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ASTM E2145-07

(Practice)

Standard Practice for Information Modeling

Standard Practice for Information Modeling

SCOPE
1.1 Information models are increasingly important in the analysis, design, and sharing a common understanding in information engineering, in business process improvement, in building information systems, in developing informatics standards, and in many other uses.
1.2 The purpose of this practice is to identify best practices for the creation, use, and assessment of various types of information models.
1.3 Included in this practice are recommended organizational policies and procedures, where modeling is best used and recommended modeling methods, best practices and evaluation criteria.
1.4 Excluded from this practice are detailed specifications of modeling techniques that are specified or described in other sources.

General Information

Status
Historical
Publication Date
31-May-2007
ICS
35.240.80 - IT applications in health care technology
Technical Committee
E31 - Healthcare Informatics
Drafting Committee
E31.25 - Healthcare Data Management, Security, Confidentiality, and Privacy
Current Stage
Ref Project

Relations

Replaces
ASTM E2145-01 - Standard Practice for Modeling in Health Informatics
Effective Date
01-Jun-2007
Replaced By
ASTM E2145-07(2013) - Standard Practice for Information Modeling (Withdrawn 2017)
Effective Date
01-Mar-2013

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ASTM E2145-07 - Standard Practice for Information Modeling
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NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: E2145 − 07 AnAmerican National Standard
Standard Practice for
1
Information Modeling
This standard is issued under the fixed designation E2145; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope* ISO/IEC 19501:2005 Information technology—Open Dis-
tributed Processing—Unified Modeling Language (UML)
1.1 Information models are increasingly important in the
Version 1.4.2
analysis, design, and sharing a common understanding in
ISO/IEC 2382 Information Processing Systems—
information engineering, in business process improvement, in
Vocabulary
building information systems, in developing informatics
4
2
2.3 Other Documents and Standards:
standards, and in many other uses.
Unified Modeling Language Specification version 1.5,
1.2 The purpose of this practice is to identify best practices
March 2003
for the creation, use, and assessment of various types of
information models. 3. Terminology
1.3 Included in this practice are recommended organiza- 3.1 The following sections present terms, definitions, and
tional policies and procedures, where modeling is best used acronyms found in this practice and in various modeling
andrecommendedmodelingmethods,bestpracticesandevalu- activities.Thesetermsanddefinitionsarereferencedorderived
ation criteria. from ANSI X3.172 or ISO/IEC 2382 unless otherwise cited.
1.4 Excluded from this practice are detailed specifications 3.2 Definitions:
of modeling techniques that are specified or described in other 3.2.1 activity, n—a group of logically related tasks per-
sources. formed for a purpose.
3.2.2 alternate key attribute, n—inalogicaldatamodel,any
2. Referenced Documents
candidate key of an entity other than the primary key.
3
2.1 ANSI Standards:
3.2.3 application model, n—a representation or description
ANSI X3.172-1990 Dictionary for Information Systems
of the application software, programs, or components needed
IEEE 1320.1-1998 Standard for Functional Modeling
to support a business function.
Language—Syntax and Semantics for IDEF0
3.2.4 attribute, n—a characteristic of an object or entity (see
IEEE 1320.2-1998 Standard for Functional Modeling
ISO/IEC 11179).
Language—Syntax and Semantics for IDEF1X (Object
3.2.5 attribute value, n—a representation of an instance of
97)
3 an attribute (see ISO/IEC 11179).
2.2 ISO Standards:
3.2.6 behavior, n—in the object-oriented methodology, be-
ISO 8601-88 Data Elements and Interchange Formats—
havior constitutes the observable effects of an operation or
Representation of Dates and Times
event, including any results generated or obtained; and repre-
ISO/IEC 1087 Terminology Work—Vocabulary-Part 1:
sented by operations, methods, and state machines (see UML
Theory and Application
1.5).
ISO/IEC11179 InformationTechnology—Specificationand
Standardization of Data Elements, Parts 1-6
3.2.7 business model, n—a representation of the strategy,
situation, environment, objectives, direction, and similar char-
acteristics of a business enterprise or business area.
1
This practice is under the jurisdiction ofASTM Committee E31 on Healthcare
3.2.8 business process model, n—a representation of busi-
Informatics and is the direct responsibility of Subcommittee E31.25 on Healthcare
Data Management, Security, Confidentiality, and Privacy.
ness processes, often where these processes are successively
CurrenteditionapprovedJune1,2007.PublishedJuly2007.Originallyapproved
decomposed to describe component activities, to identify the
in 2001. Last previous edition approved in 2001 as E2145 – 01. DOI: 10.1520/
events to which the business shall respond, and to identify the
E2145-07.
2
An information model in the context of this standard is any representation of results produced.
process, data, etc., used in any aspect of information technology or information
management.
3 4
Available fromAmerican National Standards Institute (ANSI), 25 W. 43rd St., Available from Object Management Group. OMG Headquarters, 250 First
4th Floor, New York, NY 10036, http://www.ansi.org. Avenue, Needham, MA 02494. http://www.omg.org.
*A Summary of Changes section appears at the end of this standard
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1

---------------------- Page: 1 ----------------------
E2145 − 07
3.2.9 column, n—a physical data model and relational data- 3.2.24 function, n—an activity, process or transformation
base structure that is analogous to the attribute of a logical data identified by a verb that describes that which would be
model. accomplished.
3.2.10 concept, n—a unit of thought con
...

