Difference between revisions of "User:Shawndouglas/sandbox/sublevel8"

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| type      = notice
| type      = notice
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| text      = This is sublevel1 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p>
| text      = This is sublevel8 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p>
}}
}}


==Sandbox begins below==
==Sandbox begins below==
[[File:Battery Manufacturing Lab (50954228316).jpg|right|500px]]
'''Title''': ''LIMS Selection Guide for Manufacturing Quality Control''
'''Edition''': First Edition
'''Author for citation''': Shawn E. Douglas
'''License for content''': [https://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International]
'''Publication date''': To be determined
To be written...
The table of contents for ''LIMS Selection Guide for Manufacturing Quality Control'' is as follows:


{{Infobox journal article
|name        =
|image        =
|alt          = <!-- Alternative text for images -->
|caption      =
|title_full  = Approaches to Medical Decision-Making Based on Big Clinical Data
|journal      = ''Journal of Healthcare Engineering''
|authors      = Malykh, V.L.; Rudetskiy, S.V.
|affiliations = Ailamazyan Program Systems Institute of RAS
|contact      = Email: mvl at interin dot ru
|editors      =
|pub_year    = 2018
|vol_iss      = '''2018'''
|pages        = 3917659
|doi          = [http://10.1155/2018/3917659 10.1155/2018/3917659]
|issn        = 2040-2309
|license      = [http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
|website      = [https://www.hindawi.com/journals/jhe/2018/3917659/ https://www.hindawi.com/journals/jhe/2018/3917659/]
|download    = [http://downloads.hindawi.com/journals/jhe/2018/3917659.pdf http://downloads.hindawi.com/journals/jhe/2018/3917659.pdf] (PDF)
}}
{{ombox
| type      = content
| style    = width: 500px;
| text      = This article should not be considered complete until this message box has been removed. This is a work in progress.
}}
==Abstract==
The paper discusses different approaches to building a [[clinical decision support system]] based on big data. The authors sought to abstain from any data reduction and apply universal teaching and big data processing methods independent of disease classification standards. The paper assesses and compares the accuracy of recommendations among three options: case-based reasoning, simple single-layer neural network, and probabilistic neural network. Further, the paper substantiates the assumption regarding the most efficient approach to solving the specified problem.


==Introduction==
<!--:[[LII:LIMS Selection Guide for Manufacturing Quality Control/Introduction to manufacturing laboratories|1. Introduction to manufacturing laboratories]]//-->
Providing support to clinical decision-making is one of the most urgent issues in healthcare automation. It has been repeatedly noted in different articles, reports, and forum discussions<ref name="Medsoft2016">{{cite web |url=http://www.armit.ru/medsoft/2016/conference/prog/ |title=Presentations of the 12th International Forum "MedSoft-2016" |publisher=Association for the Development of Medical Information Technologies |date=2016}}</ref> both in Russia and abroad that medical information system (MIS) introduction requires a considerable extra effort from users/doctors in the first place—to enter primary data into the system. Naturally, doctors expect practical intelligent outcomes from big clinical data accumulated by modern MISs. Handler ''et al.''<ref name="HandlerGartner07">{{cite web |url=https://www.gartner.com/doc/508592/gartners--criteria-enterprise-cpr |title=Gartner's 2007 Criteria for the Enterprise CPR |author=Handler, T.J.; Hieb, B.R. |publisher=Gartner, Inc |date=2007}}</ref> present the operating paradigm of fifth generation MISs, referred to as “MIS as Mentor.” Malykh ''et al.''<ref name="MalykhActive16">{{cite journal |title=Active MIS |journal=Information Technologies for the Physician |author=Malykh, V.L.; Rudetskiy, S.V.; Hatkevich, M.I. |volume=2016 |issue=6 |year=2016}}</ref> adds one more qualitative characteristic to the above paradigm—“MIS as automated mentor.”
:[[User:Shawndouglas/sandbox/sublevel9|1. Introduction to manufacturing laboratories]]
::1.1 Manufacturing labs, then and now
::1.2 Laboratory roles and testing in the industry
:::1.2.1 R&D roles and testing
:::1.2.2 Pre-manufacturing and manufacturing roles and testing
:::1.2.3 Post-production regulation and security roles and testing
:::1.2.4 Tangential laboratory work
::1.3 Safety and quality in the manufacturing industry


<blockquote>It is advisable to abandon the practice of active user dialogs typical of expert systems, involving requests for data that the system considers missing from the user, and substitute the dialog with an automated nonintrusive algorithm that draws its own logical conclusions and generates recommendations in a completely automated manner based on available data, without involving the user in the process. The user may either accept or ignore the system’s prompts and recommendations; however, they will not provoke rejection in users if delivered automatically without requiring a dialog with the system.<ref name="MalykhActive16" /></blockquote>
<!--:[[LII:LIMS Selection Guide for Manufacturing Quality Control/Standards and regulations affecting manufacturing labs|2. Standards and regulations affecting manufacturing labs]]//-->
:[[User:Shawndouglas/sandbox/sublevel10|2. Standards and regulations affecting manufacturing labs]]
::2.1 Globally recognized manufacturing standards
:::2.1.1 Food and beverage
:::2.1.2 Materials
:::2.1.3 Pharmaceutical and medical devices
:::2.1.4 Other industries
::2.2 Regulations and laws around the world
:::2.2.1 Food and beverage
:::2.2.2 Materials
:::2.2.3 Pharmaceutical and medical devices
:::2.3.4 Other industries
::2.3 Other influencing factors
:::2.3.1 Good manufacturing practice (GMP) and current good manufacturing practice (cGMP)
:::2.3.2 Standards and Scientific Advice on Food and Nutrition (SSA)


