Background: The digital twin paradigm holds great promise for healthcare, most importantly efficiently integrating many disparate healthcare data sources and servicing complex tasks like personalizing care, predicting health outcomes, and planning patient care, even though many technical and scientific challenges remain to be overcome. Objective: As part of the QUALITOP project, we conducted a comprehensive analysis of diverse healthcare data, encompassing both prospective and retrospective datasets, along with an in-depth examination of the advanced analytical needs of medical institutions across five European Union countries. Through these endeavors, we have systematically developed and refined a formal Personal Medical Digital Twin (PMDT) model subjected to iterative validation by medical institutions to ensure its applicability, efficacy, and utility. Findings: The PMDT is based on an interconnected set of expressive knowledge structures that are calibrated to capture an individual patient’s psychosomatic, cognitive, biometrical and genetic information in one personal digital footprint in a manner that allows medical professionals to run various models to predict an individual’s health issues over time and intervene early with personalized preventive care.Conclusion: At the forefront of digital transformation, the PMDT emerges as a pivotal entity, positioned at the convergence of Big Data and Artificial Intelligence. This paper introduces a PMDT environment that lays the foundation for the application of comprehensive big data analytics, continuous monitoring, cognitive simulations, and AI techniques. By integrating stakeholders across the care continuum, including patients, this system enables the derivation of insights and facilitates informed decision-making for personalized preventive care.

1. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 875171
MEDICAL DIGITAL TWINS FOR PERSONALIZED CHRONIC CARE
ASSOC. PROF. AMAL ELGAMMAL
HEAD OF SOFTWARE DEPT., EGYPT UNIVERSITY OF INFORMATICS, EGYPT
ADJUNCT SENIOR RESEARCHER, SCIENTIFIC ACADEMY FOR SERVICE TECHNOLOGY
(SERVTECH), GERMANY

2. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 875171
MONITORING MULTIDIMENSIONAL ASPECTS OF QUALITY OF
LIFE AFTER CANCER IMMUNOTHERAPY:
AN OPEN SMART DIGITAL PLATFORM FOR PERSONALIZED
PREVENTION AND PATIENT MANAGEMENT
WP4 BIG HEALTH DATA SMART DIGITAL PLATFORM AND
SHARED DATA LAKE

3. AGENDA
• QUALITOP Project
• Motivation
• Problem Definition
• General Objective
• QUALITOP consortium
• WP4: Big Health Data Digital Platform and shared Datalake

4. QUALITOP: MOTIVATION
• Cancer immunotherapy research revolutionized medicine. Qualified as the fifth pillar of
cancer therapy after surgery, radiation, chemotherapy, and precision medicine, cancer
immunotherapy brought about significant progress in cancer treatment
• Compared to surgery, radiotherapy or chemotherapy, immunotherapy is considered one
of the most complicated treatment options because it activates the “body’s natural anti-
cancer immune response to attack and destroy cancer
 By enabling the immune system,
immunotherapy causes toxicities or side
effects that are challenging to predict
because they are not caused by
mechanisms involved in the other cancer
treatment types. These are referred to as
immune-related adverse events (IR-AEs).

5. QUALITOP: PROBLEM DEFINITION
• There are currently three main challenges that impede improving cancer patients’ QoL after
immunotherapy:
• First, there is a crucial need to determine “predictive markers” or “patients' sub-populations”
associated with the development of IR-AEs that help prevent, predict, and manage IR-AEs and improve
patient’s health status.
• A second barrier is the lack of knowledge regarding patients in the “real-life" after the start of
immunotherapy (life-style, polymedication to treat symptoms associated with IR-AEs).
To overcome the two previous challenges about the determinants of the “medical condition” (Health
status) and QoL, significantly more diversified sources of data and higher numbers of patients
should be included in research studies
• The recourse to a vast patients’ data heterogeneity (e.g., different cancer types, patients’
characteristics, and various sources of medical and patients life data) is a great opportunity, but it
would trigger major data complexities (e.g., high volume and velocity of supply) and make it
tremendously difficult to collect, aggregate, and analyse these data, especially within the context of
the General Data Protection Regulation (GDPR).