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1.2 The first purpose of this specification is to defin...

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4.1 Purpose—Practices to be employed for the radiographic examination of materials and components with neutrons using digital neutron detectors are outlined herein. They are intended as a guide for the assessment of a digital neutron radiograph’s characteristics. For information on neutron beam lines for imaging and film neutron radiography, refer to Guide E748.  
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1.1 This guide covers the evaluation, qualification, and quantification of digital neutron images. These images can be acquired by many methods, including: neutron sensitive imaging plates (Computed Radiography – CR), Digital Detector Arrays – DDA’s (amorphous silicon, CMOS, CCD, etc.), micro-channel plates, neutron sensitive fluoroscopes, neutron sensitive scintillators coupled to optical cameras, digitized radiographic films, and linear diode arrays.  
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SIGNIFICANCE AND USE
5.1 Personnel that are responsible for the creation, transfer, and storage of digital X-ray test results will use this standard. This practice defines a set of information modules that, along with Practice E2339 and the DICOM standard, provides a standard means to organize digital X-ray test parameters and results. The digital X-ray test results may be displayed and analyzed on any device that conforms to this standard. Personnel wishing to view any digital X-ray inspection data stored according to Practice E2339 may use this document to help them decode and display the data contained in the DICONDE-compliant inspection record.
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1.3.3 The overall set of features and functions to be expected from a system implemented by integrating a group of devices each claiming DICONDE conformance.  
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1.1 This guide provides a listing and description of the fields that are recommended for inclusion in a digital radiological examination data base to facilitate the transfer of such data. This guide sets guidelines for the format of data fields for computerized transfer of digital image files obtained from radiographic, radioscopic, computed radiographic, or other radiological examination systems. The field listing includes those fields regarded as necessary for inclusion in the data base: (1) regardless of the radiological examination method (as indicated by Footnote C in Table 1), (2) for radioscopic examination (as indicated by Footnote F in Table 1), and (3) for radiographic examination (as indicated by Footnote D in Table 1). In addition, other optional fields are listed as a reminder of the types of information that may be useful for additional understanding of the data or applicable to a limited number of applications.  
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5.1 The purpose of this practice is to provide data that can be used for comparison and evaluation of the accuracy of different CAS systems.  
5.2 The use of CAS systems and robotic tracking systems is becoming increasingly common and requires a degree of trust by the user that the data provided by the system meets necessary accuracy requirements. In order to evaluate the potential use of these systems, and to make informed decisions about suitability of a system for a given procedure, objective performance data of such systems are necessary. While the end user will ultimately want to know the accuracy parameters of a system under clinical application, the first step must be to characterize the digitization accuracy of the tracking subsystem in a controlled environment under controlled conditions.  
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SCOPE
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5.1 The purpose of this practice is to provide data that can be used for evaluation of the accuracy of different CAS systems.  
5.2 The use of surgical navigation and robotic positioning systems is becoming increasingly common. In order to make informed decisions about the suitability of such systems for a given procedure, their accuracy capability needs to be evaluated under clinical application and compared to the requirements. As the performance of a whole system is constrained by those of its subparts, a preliminary step must be to objectively characterize the accuracy of the tracking subsystem in a controlled environment under controlled conditions.  
5.3 In order to make comparisons within and between systems, a standardized way of measuring and reporting accuracy is needed. Parameters such as coordinate system, units of measurement, terminology, and operational conditions must be standardized.
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SIGNIFICANCE AND USE
5.1 Personnel that are responsible for the creation, transfer, and storage of X-ray tomographic NDE data will use this standard. This practice defines a set of information modules that along with Practice E2339 and the DICOM standard provide a standard means to organize X-ray tomography test parameters and results. The X-ray CT test results may be displayed and analyzed on any device that conforms to this standard. Personnel wishing to view any tomographic inspection data stored according to Practice E2339 may use this document to help them decode and display the data contained in the DICONDE-compliant inspection record.
SCOPE
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1.3 This practice does not specify:  
1.3.1 A testing or validation procedure to assess an implementation's conformance to the standard.  
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1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.  
1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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Standard Guide for Multivariate Data Analysis in Pharmaceutical Development and Manufacturing Applications

