Big Data and Semantic-based Digital Public Health: focus ...linkedvocabs.org/bisw19/assets/semweb-gayo.pdf · Various topics covered › optimizing bus routes, analyzing social divisions,

Gayo Diallo ERIAS ISPED INSERM U1219 & LaBRI

[email protected]

Big Data and Semantic-based Digital Public Health: focus on Low and Middle Income Countries

ERIAS Web Site https://bit.ly/2zwrrcu

BI & SemWeb @ CRI'19 - Yaoundé, 02/12/2019

https://bit.ly/2zwrrcu


University of Bordeaux – Environment

➔ The city of Bordeaux (South West of France), a UNESCO World Heritage Site

➔ The University of Bordeaux welcomes over 50,000 students and nearly 6,000 staff members. About 6,000 students come from abroad

➔ The University of Bordeaux has one of the largest campuses (260 hectares) in Europe

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Source: google®

Mobile and Web-based Data Analysis and Public Health 4 décembre 2019 4 dé b 2019 G DIALLO BI & S W b @ CRI'19

University of Bordeaux

➔ Multidisciplinary study program › 245 curricula leading to national degrees, organized around four major

scientific fields:

› Science and Technology › Law, Political Science, Economy, Management › Life and Health Sciences › Social and Human Sciences

› A large range of qualifications are possible including: 180 master

specialties, 115 national diplomas in health, a national diploma in oenology...

3 Mobile and Web-based Data Analysis and Public Health 4 décembre 2019

University of Bordeaux

➔Cutting-edge research

› The field of research is a driving force of the University of Bordeaux. The excellence of its competencies in all scientific fields (Science & Technology, Life & Health, Social & Human) was recognized in July 2011 when the university was awarded with the “Initiative of Excellence”. This award consists of an endowment of 700M€ to support the growth and development of research.

› The university welcomes almost 2,000 PhD students, currently has 220 joint thesis (co-tutelle) agreements running, participates in over 60 projects in the FP7/H2020, as well as 7 associated international laboratories and 5 international research groups.

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Source: www.u-bordeaux.fr

Mobile and Web-based Data Analysis and Public Health 4 décembre 2019

ISPED: Bordeaux School of Public Health

➔ Director: Pr. Mathoulin-Pelissier ➔ 2017-18 : ~1000 students

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➔ 9 Majors in Master ➔ 22 DU / DIU ➔ 18 years online teaching experience

→ 301 students at Master level → 724 students in DU / DIU

Master in « Public Health Data

Science » interdisciplinary Épidemiology + Statistics + Informatics

(Digital Public Health Graduate Program)

Master 2 Information System and IT Technologies for Health (SITIS)

Coordinator: F. Mougin - [email protected]

http://www.isped.u-bordeaux.fr/Formation/MasterenSantePublique/M2SITIS.aspx


mailto:[email protected]




Master 2 SITIS

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Information System and IT Technologies for Health

Coordinator: F. Mougin - [email protected]


➔ Health Informatics= applying IT technologies to the domain of health ➔ Nouvelle-Aquitaine : 1st French Region for Health Informatics companies (++ Gironde) ➔ Support of the Cluster TIC Santé Nouvelle-Aquitaine ➔ Since 2017 : possible to pursue jointly with a compagny ➔ 400h teaching a year sept-fev, internship mid-feb to mid-august


mailto:[email protected]




Bordeaux Population Health INSERM 1219

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Laboratoire Bordelais de Recherche en Informatique www.labri.fr/

http://bph.center/


Research Topics

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➔ Fundamental Health Informatics and Applied Research: use cases driven research

➔ Main Topic: Knowledge and Data Integration in Healthcare

› Providing methods and tools for facilitating high accurate (and massive) healthcare data integration

› Enabling their efficient secondary use • Phenotyping: tracing of patients • Epidemiology: population follow-up, evaluation, vigilance (indicators identification) • Clinical research: patients pathway identification • Data sharing and exploration (visual analytics), statistical analysis

