SOCIAL NETWORK ANALYSIS TOOL

SOCIAL NETWORK ANALYSIS TOOL

WHO ARE WE? KAP-CODE is a start-up offering solutions in the fields of chronic disease and health signal monitoring via social network. CONNECTED DEVICES BIG DATA DIGITAL HEALTH 1

GENERAL CONTEXT 55 million Internet users, i.e. 86% of the French population MANY PEOPLE WRITE ABOUT THEIR EXPERIENCES ON DISCUSSION FORUMS, BLOGS, ETC. THESE PLATFORMS ARE BECOMING IMPORTANT SOURCES OF INFORMATION 1 32 million active social network users, i.e. 50% of French people 1. Golder S, Norman G, Loke YK. Systematic review on the prevalence, frequency and comparative value of adverse events data in social media. Br J Clin Pharmacol. 2015 Oct;80(4):878 88. 2

CONTEXT DETEC T Social media and online communities encourage patients to talk online about their experiences and personal history with respect to treatment 2. +1 100 @ BUT HOW CAN WE USE THIS COLLECTION OF DATA IN THE HEALTH SECTOR? 2. Sloane R, Osanlou O, Lewis D et al. Social media and pharmacovigilance: A review of the opportunities and challenges. Br J Clin Pharmacol. 2015 Oct;80(4):910-20. 3

THE DETEC T SOLUTION DETEC T IS AN INNOVATIVE SOLUTION DESIGNED TO ANALYSE SOCIAL MEDIA DATA RELATED TO HEALTH 9% of messages mention GASTRIC DISTRESS 4

THE DETEC T CONCEPT KEYWORD ANALYSIS RESTITUTION PREPROCESSING MISUSE AND PROPER USE OF TREATMENTS DISEASE OBSERVATION ADVERSE EVENTS KEYWORD SEARCH ON FORUMS AND SOCIAL MEDIA Collection of messages containing the keyword IDENTIFICATION OF ADVERSE EVENTS SIGNAL DETECTION ADHERENCE STUDIES PRESENTED IN DETAILED REPORTS COURSE OF CARE 4 Lardon J, Abdellaoui R, Bellet F et al. Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review. J Med Internet Res. 2015 Jul10;17(7):e171. doi: 10.2196/jmir.4304. Review. PubMed PMID: 26163365; PubMedCentral PMCID: PMC4526988. 5 Benton A, Ungar L, Hill S et al. Identifyin potential adverse effects using the web: a new approach to medical hypothesis generation. J Biomed Inform. 2011 Dec;44(6):989 96. 6 Feinaerer I, Hornik K, Meyer D. Text mining infrastructure in R. J Stat Softw. 2008 Mar;25(5):1 54. 5

A FEW FIGURES 26,862,232 MESSAGES ANALYSED 26 FRENCH LANGUAGE SOURCES ANALYSED 3 12 YEARS OF DISCUSSIONS ANALYSED 485 MEDICINES ANALYSED 3. Katsahian S, Simond Moreau E, Leprovost D et al. Evaluation of Internet Social Networks using Net scoring Tool: A Case Study in Adverse Drug Reaction Mining. Stud Health Technol Inform. 2015;210:526-30. PubMed PMID: 25991203 6

APPLICABLE FIELDS THERAPEUTIC CLASS PERMANENT OBSERVATORIES AND CROSS-DISCIPLINARY STUDIES IDENTIFICATION OF CHALLENGES FACED BY PATIENTS, ETC. PROPER MEDICINE USE IDENTIFICATION OF MEDICAL CONCEPTS MENTIONED ONLINE AND CODING USING MEDDRA TERMINOLOGY DETECTION ALGORITHM FOR ASSESSING PROPER USE AND COMPLIANCE WITH THE MARKETING AUTHORIZATION ADHERENCE (PRIMARY AND SECONDARY) IDENTIFICATION OF THE REASONS GIVEN BY INTERNET USERS FOR STOPPING THEIR TREATMENTS DETECTION ALGORITHM FOR FACTORS THAT PREDICT THE CESSATION OR MODIFICATION OF TREATMENT PHARMACOVIGILANCE SIGNAL ANALYSIS ADVERSE EFFECT DETECTION ALGORITHM USE OF STANDARD SIGNAL DETECTION METHODS 7

