CTSI Module 8 Workshop Introduction to Biomedical Informatics, Part V

Transcription

1 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 1

2 Disclosures No financial disclosures. 2

3 Overview 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 3

4 Overview A variety of tools are available to help retrieve, transform, and explore clinical datasets Access to data Public data sources UC ReX Data Explorer Characterize variables Clean data Remove variables Transform variables OpenRefine Exploratory analysis Tableau Desktop 4

5 Access to Data: Public Sources / 5

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10 Access to Data: Imaging Studies 10

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12 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) 12

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15 UC ReX Data Explorer UC ReX Data Explorer Allows authorized UC researchers to query deidentified 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 15

16 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 16

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18 UC ReX Data Explorer 18

19 UC ReX Data Explorer 19

20 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

21 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) 21

22 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 22

23 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: 23

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25 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 25

26 Clustering 26

27 OpenRefine: Expression Language Expression value + (approved) value value.trim().length() value.substring(7,10) value.substring(13) Action Concatenate two strings; whatever is in value gets converted to a string first Add two numbers; if value actually holds something other than a number, this becomes a string concatenation Takes the length of value after trimming its leading and trailing whitespace Take the substring of value from character index 7 up to and excluding character index 10 Take the substring of value from character index 13 until the end of the string Source: 27

28 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 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) 28

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31 Medical Imaging Informatics Disease Models for Neurooncology: Tools to aid clinicians and researchers to create population-based models from routine, clinically acquired cancer patient data Probabilistic Models of Cerebral Aneurysms: Database and comprehensive model for optimizing intracranial aneurysm prognosis and treatment 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. Stroke Modeling: Create an observational database that enables the generation of an influence diagram for acute stroke treatment DataServer: Open infrastructure for distributed (patient) data aggregation across healthcare and research information systems RadPath: Automated construction of integrated radiology/pathology reports for referring physicians 31

32 Thank You William Hsu, PhD Medical Imaging Informatics Group Dept of Radiological Sciences 32