➔ Sub-topics › Healthcare related Knowledge Modeling: Description Logics-based modeling › Information Extraction from biomedical texts and Semantic Information Retrieval › Semantic Heterogeneous Data Integration and Visualization


Some Recent Projects

› Drug Misuse identification from online forums (DoMiNO/DrugSafe, http://domino-project.fr:8888/)

› Identifying Food-Drug interactions (ANR MIAM, https://miam.limsi.fr/)

› kANNa: Knowledge graph completion using Artificial Neural Networks for Herb-Drug Interaction discovery

› ANR Ravel & Clinical DataWarehouse of the CHU Bordeaux

› Joint Handling of Knowledge Resources and their Alignments (K-Ware) www.k-ware.re, with Synapse Medicine, a Startup company, spin-off of the team


http://www.k-ware.re/

Big Data for Social Development in Low and Middle Income Countries

Post-2015 Sustainable Development Goals

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Source: http://www.scidev.net

4 décembre 2019 Mobile and Web-based Data Analysis and Public Health

Data Science for Social Development: a World Concern!

4 décembre 2019 Mobile and Web-based Data Analysis and Public Health 12

https://www.unglobalpulse.org

https://www.unglobalpulse.org/

Enabling Public Decision through Real Life Big Data Analysis


https://www.youtube.com/watch?v=SYVIMaiT0qc&feature=youtu.be

Using Mobile Phone Data


https://youtu.be/SjmsvDmVJsE

For Low and Middle Income Countries


Mobile internet adoption in Africa continues to grow rapidly;

The number of mobile internet subscribers tripled in the last five years to 300 million by the end of 2015, with an additional 250 million expected by 2020.

High Mobile Phone Penetration in Africa


Specific Case of Low and Middle Income Countries

➔ Prediction of epidemic patterns in Pakistan and the spatial extent and timing of dengue disease outbreaks › Using human mobility derived from mobile

phone data [Wesolowski et al., 2015]

➔ Identification of areas where malaria surveillance should be Increased in Namibia › Human mobility was calculated using CDR

location data and compared with malaria incidence data [Tatem et al., 2014]

➔ Monitoring exposure to air pollution in Belgium [Dewulf et al., 2016]


Wesolowski et al., 2016. Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data. J Infect Dis. 2016 Dec 1; 214(Suppl 4): S414–S420

https://www.ncbi.nlm.nih.gov/pubmed/?term=Wesolowski%20A%5BAuthor%5D&cauthor=true&cauthor_uid=28830104

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5144902/

Ebola outbreak in Guinea: a study from International Telecommunication Union (IUT)


Ebola Epidemic in Guinea: population mobility and disesase spreading


1 2

3 4

When data come from mobile usage…

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‘Data for Development Senegal’ is an innovation challenge open on ICT Big Data for the purposes of societal development.

Source: http://www.orange.com Mobile and Web-based Data Analysis and Public Health 4 décembre 2019

Orange Sonatel D4D Challenge

➔ Launched in 2012 by the Orange compagny

➔ Partnership with University of Louvain (Belgium) and MIT (USA)

➔ The first edition, D4D-Cote d’Ivoire (2012/2013), made available five months of mobile phone metadata while D4D-Senegal (2014/2015) provided one year

➔ ~360 applications from around the world were submitted for the two editions

➔ 80 in 2013 Vs. 60 in 2015 research papers had been produced

➔ Various topics covered › optimizing bus routes, analyzing social divisions, studying disease

containment policies, etc.


