CTSI Module 8 Workshop Introduction to Biomedical Informatics,

Part V

Practical Tools: Data Processing & Analysis

William Hsu, PhD Assistant Professor Medical Imaging Informatics Group Dept of Radiological Sciences, UCLA

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Disclosures

No financial disclosures.

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Overview

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Access data • Query databases/integrate multiple data sets

Characterize variables • Determine variable type, frequency distribution

Clean data • Consolidate observations, identify missing values

Remove variables • Eliminate variables with too few observations

Transform variables • Normalize, aggregate, discretize

Overview

• A variety of tools are available to help retrieve, transform, and explore clinical datasets

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• Characterize variables • Clean data • Remove variables • Transform variables

• OpenRefine

• Exploratory analysis • Tableau Desktop

• Access to data • Public data sources • UC ReX Data Explorer

Access to Data: Public Sources

5 http://cancergenome.nih.gov

/

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Access to Data: Imaging Studies

10 http://www.cancerimagingarchive.net/

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Modalities • Computed radiography (CR) • Computed tomography (CT) • Mammography (MG) • Magnetic resonance (MR) • Nuclear medicine (NM) • …

Collections • Lung Imaging Database Consortium

(LIDC-IDRI) • Glioblastoma multiforme MR studies

(REMBRANDT) • Reference Imaging Database to

Evaluate Response (RIDER)

Annotations • Radiologist generated interpretations

(when available in the form of XML markup)

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UC ReX Data Explorer

• UC ReX Data Explorer – Allows authorized UC researchers to query de-

identified diagnosis and procedure data across all patient records

• 11.8 million patient records • All 5 UC academic medical centers

– Provides secure, web-based, local access to shared database

– Includes patient demographics, diagnosis, and procedures data

• http://ucrex.org

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Finding Patient Cohorts

• Query for aggregate patient numbers • Obtain IRB approval for research protocol • Obtain list of specific patients

– Eventually this list will be available as a function of the cohort search

• Retrieve specific information about matching individuals – Demographics, providers, visits, diagnoses,

medications, procedures, labs, radiology…

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UC ReX Data Explorer

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UC ReX Data Explorer

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UC ReX Data Explorer

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Access to Data: Claims

Electronic health records • Large amount of detail captured

about individual patients • Can be structured or

unstructured • Single institution

Administrative (Claims) Data • Information summarized as

diagnosis/billing codes (CPT, ICD-9,NDC)

• Structured • Any institution requesting

reimbursement

http://www.resdac.org/

Characterization • Type

– Constant (every value is the same) – Dichotomous (male/female, 0 or 1) – Discrete (finite number of values, e.g., color) – Continuous (infinite number of numeric values)

• Scale – Nominal – Ordinal – Interval – Ratio

• Role – Label (individual observations) – Descriptor (predictors) – Response (target)

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Data Cleaning Addresses... • Standardization

– General Electric Company, General Elec, GE… • Outliers • Inconsistencies

– Non-numeric terms in a continuous variable (e.g., above 100)

• Missing values – Missing completely at random – Missing at random – Not missing at random – Imputation

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OpenRefine

• Formerly Google Refine • Handles tasks related to loading, cleaning, and

transforming raw data • Web-based interface connected to a

lightweight web server that runs on your desktop computer

• Freely available • Available here:

http://openrefine.org/

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OpenRefine: Facets • Purpose: Filtering which rows are displayed based

on a user-defined set of criteria – Define facet for a column – Utilize facet to identify relevant rows

• Many types of facets available – Text facet – Numerical facet – Timeline facet – Scatterplot facet

• Tasks – Removing redundancy – Error checking

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Clustering

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OpenRefine: Expression Language

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Expression Action

value + “ (approved)” Concatenate two strings; whatever is in value gets converted to a string first

value + 2.239 Add two numbers; if value actually holds something other than a number, this becomes a string concatenation

value.trim().length() Takes the length of value after trimming its leading and trailing whitespace

value.substring(7,10) Take the substring of value from character index 7 up to and excluding character index 10

value.substring(13) Take the substring of value from character index 13 until the end of the string

Source: https://code.google.com/p/google-refine/wiki/UnderstandingExpressions

Exploratory Analysis • Information visualization

– The use of visual representations of abstract data to enhance human cognition

– Visual analytics: Facilitating analytical reasoning through interactive visual interfaces

• Tableau Desktop – http://www.tableausoftware.com/ – Commercial software derived from visualization

research done at Stanford University – Various versions available:

• Public (free) but can only save to Tableau website • Desktop Personal ($$, free for full-time students) • Desktop Professional ($$$, connect to more data sources)

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30 http://orbit.nlm.nih.gov/

Medical Imaging Informatics

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Stroke Modeling: Create an observational database that enables the generation of an influence diagram for acute stroke treatment

Disease Models for Neuro-oncology: Tools to aid clinicians and researchers to create population-based models from routine, clinically acquired cancer patient data

Patient Portals: Develop a framework that provides explicit information about the process of care to cancer patients in the context of their own medical records.

Probabilistic Models of Cerebral Aneurysms: Database and comprehensive model for optimizing intracranial aneurysm prognosis and treatment

RadPath: Automated construction of integrated radiology/pathology reports for referring physicians

DataServer: Open infrastructure for distributed (patient) data aggregation across healthcare and research information systems

http://www.mii.ucla.edu

Thank You

William Hsu, PhD Medical Imaging Informatics Group

Dept of Radiological Sciences willhsu@mii.ucla.edu

http://www.mii.ucla.edu/~willhsu

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