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BigData in Urology | The urology of the futur

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What is the history of health data?
What is the BigData?
We talk about artificial intelligence, machine learning and deep learning: what's the difference?
What applications today in medicine.
What will be the urology of tomorrow?
BIGDATA is a vast subject, inseparable from BIGDATA ANALYTICS. I will share my experience in the field of data analysis and BigData!

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BigData in Urology | The urology of the futur

  1. 1. Cette photo par Auteur inconnu est soumise à la licence CC BY-SA BIGDATA (BIGDATA ANALYTICS) IN UROLOGY @HUPERTAN Abbaye Royale de Fontevraud Samedi 7 décembre 2019 Rencontres d’Urologie de Fontevraud #Fontevraud2019.
  2. 2. What is the history of health data? What is the BigData? We talk about artificial intelligence, machine learning and deep learning: what's the difference? What applications today in medicine. What will be the urology of tomorrow? BIGDATA is a vast subject, inseparable from BIGDATA ANALYTICS. I will share my experience in the field of data analysis and BigData! The presentation was done remotely using ZOOM.
  3. 3. CONFLICT OF INTEREST STATEMENT Cette photo par Auteur inconnu est soumise à la licence CC BY-SA BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan
  4. 4. @hupertan by Hupertan,MD • 2019 – urologist – sexologist previously • 1993 – Assistant «IT & Biostatistics» - UMF Cluj • 1999 – 2007 SPSS consultant – «Data Mining» • 2001 – DEA– «Data Mining»  PhD • 2003 – Hupertan StatisticsBIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan
  5. 5. IBM PC AT 286 Cette photo par Auteur inconnu est soumise à la licence CC BY 1990 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan
  6. 6. Health Data levels 1. «Lab» level 2. Warehouse (entreprise, hospital) 3. eHealth - BigdataBIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  7. 7. Cette photo par Auteur inconnu est soumise à la licence CC BY-SA-NC Cette photo par Auteur inconnu est soumise à la licence CC BY-SA-NC BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  8. 8. Cette photo par Auteur inconnu est soumise à la licence CC BY-NC Univariate analysis P value= 0,05 𝒑𝒂𝒕𝒊𝒆𝒏𝒕 ∈ 𝒔𝒂𝒎𝒑𝒍𝒆 (disease Y/N) BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  9. 9. Concentration of serum prostate specific antigen in men who developed clinical prostate cancer according to interval between blood collection and date of diagnosis (observation time). Carol Parkes et al. BMJ 1995;311:1340-1343 ©1995 by British Medical Journal Publishing Group Fig 1Concentration of serum prostate specific antigen in men who developed clinical prostate cancer according to interval between blood collection and date of diagnosis (observation time). Median of the cases is shown (−---). Concentrations are expressed in multiples of median for controls of same age from same centre. Nine cases and four controls from Washington County study 12 and one case from the Social Insurance Institution study 13 were previously published. Results are shown separately for men who died of prostate cancer (solid dots) and for those who presented clinically with prostate cancer and were still alive or had died from other causes at end of follow up (open dots). Numbers to the right of the vertical axis are BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  10. 10. «It is much more important to know what sort of a patient has a disease than what sort a disease a patient has» WilliamOsler Sir William Osler BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  11. 11. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  12. 12. Health Data levels 1. Lab level 2. Warehouse (hospital) 3. eHealth - BigdataBIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  13. 13. Cette photo par Auteur inconnu est soumise à la licence CC BY-NC Cette photo par Auteur inconnu est soumise à la licence CC BY- SA-NC Multivariate analysis predictive models 𝒑𝒂𝒕𝒊𝒆𝒏𝒕 ∈ 𝒓𝒊𝒔𝒌 𝒈𝒓𝒐𝒖𝒑 Machine learning Cette photo par Auteur inconnu est soumise à la licence CC BY-SA-NC BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  14. 14. ALBERTSEN PC, HANLEY JA, GLEASON DF, BARRY MJ. Competing risk analysis of men aged 55 to 74 years at diagnosis managed conservatively for clinically localized prostate cancer. JAMA. 1998 ; 280:975-80 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  15. 15. D'AMICO AV, WHITTINGTON R, MALKOWICZ SB, SHULTZ D, BLANK K, BRODERICK GA, TOMASZEWSKI JE, RENSHAW AA, KAPLAN I, BEARD CJ, WEIN Biocheminical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinicaly localized prostate cancer, JAMA 1998 ;280 :969-74 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  16. 16. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  17. 17. hupertan.stat@me.com Predictive nomogram ★ A device that suppose two elements: 1. equation of an event probability 2. specific functional representation in a graphic form BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  18. 18. V. Hupertan 1,2, M. Rouprêt2, J.