D4D: studies from Ivory Coast data (2013)

➔ 1: Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-Wide Epidemics [University of Birmingham]

➔ 2:Tous à bord (AllAboard) : a system for exploring

urban mobility and optimizing public transport using cellphone data [IBM, Dublin]

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➔ 3: Analysis of social categories by using cell phones data [University of California/San Diego]

➔ 4: Study and analysis of massive mobile data

[Eindhoven University of Technology/SynerScope BV/MIT]


Example of needs for the D4D 2014 edition

➔ identify the demand for mobility by region and by period over the year,

➔ measure the need for temporary infrastructure due to ad-hoc events (football match, pilgrimage)

➔ optimize tourist infrastructure

➔ model towns, and display the main streams for optimization

➔ measure the "before-after" impact of the introduction of facilities. In particular regarding the following events

› opening of the Dakar motorway in 2013 › refurbishment of the railway network

➔ conditions of access to treatment (average distance from treatment centres, location of vaccination stores, location of doctors, etc.)

➔ outbreak simulation: how would Ebola spread in Senegal? Of particular interest at the moment as Senegal has just closed its border with Guinea due to the Ebola outbreak 23


Practical Application Prize of D4D 2015

Large CDR Data as Public Health Decision Enabler: A Case Study of Cardiac and Neurological Emergencies

Context of the study

➔ Epidemiology of Stroke and Myocardial Infarction in Senegal › Incidence rate of stroke:

100 per 100000 inhabitants (~13.500 cases/year) the incidence of stroke in Europe is between 63 and 159 for 100000 women, and between 101 and 239 for 100000 men

› Incidence rate of Myocardial Infarction: 150 per 100000 inhabitants (~24.300 cases)

180 per 100000 inhabitants in France

Number of cases directly proportional to the density of population

How many patients cannot reach the hospital in time ?


➔ Certain diseases require very early medical intervention ➔ Absence of care => patient death or serious sequelae ➔ 2 use cases : Stroke and Myocardial infarction

Context of the study (cont.)

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thrombolysis

180 min


Context of the study (cont.)

➔Myocardial infarction

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percutaneous coronary intervention

90 min


Senegal: demographic and administrative division

Region Number on the map

Name of the region

Number of males

Number of females

Global Population

Area (km²) Density (/km²)

1 Dakar 1 579 020 1 558 176 3 137 196 547 5735.3

2 Thies 896 572 892 292 1 788 864 6670 268.2

3 Diourbel 716 460 780 995 1 497 455 4824 310.4

4 Kaolack 474 404 486 471 960 875 5357 179.4

5 Saint-Louis 453 315 455 627 908 942 19241 47.2

6 Louga 433 715 440 478 874 193 24889 35.1

7 Fatick 353 716 360 676 714 392 6849 104.3

8 Tambacounda 344 475 336 835 681 310 42364 16.1

9 Kolda 335 018 327 437 662 455 13771 48.1

10 Kaffrine 282 093 284 899 566 992 11262 50.3

11 Matam 276 481 286 058 562 539 29445 19.1

12 Ziguinchor 281 813 267 338 549 151 7352 74.7

13 Sedhiou 229 468 223 526 452 994 7341 61.7

14 Kedougou 78 867 72 490 151 357 16800 9.0

Total 6 735 417 6 773 298 13 508 715 196 712 68.7

Source: General Population Census (RGPHAE) 2013 4 décembre 2019 Mobile Data in Senegal: a public health decision enabler

Used Dataset


General Census

Data

Dataset 1 One year of site-to-site traffic for 1666 sites on an

hourly basis

Dataset 2 Fine-grained mobility data (antenna) on a

rolling 2-week basis for about 300,000 randomly

sampled users

Dataset 3 One year of coarse-

grained (123 arrondissement level) mobility data for about

150,000 randomly sampled users

Senegal Geolocalised Hospital Directory

Incidence Rate of the Ermergency Diseases

Anonymised data from 9 million mobile customers in Senegal

Contextual Data

OpenStreetMap

user_id, timestamp, site_id 1, 2013-03-18 21:30:00, 716 1, 2013-03-18 21:40:00, 718 1, 2013-03-19 20:40:00, 716 1, 2013-03-19 20:40:00, 716 1, 2013-03-19 20:40:00, 716 1, 2013-03-19 20:40:00, 716 1, 2013-03-19 21:00:00, 716 1, 2013-03-19 21:30:00, 718 1, 2013-03-20 09:10:00, 705 1, 2013-03-21 13:00:00, 705

