Wiki source code of Onboarding to the Medical Informatics Platform MIP

Version 17.1 by bschaffha on 2024/10/10 13:48

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6.1	5	= Onboarding to the Medical Informatics Platform MIP =
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6.1	7	Step-by-step guidance
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	15	= What can I find here? =
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7.1	17	* Creation of a MIP User Account
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	34	[[image:https://mip.ebrains.eu/img/section1.2b1f04df.png\|\|alt="MIP user interface"]]
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12.1	36	//Figure 1: User Interface of the Medical Informatics Platform MIP//
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12.1	40	== Creation of a MIP User Account ==
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15.1	42	Prerequisite – Step 1: Access to the MIP requires an EBRAINS user account, which needs to be permitted and authenticated. EBRAINS user accounts are available to users with a legitimate interest (mainly research and development) from Europe and beyond.
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15.1	44	Request an EBRAINS user account: [[https:~~/~~/www.ebrains.eu/page/sign-up>>url:https://www.ebrains.eu/page/sign-up]]
	45	The EBRAINS user account allows users to directly access the Public MIP ([[https:~~/~~/mip.ebrains.eu/>>url:https://mip.ebrains.eu/]]) with no further accreditation being required.
	46	EBRAINS authorised Users with an active EBRAINS account can request access to a specific federation by contacting [[support@ebrains.eu>>path:mailto:support@ebrains.eu]], who will forward the specific request to the MIP Management team.
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15.1	48	Users can also get in direct contact with the MIP team via the online form on the EBRAINS website: [[https:~~/~~/www.ebrains.eu/tools/medical-informatics-platform>>url:https://www.ebrains.eu/tools/medical-informatics-platform]]
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15.1	50	The Data Science Steering Committee (DSSC) of the specific federation will be involved in the accreditation process to receive access approvals.
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15.1	52	New federated projects can be initiated at any time. Users are required to accept the EBRAINS Terms and Policies [[https:~~/~~/www.ebrains.eu/page/terms-and-policies>>url:https://www.ebrains.eu/page/terms-and-policies]], to indicate acceptance and compliance with all applicable laws, regulations, rules, and approvals in the use and sharing of the data, including, but not limited to, the General Data Protection Regulation (GDPR).
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15.1	54	Upon login to the MIP, users are mandated to accept the Terms of Use of the MIP. Accredited users access the MIP through a web-based interface, which will provide them with access to the respective federation on the MIP.
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14.1	56	== The MIP Data Governance ==
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	58	[[image:1728560441625-436.png]]
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	60	Figure 2: MIP Data Governance Flow
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16.2	62	//T(% class="small" %)his illustration depicts how data governance and data flow in the MIP are organised and how the legal framework and data management are interlinked. Decision points are indicated.//
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	64	=== MIP concepts and definitions ===
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	66	* Common Data Elements (CDEs)
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	68	A set of standard variables defined by clinical experts and data scientists, which would be used by researchers to perform analysis on specific medical conditions at the federation level. In the MIP context, we use the term CDEs to refer to the standardised federated datamodels only.
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	70	* Data Element
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	72	In metadata, the term data element is an atomic unit of data that has precise meaning or precise semantics.
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	74	* Datamodel (Metadata)
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	76	A Datamodel (Metadata) describes the structure of database variables found in specific extracts of a hospital database, including descriptive metadata, structural metadata, administrative metadata, reference metadata and statistical metadata.
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	78	* Database Variables
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	80	A variable or scalar is a storage address (identified by an index or address) paired with an associated symbolic name, which contains some known or unknown quantity of information referred to as a value.
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	82	* Electronic Health Records (EHR)
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	84	Health information and clinical records registered per each patient per visit in the hospital's database (Oracle, SQL, or any other database system) and usually transferred in db or CSV format. EHRs usually contain different levels of data; we might define them in this context as spaces, domain, and sub-domain. For example, a space might include demographics, social status, or patient's medical history as different data domains. On the other hand, EHR contain other data spaces related to the specific medical condition such as Dementia or Epilepsy where each space includes specific domain and sub-domain, such as medical assessments and tests, diagnoses, treatment, and operations, etc.
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	86	* Medical Conditions
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	88	Diseases are often known to be medical conditions that are associated with specific symptoms and signs.
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16.2	90	== MIP Data Flow ==
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	92	[[image:1728560774193-188.png]]
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	96	Figure 3// MIP Data Flow//
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	98	//T(% class="small" %)his diagramme illustrates the MIP Data Flow, indicating processing steps prior to data upload and steps after data upload to the MIP. EHR – electronic health record, MRI - magnetic resonance imaging, ETL - data integration (extract, transform, load), CDE – common data elements, ML – machine learning, GUI – graphical user interface, VM – virtual machine. Data pre-processing: extract data from EHR records and produce pseudonymised data in .csv format; optional Step1: extract brain volumes from MRI images and merge with data extracted from EHR records; Data Quality and Harmonisation: Prepare CDE: if CDE exists – Steps 2B, 4 and 5 are followed; if CDE needs to be prepared, first Steps 2A and 3A need to be performed, followed by Steps 2B, 4 and 5. Data Analysis and ML: anonymised dataset is uploaded either to the federated node in the institution or the dedicated VM on EBRAINS CSCS. Data Analysis can be performed via the Federation Service Layer and User Interface: use of predefined federated algorithms, aggregated results will be retrieved via the GUI.//
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