Parallel computing architectures: Operate on large datasets in reasonable time Try exhaustive searches and brute force solutions
Data Mining Techniques (box of tricks) Older, Data preparation, Exploratory Statistics Linear Regression Visualization Cluster analysis Decision trees Rule induction Neural networks Abductive networks Newer, Modeling, Knowledge Representation
Data Mining Methodology CRISP-DM CRoss-Industry Standard Process for Data Mining
Modeling Procedure Develop Model With Known Cases Use Model For New Cases 1 2 IN OUT IN OUT F(X) Attributes, X Diagnosis, Y Rock Properties (Y) Attributes (X) Diagnosis Y = F(X) Determine F(X)
WHY DATA MINING IN MEDICINE? Development of information systems Massive databases in the domain of health Nature of medical data: noisy, incomplete, uncertain, nonlinearities, fuzziness Too many hidden and valuable knowledge in databases. Uncertainties, missing and error in gathered data in domain of health and especially in medicine. Too much data now collected with various formats due to computerization (text, graphs, images, ) Too many markers (attributes) now available for decision making Increased demand for better health services: Overworked physicians and facilities Stressful work conditions in ICUs, etc. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. are reasons that leads to increased use of these methods in health.
Medical Applications Biomedical/Biological Analysis Epidemiological Studies Screening Hospital Management Diagnosis Monitoring Medical Instruction and Training Therapy Prognosis
Medicine revolves on: Pattern Recognition, Classification, and Prediction Recognize and classify patterns in multivariate patient attributes. Diagnosis Select from available treatment methods; based on effectiveness, suitability to patient, etc. Treatment Predict future outcomes based on previous experience and present conditions. Prognosis
DATA MINING: APPLICATIONS AND CHALLENGES
Comparing growth rate of Data Mining publications in health and all sciences in world 8000 6790 7000 Number of Data Mining publications 5799 6000 5335 5146 5000 4308 3720 4000 3333 3000 2135 2000 1382 1079 814 1000 614 428 333 289 113 202 88 77 129 59 42 0 2000 2001 2002 2003 2004 2005 Year 2006 2007 2008 2009 2010 All Sciences Health 2nd Medical Informatics Slide No. 15 Seminar
Comparing growth rate of Data Mining publications in health and all sciences in Iran 140 117 120 Number of Data Mining publications 100 80 80 60 53 40 31 15 20 8 8 4 2 2 2 1 1 0 0 0 0 0 0 0 2000 2001 2002 2003 2004 2005 Year 2006 2007 2008 2009 2010 All Sciences Health 2nd Medical Informatics Slide No. 16 Seminar
Data mining challenges ultra-high dimensional classification problems (millions or billions of features, e.g., bio data) Ultra-high speed data streams Security, Privacy and Data Integrity Non-traditional Feature Selection (number of attributes > number of samples, Highly imbalanced) Explainable and Accurate Data Mining Methods Transfer Learning (Can knowledge learned from one set of samples help data mining on another sample) Contamination and noise Mining Multi-agent Data Dealing with Non-static and Unbalanced Data Different types of data Theory and methods of Data Mining
DATA MINING: EXPLORING DATA 19
What is data exploration? A preliminary exploration of the data to better understand its characteristics. The aims: Helping to select the right tool for preprocessing or analysis Making use of humans abilities to recognize patterns People can recognize patterns not captured by data analysis tools Related to the area of Exploratory Data Analysis (EDA) Created by statistician John Tukey
Techniques Used In Data Exploration In EDA, as originally defined by Tukey The focus was on visualization Clustering and anomaly detection were viewed as exploratory techniques In data mining, clustering and anomaly detection are major areas of interest, and not thought of as just exploratory
data exploration Summary statistics Visualization Online Analytical Processing (OLAP)
Summary Statistics Summary statistics are numbers that summarize properties of the data Summarized properties include frequency, location and spread
Frequency, Mode and Percentiles The frequency of an attribute value is the percentage of time the value occurs in the data set The mode of a an attribute is the most frequent attribute value The notions of frequency and mode are typically used with categorical data For continuous data, the notion of a percentile is more useful.
Measures of Location: Mean and Median The mean is the most common measure of the location of a set of points. However, the mean is very sensitive to outliers. Thus, the median or a trimmed mean is also commonly used.