Overall Approach


E. Mutafungwa, F. Thiessard, MP. Diallo, R. Gore, V. Jouhet, F. Mougin, S. Ben Yahia, C. Karray, N. Kheder, R. Saddem, J. Hämäläinend, G. Diallo. Mobile Data as Public Health Decision Enabler: A Case Study of Cardiac and Neurological Emergencies. Proceedings of the Orange D4D Challenge, in conjunction with the 4th International conference on the scientific analysis of mobile phone datasets (NetMob'2015), 7-10 April 2015, MIT Media Lab, Cambridge, MA, USA.

➔ Distance Evaluation

› Different geographical locations are then app segmented according to antenna coverage areas

› Coverage area evaluated using Voronoi tesselations

› › Antenna site location assumed

to be default location for whole the coverage area

Voronoi cell layout for Senegal based on provided 1666 site locations

Coverage of Antenna Sites


Travel Times Computing: use of detour index

➔Estimating travel times to reach the nearest hospital

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Travel Times to nearest Hospital

Green: less than 90 minutes, Yellow: 90 to 180 minutes, Red: over 180 minutes

Location of the 40 hospitals considered in the study

Dakar


Mobile Population Density Estimation

➔Computing the population density + incidence rates at antenna (site location) level

Mobile and Web-based Data Analysis and Public Health 33

Distribution of Senegal Population according to the mobile antenna

grey : N < 100 yellow : 101<N<1000 Red: 1001<N<10000 Brown: 10001<N<100000 black : N > 100000

α = 𝑈𝑈𝑆𝑆 ∗ 1.2 ∗ 1 𝑂𝑂𝑂𝑂𝑂𝑂⁄ β =𝑈𝑈𝑅𝑅

𝑂𝑂𝑅𝑅

Correction Factors

4 décembre 2019

Risk Zones Identification

➔Highlighting white zones: areas where people are at high risk in case of Stroke or Myocardial Infarction

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Estimated incident cases of Myocardial Infarction from different areas (4241)

Grey < 5 victims Yellow 5 to 25 Orange 26 to 50 Red 51 to 100

Estimated incident cases of strokes from different areas (432)

Grey < 5 victims, Yellow 5 to 25


Limitations of the study

➔ Bias related to the extrapolation of population at a given antenna coverage area

› As if any single individual had a cell phone › As if Orange was the only mobile provider in Senegal

➔ A filtering on data is performed by Orange- Sonatel ➔ The study is based on an estimated incidence rate of the considered

medical emergency as there is no recent official figures for Senegal, but probably very close.


Overall CDR Data Limitations and Difficulties

➔ Privacy Concerns › When mobile phones transmit voice they also collect

and record other information, not only inherent to CDRs but also user specific and personal information, and this creates new challenges in terms of the conflict between technology innovation and rights to privacy

➔ Accuracy

› Mobile device geolocation or estimated position are rarely very precise (from 50m to 300m in dense area)

➔ Availability of CDR data

➔ Data discontinuity

➔ Shared market of the Mobile Operator


Semantic Web as Patient Safety Enabler in Herbal Based African Traditional

Medicine and Drug Monitoring

Context

➔ Traditional medicine (TM) is the oldest form of healthcare delivery

➔ The World Health Organization (WHO) estimates that as much as 80% of the population uses it in Africa

➔ In Africa › Many reasons: inaccessibility, unaffordability or

unavailability of conventional health care services and medicines

› Most people are underserved and mostly illiterate rural people

› the distribution of products is limited by the low density of pharmaceutical personnel and/or medical infrastructures


Context (cont.)