6. QUALITOP: GENERAL OBJECTIVE
“Develop and implement an IT-based European immunotherapy
platform and use big data analysis, artificial intelligence, and
simulation modelling approaches to collect and aggregate
efficiently and effectively real-world QoL data, monitor patients'
health status, conduct causal inference analyses, create harm-
reduction recommendations for patients and other stakeholders,
and disseminate the findings”

7. QUALITOPCONSORTIUM

8. IMPLEMENTATION OF QUALITOP THROUGH WPS
8
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 875171

9. IMPLEMENTATION
ARCHITECTURE OF
BIG HEALTH DATA
SMART DIGITAL
PLATFORM
9
Data Analytics
-Prediction
-Clustering
-Classification
QUALITOP Platform (Humhub)
QUALITOP Platform (Humhub)
Interactive
Dashborads
Interactive
Dashborads
Reporting &
Publishing
Reporting &
Publishing
Detection &
Alerts
Detection &
Alerts
APIs
APIs
Role-based
access
Role-based
access
Stakeholders (patients, clinicians,
nurses, family, friends, insurance,
public authorities, etc. )
Stakeholders (patients, clinicians,
nurses, family, friends, insurance,
public authorities, etc. )
WP4 ServTech
WP4 ServTech
...
...
Medical Data Lake
WP4 Servtech
WP4 Servtech
Google Cloud Platform (GCP)
Google Cloud Platform (GCP)
Effective
Data
Governance
Effective
Data
Governance
Role-Based
Access
Role-Based
Access
Data
Security
Data
Security
Data
Lifecycle
Data
Lifecycle
Admin.
Admin.
Central
Metadata
Repository
Central
Metadata
Repository
Metadata
Manager
Metadata
Manager
Patient
Medical
Digital
Twins
Patient
Medical
Digital
Twins
Metadata
mangmt.
Metadata
mangmt.
WP4 ServTech
WP4 ServTech
WP4 ServTech
WP4 ServTech
WP4
WP4
Connectors/
APIs
.
.
.
.
.
.
.
.
Connectors/
APIs
.
.
.
.
.
.
.
.
Connectors/
APIs
.
.
.
.
.
.
.
.
Connectors/
APIs
.
.
.
.
.
.
.
.
Pipelining
Pipelining
Aggregation
Aggregation
Edge Data Node
Edge Data Node Edge Data Node
Edge Data Node Edge Data Node
Edge Data Node Edge Data Node
Edge Data Node
Patient Medical
Digital Twins
Repository
Patient Medical
Digital Twins
Repository
Polystore
WP6
WP6
Connectors/
APIs
.
.
.
.
.
.
.
.
AB-DOM
Privacy
control
AB-DOM
Privacy
control
WP6
WP6
.
.
.
.
MiLA DSL
MiLA DSL
Definition DSL
Analytical
Query DSL Comp. DSL
Executable
Query
(Big) Data
Analytics
-Federated
Querying
WP4 Unical
WP4 Unical
.
.
.
.
.
.
.
.
.
.
.
.
ServTech
ServTech
Unical
Unical

10. DATA INTEROPERABILITY: STANDARDIZATION
Data
structure/technical
interchange
standard
Data value Standards
Schemas, data
element sets
expressed in machine-
readable form
Data content
Standards
Medical
Digital
Twins
Controlled
vocabularies,
thesauri, terms
coding schemes
Cataloging rules and
codes. Guidelines for the
format and syntax of the
data values that are used
to populate data
elements

11. MEDICAL DIGITAL TWINS
14
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 875171
CREATE A SMART HEALTH COMMUNITY/PLATFORM: an entity
that operates largely outside of the traditional health care system
centred around the after cancer ImmunoTherapy patient and
encourages disease prevention, improved QoL and overall well-
being in a virtual community setting in which stakeholders
respond more and more to mutual, shared challenges.
MEDICAL DIGITAL TWIN = Capturing and Tracking Patient Data
• construct a digital model of the patient (personal digital
replica) that can be used as a virtual test-bed for improved
QoL & future treatment;
• enable doctors and other healthcare providers to capture &
track patient data in order to tailor treatment to each patient;
• incorporate a variety of care data, including vital medical
information from medical records, current medication,
imaging studies, lifestyle, genetic, & patient-provided health
data from exercise or health monitoring applications &
medical pathways;
• improve post operative planning, reduce medical risks, and
generate more accurate therapy & improved QoL for patients.