Measures of Spread: Range and Variance Range is the difference between the max and min The variance or standard deviation is the most common measure of the spread of a set of points. However, this is also sensitive to outliers.
Visualization Visualization is the conversion of data into a visual or tabular format so that the characteristics of the data and the relationships among data items or attributes can be analyzed or reported. Visualization of data is one of the most powerful and appealing techniques for data exploration. Humans have a well developed ability to analyze large amounts of information that is presented visually Can detect general patterns and trends Can detect outliers and unusual patterns
Example: Sea Surface Temperature The following shows the Sea Surface Temperature (SST) for July 1982 Tens of thousands of data points are summarized in a single figure
Representation mapping of information to a visual format Data objects, their attributes, and the relationships among data objects are translated into graphical elements such as points, lines, shapes, and colors.
Arrangement Is the placement of visual elements within a display Can make a large difference in how easy it is to understand the data
Selection the elimination or the de-emphasis of certain objects and attributes Selection may involve the choosing a subset of attributes Dimensionality reduction is often used to reduce the number of dimensions to two or three Alternatively, pairs of attributes can be considered Selection may also involve choosing a subset of objects A region of the screen can only show so many points Can sample, but want to preserve points in sparse areas
Visualization Techniques: Histograms Histogram Usually shows the distribution of values of a single variable
Two-Dimensional Histograms Show the joint distribution of the values of two attributes
Visualization Techniques: Box Plots Box Plots Another way of displaying the distribution of data outlier 10th percentile 75th percentile 50th percentile 25th percentile 10th percentile
Example of Box Plots Box plots can be used to compare attributes
Visualization Techniques: Scatter Plots Scatter plots Attributes values determine the position Two-dimensional scatter plots most common, but can have three-dimensional scatter plots Often additional attributes can be displayed by using the size, shape, and color of the markers that represent the objects
Visualization Techniques: Contour Plots Contour plots Useful when a continuous attribute is measured on a spatial grid They partition the plane into regions of similar values The contour lines that form the boundaries of these regions connect points with equal values
Visualization Techniques: Matrix Plots Matrix plots Can plot the data matrix This can be useful when objects are sorted according to class Typically, the attributes are normalized to prevent one attribute from dominating the plot
Visualization of the Iris Data Matrix standard deviation
Visualization Techniques: Parallel Coordinates Parallel Coordinates Used to plot the attribute values of high- dimensional data Instead of using perpendicular axes, use a set of parallel axes The attribute values of each object are plotted as a point on each corresponding coordinate axis and the points are connected by a line Thus, each object is represented as a line Often, the lines representing a distinct class of objects group together, at least for some attributes Ordering of attributes is important in seeing such groupings
OLAP On-Line Analytical Processing (OLAP) was proposed by E. F. Codd, the father of the relational database. Relational databases put data into tables, while OLAP uses a multidimensional array representation. Such representations of data previously existed in statistics and other fields There are a number of data analysis and data exploration operations that are easier with such a data representation.
Example: Iris data We show how the attributes, petal length, petal width, and species type can be converted to a multidimensional array First, we discretized the petal width and length to have categorical values: low, medium, and high We get the following table - note the count attribute
Example: Iris data (continued) Each unique tuple of petal width, petal length, and species type identifies one element of the array. This element is assigned the corresponding count value. The figure illustrates the result. All non-specified tuples are 0.
Example: Iris data (continued) Slices of the multidimensional array are shown by the following cross-tabulations What do these tables tell us?
OLAP Operations: Data Cube The key operation of a OLAP is the formation of a data cube A data cube is a multidimensional representation of data, together with all possible aggregates: the aggregates that result by selecting a proper subset of the dimensions and summing over all remaining dimensions.
Data Cube Example Consider a data set that records the sales of products at a number of company stores at various dates. This data can be represented as a 3 dimensional array There are 3 two-dimensional aggregates, 3 one-dimensional aggregates, and 1 zero-dimensional aggregate (the overall total)
OLAP Operations: Slicing and Dicing Slicing is selecting a group of cells from the entire multidimensional array by specifying a specific value for one or more dimensions. Dicing involves selecting a subset of cells by specifying a range of attribute values. In practice, both operations can also be accompanied by aggregation over some dimensions.