➔ The 2014-2023 strategic plan of the WHO seeks to promote the safe and effective use of TM by regulating and integrating TM products, practitioners and practice into the formal health systems

➔ West African Health Organisation (WAHO) has given priority to traditional medicine since 2007 › important action item is the

standardisation of descriptions of herbal and traditional medicines

› Launch of the West African pharmacopeia in 2013


WATRIMED: West African Herbal-based Traditional Medicine Knowledge Graph

➔ Objective

› Bringing West African TM to the digital world thanks to the use of state-of-the art, flexible and shareable knowledge representation approach

› Contributing to preserve secular knowledge and help for more safe TM usage

› Helping to establish bridges with conventional medicine where necessary

› Offering a platform which can contribute enabling phytovigilance activity


WATRIMed: Overall Approach

41

1

2

3

4

• BMJ. Some, G. Bordea, F. Thiessard, S. Schulz, G. Diallo: Design Considerations for a Knowledge Graph: the WATRIMed use case. Studies in Health Technology and Informatics, vol. 259:59 - 64. IOS Press . DOI:10.3233/978-1-61499-961-4-59. • BMJ. Some, G. Bordea, F. Thiessard, G. Diallo: Enabling West African Herbal Traditional Medicine Digitalization: the WATRIMed Knowledge Graph. Studies in Health Technology and Informatics, vol. 259:59 - 64. IOS Press.


Examples of the WAHO materials

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Extract of WAHO Pharmacopeia Extract of WAHO Inventory of Proven Effective Medicinal Plant Formulations

Vernacular names

Synonym terms

Main part used

Formulation

Side effetcs

Dosage

Indications


Result: the Knowledge Graph into Protege

4 décembre 2019 G. DIALLO, BI & SemWeb @ CRI'19 43

Current work: Herbs-Drugs Iteractions Identification using the Watrimed Knowledge Graph

➔ HDI Interaction Mechanism

› St John’s wort induction of the cytochrome P450 enzymes


Courtesy: Georgeta Bordea

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DoMINO (Drug Misuses In NetwOrks)

➔ Modification of usages throughout the time

RCP Forums Social

networks Différence ?

2015 Forums Social

Networks

2016 Forums Social

Networks

2017 Forums Social

Networks

2018 Forums Social

Networks

S. Cossin, L. Lebrun, G. Lobre, R. Loustau, V. Jouhet, R. Griffier, F. Mougin, G. Diallo, F. Thiessard. Romedi: an open data source about French drugs on the semantic web. MEDINFO'2019, Studies in health technology and informatics,

Courtesy: Frantz Thiessard


DoMINO: Overview

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RCP Forums Social

Networks Difference ?

Mésusage médicaments

Knowledge Representation/integration

NLP : Aligning patient vocabulary with

concepts

Visualisation of Post (co-occurrences)

Extracting Forums Posts

Mesusage Identification

Visual analytics



Workflow Overview

4 décembre 2019 G. DIALLO, BI & SemWeb @ CRI'19 47


DoMINO: Results

48

Demo

https://www.domino-project.fr:8888

4 décembre 2019 G. DIALLO, BI & SemWeb @ CRI'19

https://www.domino-project.fr:8888/

As African students/junior researchers, there is an avenue for you to do exciting things

Design Your projects from Real World Use Cases, Real World Data to impact more

People Life and contribute to the Progress

Take Home Message

Leave your Own Life Do your the Research which

Inspires You and People Do YOUR Research

Thanks to my Colleagues from Team ERIAS

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VJ FM

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4 décembre 2019 G. DIALLO, BI & SemWeb @ CRI'19

Gayo Diallo ERIAS ISPED INSERM U1219 & LaBRI

[email protected]

Big Data and Semantic-based Digital Public Health: focus on Low and Middle Income Countries

ERIAS Web Site https://bit.ly/2zwrrcu

BI & SemWeb @ CRI'19 - Yaoundé, 02/12/2019



Documents

Big Data and Semantic-based Digital Public Health: focus ...linkedvocabs.org/bisw19/assets/semweb-gayo.pdf · Various topics covered › optimizing bus routes, analyzing social divisions,