OUR SERVICES MISUSE AND PROPER USE OF TREATMENTS ADVERSE EVENTS DISEASE OBSERVATION ADHERENCE STUDIES COURSE OF CARE 8

9 USE CASES

USE AND OFF-LABEL USE DETECTION OF OFF-LABEL USE Medical topics mentioned excluding topics related to the MA NUMBER OF MEDICAL TOPICS BY SOC GROUP MENTIONED OVER TIME FOR A GIVEN MEDICINE 10

USE AND OFF-LABEL USE DETECTION OF OFF-LABEL USE EXAMPLE OF A DRUG DEPENDENCE SIGNAL DISCOVERED DURING THE SURVEILLANCE OF A CENTRAL NERVOUS SYSTEM MEDICINE Visualisation of topics mentioned LONGITUDINAL MONITORING OF CNS MEDICINE USE 11

SIGNAL DETECTION 7-10 30% OF SIGNALS ARE IDENTIFIED ON AVERAGE 75 DAYS BEFORE THEY ARE INCLUDED IN VIGIBASE AND FAERS Diarrhoea Psoriasis Infection CHI-SQUARED AND PRR DISTRIBUTION FOR MEDICINE X 7. Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data mining of the public version of the FDA Adverse Event Reporting System. Int J Med Sci. 2013 Apr 25;10(7):796-803. doi: 10.7150/ijms.6048. Print 2013. Review. PubMed PMID: 23794943; PubMed CentralPMCID: PMC3689877. 8. Yang CC, Yang H, Jiang L, Zhang M. Social Media Mining for Drug Safety Signal Detection. Proc 2012 Int Workshop Smart Health Wellbeing. 2012;33 40. 9. Benton A, Ungar L, Hill S, Hennessy S, Mao J, Chung A, et al. Identifying potential adverse effects using the web: a new approach to hypothesis generation. J Biomed Inform. 2011 Dec;44(6):989 96. 10. Ahmed I, Haramburu F, Fourrier-Réglat A, et al. Pharmacovigilance signal detection methods revisited in a multiple comparison setting. Stat Med. 2009 Jun 15;28(13):1774-92. doi: 10.1002/sim.3586. PubMed PMID: 19360795 12

QUALITATIVE ADHERENCE ANALYSIS % - PROPORTION OF MESSAGES REASONS FOR STOPPING A GIVEN CNS MEDICINE 11,4% 18,0% 12,8% WEIGHT THE TIME IT TAKES TO ACT PROBLEMS, DEPENDENCE 19,0% 12,8% 14,7% 11,3% DIFFICULTIES, CESSATION OTHER TREATMENTS DOSAGE TREATMENT INTOLERANCE 13

QUALITATIVE ADHERENCE ANALYSIS % - PROPORTION OF MESSAGES PATIENT RELUCTANCE TO ADHERE TO TREATMENT 20,2% CESSATION, SWITCH 17,7% WAVERING, FEAR OF DIGESTIVE PROBLEMS, WEIGHT GAIN, LIBIDO 23,6% 19,4% 19% EXPERIENCE,WITHDRAWAL SLEEP, DOSAGE SUFFERING, PSYCHOTIC DISTURBANCES 14

OUR OFFERS ENJOY COMPREHENSIVE SURVEILLANCE WITH DETEC T - ASSESS HOW YOUR MEDICINES ARE PERCEIVED BY PATIENTS - - MEASURE PATIENT INFLUENCE ON SELECTING AND MODIFYING TREATMENT - - IDENTIFY THE CRITERIA FOR CONTINUING OR STOPPING A TREATMENT - - ANALYSE YOUR IMPACT ON PATIENTS QUALITY OF LIFE - CONSTANT SURVEILLANCE AD HOC STUDY Each month, receive a summary of what Internet users have been saying about your medicines on social networks and discussion forums. With this offer, receive a detailed report examining a set of indicators previously selected with our teams. 15

AND MUCH MORE WE OFFER TAILORED SOLUTIONS TO MEET YOUR NEEDS YOUR NEEDS ANALYSIS YOUR CASE EXTRACTION RESTITUTION 16

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