To provide a brief qualitative description of this increasing subjectivity of MISs, we have proposed the new term “active MIS” that emphasizes a certain degree of independence from users or subjectivity of the cyber system. Kohane<ref name="KohaneTheTwin09">{{cite journal |title=The twin questions of personalized medicine: who are you and whom do you most resemble? |journal=Genome Medicine |author=Kohane, I.S. |volume=1 |issue=1 |page=4 |year=2009 |doi=10.1186/gm4 |pmid=19348691 |pmc=PMC2651581}}</ref> presents the most “balanced” definition of personalized medicine: “personalized medicine is the practice of clinical decision-making such that the decisions made maximize the outcomes that the patient most cares about and minimize those that the patient fears the most, on the basis of as much knowledge about the individual’s state as is available.” This perception of personal medicine is focused on clinical decision-making and once again exhibits the urgency and importance of scientific research in the area. Therefore, building an automated active mentor-type system that provides recommendations regarding treatment and diagnostic activities to the doctor is an urgent practical task.
<!--:[[LII:LIMS Selection Guide for Manufacturing Quality Control/Choosing laboratory informatics software for your manufacturing lab|3. Choosing laboratory informatics software for your manufacturing lab]]//-->
:[[User:Shawndouglas/sandbox/sublevel11|3. Choosing laboratory informatics software for your manufacturing lab]]
::3.1 Evaluation and selection
:::3.1.1 Technology considerations
::::3.1.1.1 Laboratory informatics options
:::3.1.2 Features and functions
::::3.1.2.1 Base features
::::3.1.2.2 Specialty features
:::3.1.3 Cybersecurity considerations
:::3.1.4 Regulatory compliance considerations
:::3.1.5 System flexibility
:::3.1.6 Cost considerations
::3.2 Implementation
:::3.2.1 Internal and external integrations
::3.3 MSW, updates, and other contracted services
::3.4 How a user requirements specification fits into the entire process (LIMSpec)


Butko and Olshansky<ref name="ButkoNew90">{{cite journal |title=New Decision Support Systems in Foreign Healthcare |journal=Automation and Remote Control |author=Butko, S.N.; Olshansky, V.K. |volume=51 |year=1990}}</ref> and Kotov<ref name="KotovNew04">{{cite book |chapter=New Mathematical Approaches to Medical Diagnostics |title=Editorial URSS |author=Kotov, Y.B. |year=2004}}</ref> provide a retrospective overview of approaches to building clinical decision support systems. The applied approaches were restricted in many respects by the abilities of computers at that time. Accordingly, there was no such problem as processing big medical data. Technologies have evolved to the point where big medical data (both on individuals and the population in general) collection and accumulation is finally feasible. At the same time, big data processing and intelligent system learning methods were evolving as well. Along with “deep learning,” the term “deep patient”<ref name="MiottoDeep16">{{cite journal |title=Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records |journal=Scientific Reports |author=Miotto, R.; Li, L.; Kidd, B.A. et al. |volume=6 |page=26094 |year=2016 |doi=10.1038/srep26094}}</ref> was coined, meaning the opportunity to extract increasingly more complete, deep, and valuable [[information]] about patients from big clinical data using deep learning methods.
<!--::[[LII:LIMS Selection Guide for Manufacturing Quality Control/Resources for selecting and implementing informatics solutions|4. Resources for selecting and implementing informatics solutions]]//-->
:[[User:Shawndouglas/sandbox/sublevel12|4. Resources for selecting and implementing informatics solutions]]
::4.1 LIMS vendors
::4.2 Consultants
::4.3 Professional
:::4.3.1 Trade organizations
:::4.3.2 Conferences and trade shows
::4.4 LIMSpec