-F. Poisson2, Y. Chrétien2, B. Dufour2, N. Thiounn2, A. Méjean2 From the E.R.I.C "Equipe de Recherche en Ingéniérie des Connaissances", University Lyon 2, France Department of Urology,a Necker Hospital, GHU Ouest, University René Descartes, Paris V . INTRODUCTION DISCUSSION A postoperative nomogram proposed by Kattan et al, based on the analysis of a Memorial Sloan-Kettering database. The postoperative nomogram developed by Kattan et al. was used to calculate the probability that a patient would be free from recurrence at 5 years of follow-up. The four variables included in the nomogram were clinical symptoms, histology, tumor size, and 1997 TNM stage. The generalization of the Kattan nomogram to external cohorts of patients with characteristics different from the original dataset has therefore yet to be validated. The aim of this study was to establish the accuracy of the Kattan nomogram in predicting RCC recurrence in a representative patient population who underwent surgery in a large single center. • some variables included in the nomogram were not significant prognostic factors for recurrence(overtraining of the model?) • selection bias : patient recruitment and management. MATERIALAND METHODS LOW PREDICTIVE ACCURACY OF KATTAN'S POSTOPERATIVE NOMOGRAM FOR RENAL CELL CARCINOMA RECURRENCE IN A POPULATION OF FRENCH PATIENTS Abstract Nr: 550 Nomogram and statistics. The predictive accuracy of the nomogram was measured by the area under the receiver operating characteristic (ROC) under the receiver operating characteristic (ROC) curve as given by Harrell et al's concordance index (c-index) for censored data and CI by 1000*bootstrap. Survival analysis. Survival analysis was provides by the Kaplan- Meier method, the log-rank test (a p-value of <0.05 was considered significant) and Cox model. The primary endpoint was recurrence-free survival defined as either the time from surgery to detection of the first local recurrence or of distant metastases, or the time from surgery to the close of the study. Kattan's nomogram accuracy: the c-index = 0.607 ,95% CI (0.576: 0.635) CONCLUSIONS • New dynamic models ? • More patients? • Worldwide? • Specific models? Characteristics Current study N (%) Kattan et al. N (%) Sex Men 402 (71.1) 363 (60.4) Women 163 (28.9) 238 (39.6) Median age at diagnosis, y 62 63.5 Symptoms Incidental 307 (54.3) 384 (63.9) Local 220 (39) 204 (33.9) Systemic 38 (6.7) 13 (2.2) Nephrectomy type Radical 477 (84.4) NA Tumorectomy 60 (10.6) NA Nephron-sparing 28 (5.0) NA Histology Clear cell 470 (83.2) 430 (71.5) Papillary 74 (13.1) 108 (18) Chromophobe 21 (3.7) 63 (10.5) Tumor size, cm Minimum 0.6 0.5 Median 5.1 4.5 Maximum 22 20 Stage, TNM 1997 1 363 (64.2) NA 2 118 (20.9) NA 3a 40 (7.1) NA 3b/c 44 (7.8) 50 (8.3) Follow-up status Alive 484 (85.7) 559 (93) Dead, all causes 81 (14.3) 42 (7) Dead, disease-related 78 (13.8) NA Patient Characteristics From the Current Study (N:565) and for the Patients Included in the Reference Study (N:601) RESULTS Survival analysis. The 5-year recurrence-free and disease- free survival rates were 81.7% and 84.7%, respectively BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  19. 19. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  20. 20. hupertan.stat@me.com «we tend to be inconsistent when processing our mental databse» 2 1 21 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  21. 21. http://penseeartificielle.fr/difference-intelligence-artificielle-machine-learning-deep-learning/
  22. 22. The construction of nomograms is the typical example of machine learning - from the structured data, a model («black box» «white box») is used to calculate an output value Cette photo par Auteur inconnu est soumise à la licence CC BY-NC-ND BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  23. 23. https://www.slideshare.net/SebastianRaschka/nextgen-talk-022015/8 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan
  24. 24. Apprentissage non supervisée Nuées dynamiques - Kmeans K=5 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Centre final classe 2 Variable 1 Variable2 Stage de recherche DEA Extraction des connaissances à partir des données IRIS Division Biométrie Université Lyon 2, Laboratoire E.R.I.C. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  25. 25. Couche d’entrées Carte topologique Apprentissage non supervisée Réseaux de neurones de Kohonen (SOM) Structures topologiques MAPA Stage de recherche DEA Extraction des connaissances à partir des données IRIS Division Biométrie Université Lyon 2, Laboratoire E.R.I.C. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  26. 26. Variabilité de la TAs et TAd Typologie Kmeans à 12 classes 60 80 100 120 140 160 180 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 Heure TAd-KM12cl:type-1(40) TAd-KM12cl:type-10(77) TAd-KM12cl:type-11(32) TAd-KM12cl:type-12(70) TAd-KM12cl:type-3(52) TAd-KM12cl:type-6(51) TAd-KM12cl:type-8(37) TAd-KM12cl:type-9(50) TAd-TOTAL TAs-KM12cl:type-1(40) TAs-KM12cl:type-10(77) TAs-KM12cl:type-11(32) TAs-KM12cl:type-12(70) TAs-KM12cl:type-3(52) TAs-KM12cl:type-6(51) TAs-KM12cl:type-8(37) TAs-KM12cl:type-9(50) TAs-TOTAL BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 Stage de recherche DEA Extraction des connaissances à partir des données IRIS Division Biométrie Université Lyon 2, Laboratoire E.R.I.C.