12. MEDICAL
DIGITAL TWINS
MODELS

13. m
1
has
1
m undergoes
m
n
isPartOf
.
.
MedicalStakeholder
MedicalHistory
Medication
Disease (could reuse DO
ontology)
ClinicalExamination
BloodAnalysis
AdverseEvent
TreatmentFollowup
Surgery
PsycosocialInfo
CancerMedicalHistory
MelanomaCancerMedicalHistory
LungCancerMedicalHistory
LymphomaCancerMedicalHistory
...
...
MeduimTermAdverseEvent
ShortTermAdverseEvent
ICANS
CardiovascularToxcity
MacrophageActivationSyndr
ome
CytokineReleaseSyndrome
Infection
TumorLysisSyndrome
Graft-versus-hostDisease
BcellAplasiaHypogammaglob
ulinemia
InfectionsAntimicrobialProphylaxis
DelayedCytopenias
DelayedTLS/CRS/ICANS
Treatment
Vaccination
CancerTherapy
Test
Imaging
pathology
AELongTermFollowUP
1
m
has
1
0..m
has
1
m
isDiagnosedWith
PatientCancerDisease
MelanomaCancer
LungCancer
LymphomaCancer
GastroIntestinalCancer
1
m
wasDiagnosedWIth
GICancerMedicalHistory
CancerDisease ...
...
m
1
1
m
performs
MedicationFollowup
SurgeryFollowup
VaccinationFollowup
CancerTherapyFollowup
m 1
has
m 1
has
m 1
has
m 1
has
...
...
...
1
m
has
ChemoTherapy
RadiationTherapy
CARTTherapy
1
m
has
1
m
has
...
...
1
m
has
1
m
has
1
m
has
Patient
Physician
Nurse
SocialWorker
TreatmentPerformance
.
PsychologicalInfo
QualityOfLifeInfo
LifestyleInfo
NutritionInfo
MedicalAgency
m
1
contains
1
m
carriesOut
underwent
m
1
treatedBy
1
m
hasTests
Study
m
1 involves
1
m
has
DETAILED CONCEPTUAL ONTOLOGY MODEL OF THE PMDT

14. Data Homogenization & Federated Query Processing Tool
Security & Privacy-Preserving Tool
Hybrid Federated/Data-Mesh Management Approach
Hospital-1
Database
Exported schema
information &
meta-data
Hospital-2
Database
Exported schema
information &
meta-data
Hospital-3
Database
Exported schema
information &
meta-data
Local (edge) data sources
Results Aggregation
Query Translation
Storage of DB structure
and relationships meta-
data, unifying
knowledge model
entities &
correspondence to
databases.
Database Meta-data &
Knowledge Model
Repository
Query submission
Response/
answer
Tools & users
Access control,
Anonymisation
Local Data Model
Data Product
APIs
Access control,
Anonymisation
Local Data Model
Data Product
APIs
Access control,
Anonymisation
Local Data Model
Data Product
APIs
Retrospective
Data
Routing Logic

15. CONCEPTUAL
MODEL OF
MILA
QUERY
LANGUAGE

16. RECOMMEND FUTURE TREATMENT WITH
HIERARCHICAL ASSOCIATION RULE MINING
In order to have a more personalized experience while recommending
a treatment we found that the best approach is to first create patient
clusters based on the TNM staging system, which provides a
standardized approach to classifying cancer severity.
• T: Tumor size and extent of local invasion - This category describes
the size of the primary tumor and whether it has grown into nearby
tissues.
• N: Lymph node involvement - This category indicates whether
cancer cells have spread to the lymph nodes closest to the original
tumor.
• M: Distant metastasis - This category indicates whether cancer has
spread to distant parts of the body.
• For each cluster, we can utilize a technique called Hierarchical
Association Rule Mining (HARM) to identify treatment patterns.

17. HIERARCHICAL ASSOCIATION RULE MINING
PROCESS
1.Clustering: Patient data is segmented into clusters based
on TNM stage.
1.Apriori Algorithm: Within each cluster, the Apriori
algorithm analyzes treatment records to identify frequently
occurring treatment combinations.
1. Minimum support and confidence thresholds are set
to filter out less frequent associations.
2. This generates association rules that suggest
potential treatment plans for patients within the
cluster.
2.Expanding the Tree: For each identified treatment, we
can further analyze the data to find associations with
treatment dosage and duration. This can be achieved by
repeating the Apriori algorithm on the filtered data specific to
each treatment.

18. PREDICTING QUALITY OF LIFE WITH MULTI-FACETED
The Importance of QOL: A
patient's Quality of Life (QOL)
after diagnosis is crucial for
their well-being and ability to
cope with treatment.
Our Model's Goal: We present
a novel AI model designed to
predict QOL for cancer patients,
aiding in personalized
treatment plans and improved
patient outcomes.

19. PREDICTING QUALITY OF LIFE WITH MULTI-FACETED MODEL
Data Integration:
Combine data from
multiple sources that reflect
different aspects of QOL
(e.g., emotional state, social
support, daily activities).
Sub-model Training:
Train individual models on
each data subset to capture
specific QOL factors.
Evaluate each model's
effectiveness in predicting
QOL based on its data.
Ensemble Approach:
Combine the predictions
from the sub-models using
a weighted averaging
strategy.
This leverages the strengths
of each sub-model to create
a more robust prediction.
Model Refinement:
Evaluate the final model's
performance on unseen
data.
Refine the models and
ensemble approach based
on the evaluation results to
improve prediction
accuracy.

20. Questions?
Comments?
Amal.Elgammal@eui.edu.eg
Homepage: https://eui.edu.eg/en/faculty-members-list/dr-amal-
elgammal/

21. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 875171