Malykh ''et al.''<ref name="MalykhCase15">{{cite journal |title=Case-based reasoning in clinical processes using clinical data banks |journal=Proceedings from the 2015 International Conference on Biomedical Engineering and Computational Technologies |author=Malykh, V.L.; Belyshev, D.V. |pages=211-216 |year=2015 |doi=10.1109/SIBIRCON.2015.7361885}}</ref> mention the possibility of creating national-scale clinical data banks. Herrett ''et al.''<ref name="HerrettData15">{{cite journal |title=Data Resource Profile: Clinical Practice Research Datalink (CPRD) |journal=International Journal of Epidemiology |author=Herrett, E.; Gallagher, A.M.; Bhaskaran, K. et al. |volume=44 |issue=3 |pages=827-36 |year=2015 |doi=10.1093/ije/dyv098 |pmid=26050254 |pmc=PMC4521131}}</ref> provide an example of a database (DB) containing anonymous medical records on primary healthcare services provided. This DB was created by a joint effort of 674 general practitioners and covers over 11.3 million patients in Great Britain.
<!--::[[LII:LIMS Selection Guide for Manufacturing Quality Control/Taking the next step|5. Taking the next step]]//-->
:[[User:Shawndouglas/sandbox/sublevel13|5. Taking the next step]]
::5.1 Conduct initial research into a specification document tailored to your lab's needs
::5.2 Issue some of the specification as part of a request for information (RFI)
::5.3 Respond to or open dialogue with vendors
:::5.3.1 The value of demonstrations
::5.4 Finalize the requirements specification and choose a vendor


==References==
<!--::[[LII:LIMS Selection Guide for Manufacturing Quality Control/Closing remarks|6. Closing remarks]]//-->
{{Reflist|colwidth=30em}}
:[[User:Shawndouglas/sandbox/sublevel14|6. Closing remarks]]


==Notes==
<!--::[[LII:LIMS Selection Guide for Manufacturing Quality Control/Blank LIMSpec template for manufacturing labs|Appendix 1. Blank LIMSpec template for manufacturing labs]]//-->
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
:[[User:Shawndouglas/sandbox/sublevel15|Appendix 1. Blank LIMSpec template for manufacturing labs]]
::A1. Introduction and methodology
::A2. Primary laboratory workflow
::A3. Maintaining laboratory workflow and operations
::A4. Specialty laboratory functions
::A5. Technology and performance improvements
::A6. Security and integrity of systems and operations
::A7. Putting those requirements to practical use and caveats
::A8. LIMSpec in Microsoft Word format


<!--Place all category tags here-->
<!---Place all category tags here-->
[[Category:LIMSwiki journal articles (added in 2018)‎]]
[[Category:LIMSwiki journal articles (all)‎]]
[[Category:LIMSwiki journal articles on big data]]
[[Category:LIMSwiki journal articles on health informatics‎‎]]

Latest revision as of 21:44, 21 March 2023

This is sublevel8 of my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see my discussion page instead.

Sandbox begins below

Battery Manufacturing Lab (50954228316).jpg

Title: LIMS Selection Guide for Manufacturing Quality Control

Edition: First Edition

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: To be determined


To be written...

The table of contents for LIMS Selection Guide for Manufacturing Quality Control is as follows:


1. Introduction to manufacturing laboratories
1.1 Manufacturing labs, then and now
1.2 Laboratory roles and testing in the industry
1.2.1 R&D roles and testing
1.2.2 Pre-manufacturing and manufacturing roles and testing
1.2.3 Post-production regulation and security roles and testing
1.2.4 Tangential laboratory work
1.3 Safety and quality in the manufacturing industry
2. Standards and regulations affecting manufacturing labs
2.1 Globally recognized manufacturing standards
2.1.1 Food and beverage
2.1.2 Materials
2.1.3 Pharmaceutical and medical devices
2.1.4 Other industries
2.2 Regulations and laws around the world
2.2.1 Food and beverage
2.2.2 Materials
2.2.3 Pharmaceutical and medical devices
2.3.4 Other industries
2.3 Other influencing factors
2.3.1 Good manufacturing practice (GMP) and current good manufacturing practice (cGMP)
2.3.2 Standards and Scientific Advice on Food and Nutrition (SSA)
3. Choosing laboratory informatics software for your manufacturing lab
3.1 Evaluation and selection
3.1.1 Technology considerations
3.1.1.1 Laboratory informatics options
3.1.2 Features and functions
3.1.2.1 Base features
3.1.2.2 Specialty features
3.1.3 Cybersecurity considerations
3.1.4 Regulatory compliance considerations
3.1.5 System flexibility
3.1.6 Cost considerations
3.2 Implementation
3.2.1 Internal and external integrations
3.3 MSW, updates, and other contracted services
3.4 How a user requirements specification fits into the entire process (LIMSpec)
4. Resources for selecting and implementing informatics solutions
4.1 LIMS vendors
4.2 Consultants
4.3 Professional
4.3.1 Trade organizations
4.3.2 Conferences and trade shows
4.4 LIMSpec
5. Taking the next step
5.1 Conduct initial research into a specification document tailored to your lab's needs
5.2 Issue some of the specification as part of a request for information (RFI)
5.3 Respond to or open dialogue with vendors
5.3.1 The value of demonstrations
5.4 Finalize the requirements specification and choose a vendor
6. Closing remarks
Appendix 1. Blank LIMSpec template for manufacturing labs
A1. Introduction and methodology
A2. Primary laboratory workflow
A3. Maintaining laboratory workflow and operations
A4. Specialty laboratory functions
A5. Technology and performance improvements
A6. Security and integrity of systems and operations
A7. Putting those requirements to practical use and caveats
A8. LIMSpec in Microsoft Word format


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