SIGNIFICANCE AND USE
4.1 A significant amount of data is generated during pharmaceutical development and manufacturing activities. The interpretation of such data is becoming increasingly difficult. Individual examination of the univariate process variables is relevant but can be significantly complemented by multivariate data analysis (MVDA). MVDA may be particularly appropriate for exploring and handling large sets of heterogenous data, mapping data of high dimensionality onto lower dimensional representations, exposing significant correlations among multivariate variables within a single data set or significant correlations among multivariate variables across data sets. MVDA may extract statistically significant information which may enhance process understanding, decision making in process development, process monitoring and control (including product release), product life-cycle management, and continuous improvement.  
4.2 MVDA is widely used in various industries including the pharmaceutical industry. To achieve a valid outcome, an MVDA model/application should incorporate the following:  
4.2.1 A predefined risk-based objective incorporating one or more relevant scientific hypotheses specific to the application;  
4.2.2 Sufficient relevant data of requisite quality covering the variance space encountered during intended use, that is, pharmaceutical development, or pharmaceutical manufacturing, or both;  
4.2.3 Appropriate data analysis and model utilization practices including considerations on testing, validation, and qualification of all new data prior to using a model to analyze it;  
4.2.4 Appropriately trained staff;  
4.2.5 Appropriate standard operating procedures; and  
4.2.6 Life-cycle management.  
4.3 This guide can be used to support data analysis activities associated with pharmaceutical development and manufacturing, process performance and product quality monitoring in manufacturing, as well as for troubleshooting and investigation events. Technical detai...
SCOPE
1.1 This guide covers the applications of multivariate data analysis (MVDA) to support pharmaceutical development and manufacturing activities. MVDA is one of the key enablers for process understanding and decision making in pharmaceutical development, and for the release of intermediate and final products after being validated appropriately using a science and risk-based approach.  
1.2 The scope of this guide is to provide general guidelines on the application of MVDA in the pharmaceutical industry. While MVDA refers to typical empirical data analysis, the scope is limited to providing a high level guidance and not intended to provide application-specific data analysis procedures. This guide provides considerations on the following aspects:  
1.2.1 Use of a risk-based approach (understanding the objective requirements and assessing the fit-for-use status);  
1.2.2 Considerations on the data collection and diagnostics used for MVDA (including data preprocessing and outliers);  
1.2.3 Considerations on the different types of data analysis, model testing, and validation;  
1.2.4 Qualified and competent personnel; and  
1.2.5 Life-cycle management of MVDA model.  
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.  
1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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  • Guide
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ASTM
ASTM E1935-97(2019)(Test Method)
Standard Test Method for Calibrating and Measuring CT Density

SIGNIFICANCE AND USE
5.1 This test method allows specification of the density calibration procedures to be used to calibrate and perform material density measurements using CT image data. Such measurements can be used to evaluate parts, characterize a particular system, or compare different systems, provided that observed variations are dominated by true changes in object density rather than by image artifacts. The specified procedure may also be used to determine the effective X-ray energy of a CT system.  
5.2 The recommended test method is more accurate and less susceptible to errors than alternative CT-based approaches, because it takes into account the effective energy of the CT system and the energy-dependent effects of the X-ray attenuation process.
FIG. 1 Density Calibration Phantom  
5.3 This (or any) test method for measuring density is valid only to the extent that observed CT-number variations are reflective of true changes in object density rather than image artifacts. Artifacts are always present at some level and can masquerade as density variations. Beam hardening artifacts are particularly detrimental. It is the responsibility of the user to determine or establish, or both, the validity of the density measurements; that is, they are performed in regions of the image which are not overly influenced by artifacts.  
5.4 Linear attenuation and mass attenuation may be measured in various ways. For a discussion of attenuation and attenuation measurement, see Guide E1441 and Practice E1570.
SCOPE
1.1 This test method covers instruction for determining the density calibration of X- and γ-ray computed tomography (CT) systems and for using this information to measure material densities from CT images. The calibration is based on an examination of the CT image of a disk of material with embedded specimens of known composition and density. The measured mean CT values of the known standards are determined from an analysis of the image, and their linear attenuation coefficients are determined by multiplying their measured physical density by their published mass attenuation coefficient. The density calibration is performed by applying a linear regression to the data. Once calibrated, the linear attenuation coefficient of an unknown feature in an image can be measured from a determination of its mean CT value. Its density can then be extracted from a knowledge of its mass attenuation coefficient, or one representative of the feature.  
1.2 CT provides an excellent method of nondestructively measuring density variations, which would be very difficult to quantify otherwise. Density is inherently a volumetric property of matter. As the measurement volume shrinks, local material inhomogeneities become more important; and measured values will begin to vary about the bulk density value of the material.  
1.3 All values are stated in SI units.  
1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.  
1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

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