  27. 27. Predictive Care Analysis Solution in Healthcare Solution using IBM SPSS MODELER By Vincent Hupertan Update 2015 Vincent Hupertan© 2007 SPSS Inc. SPSS 2007 : carte d identité § Éditeur de logiciels international fondé en 1968, coté au NASDAQ depuis 1993 § Leader dans l’édition de logiciels de Datamining et de CRM analytique § Leader incontesté des logiciels d’enquêtes et d’analyse comportementale § Pionnier de l’Analyse Prédictive § Chiffre d’affaires mondial en 2006 : 261 M$ § Plus de 1300 personnes dans le monde § 41 collaborateurs en France (ventes, marketing, services, R&D) § 4 millions d’utilisateurs et 250 000 clients dans le monde, 4 500 clients actifs en France Décisionnel Data mining Text mining Statistiques Enquêtes Applications Analyse prédictive Pilotez et améliorez l’activité de votre structure hospitalière grâce à l’analyse prédictive : - Grâce à des enquêtes, validez la Certification V2, l’Evaluation des Pratiques Professionnelles (EPP) et la Gestion des Risques (GDR) - Grâce à un portail unique disposant d’un accès par métier à disposition des acteurs de l’hôpital, diffusez l’information nécessaire à la bonne gouvernance, au pilotage et à la contractualisation interne - Grâce aux modèles prédictifs, anticipez l’EPRD (état prévisionnel des recettes et des dépenses) en intégrant la T2A, anticipez les risques médicaux (infections nosocomiales)
  28. 28. Health Data levels 1. Lab level 2. Warehouse (entreprise, hospital) 3. eHealth  Bigdata “datafication” of everything BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  29. 29. Cette photo par Auteur inconnu est soumise à la licence CC BY VOLUME VARIET Y VELOCI TY EHRs, PACS, medical devices, patient-reported outcomes, financial transaction, social media posts, oline patients foru BIGDATA (BIGDATA ANALYTICS) in UROLOGY © @hup
  30. 30. Cette photo par Auteur inconnu est soumise à la licence CC BY-SA-NC Big Data and the analytics bot BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  31. 31. http://penseeartificielle.fr/difference-intelligence-artificielle-machine-learning-deep-learning/
  32. 32. https://blog.dataiku.com/ai-vs.-machine-learning-vs.-deep-learning BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  33. 33. https://www.lemagit.fr/conseil/Machine-Learning-vs-Deep-Learning-un-avion-a-helices-et-un-avion-a-reaction BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 @hupertan
  34. 34. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  35. 35. https://ai.googleblog.com/2017/03/assisting-pathologists-in-detecting.html Assisting Pathologists in Detecting Cancer with Deep Learning BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  36. 36. Nagpal, K., Foote, D., Liu, Y. et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. npj Digit. Med. 2, 48 (2019) doi:10.1038/s41746- 019-0112-2 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  37. 37. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  38. 38. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  39. 39. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  40. 40. The urologist of the future • AI • Collaborative medicine • Telemedicine • Machine learning • Personalisation Cette photo par Auteur inconnu est soumise à la licence CC BY-NC-ND BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  41. 41. Bigdatawillallow «It is much more important to know what sort of a PATIENT has a disease than what sort a disease a patient has» WilliamOslerSir William Osler BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  42. 42. hupertan.stat@me.com «no nomogram will ever take the place of good clinical judgement and the well- informed patients.» Robert W. Ross, Philip W. Kantoff Predicting Outcomes in Prostate Cancer: How Many More Nomograms Do Se Nedd? J.CLIN.ONCOL, 25,2077:3563-3564 4 6 46 BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019 ?
  43. 43. The best thinga docto can do for a patient is sharehis humanity, packed with his knowledgeof the disease, all witha good dose of empathy. BIGDATA (BIGDATA ANALYTICS) in UROLOGY ©2019
  44. 44. I thank Thibaud for having invited me to this wonderful day of exchanges around the urology of the future.
  45. 45. #ENCYCLOPENIS @hupertan
  46. 46. Le livre est la concrétisation de mon blog UROBLOG, crée en 2014. Avec plus de 70 000 visiteurs, il m’a permis 5 ans après de publier ce livre chez Editions Leduc.s, écrit dans la même logique que le livre. Je suis toujours à la recherche des auteurs qui souhaiteraient publier des articles destinés aux patients.

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