Infographics : le Design au Service de la VulgarisationSimplexity Agency
Le Design s'intègre aujourd'hui dans tous les domaines de la vie. Cette petite présentation réalisée dans la cadre des événements "Réveil en Forme" du réseau wallon Easynove propose d'aborder l'utilité du design graphique dans le cadre de la vulgarisation. Sensibiliser, informer, motiver, interpeler, séduire... comment les "infographics" peuvent-ils vous aider à mieux atteindre vos objectifs de communication?
Envie d'organiser cette présentation dans votre structure? Contactez-moi!
Online- und Mobile Banking - Zugangswege zur BankBankenverband
Viele Wege führen zur Bank, immer häufiger ist es aber ein digitaler! So sind Online- und Mobile Banking inzwischen die am meisten genutzten Zugangswege, während die Kundinnen und Kunden immer seltener die Bankfiliale aufsuchen. Alle Ergebnisse und Trends aktuell in unserer repräsentativen Umfrage.
Infographics : le Design au Service de la VulgarisationSimplexity Agency
Le Design s'intègre aujourd'hui dans tous les domaines de la vie. Cette petite présentation réalisée dans la cadre des événements "Réveil en Forme" du réseau wallon Easynove propose d'aborder l'utilité du design graphique dans le cadre de la vulgarisation. Sensibiliser, informer, motiver, interpeler, séduire... comment les "infographics" peuvent-ils vous aider à mieux atteindre vos objectifs de communication?
Envie d'organiser cette présentation dans votre structure? Contactez-moi!
Online- und Mobile Banking - Zugangswege zur BankBankenverband
Viele Wege führen zur Bank, immer häufiger ist es aber ein digitaler! So sind Online- und Mobile Banking inzwischen die am meisten genutzten Zugangswege, während die Kundinnen und Kunden immer seltener die Bankfiliale aufsuchen. Alle Ergebnisse und Trends aktuell in unserer repräsentativen Umfrage.
2022-09-08 ECPM Digital Biomarkers and AI, Basel, Alain van Gool.pdfAlain van Gool
Lecture for 150 pharma professionals to outline the potentials and things-to-do with digital biomarkers, as part of a ECPM training on digitization and AI in drug development.
CORSO DI INFORMATICA AZIENDALE
I processi aziendali: classificazione dei dati e dei processi aziendali.
L'informatica per il marketing: business intelligence, data warehouse, data mining
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
Machine Learning meets Granular Computing: the emergence of granular models in the Big Data era
** Presentation Slides from Dr Rafael Falcon, from Larus Technologies, for the February 2018 Ottawa Machine Learning & Artificial Intelligence Meetup
Abstract
Traditional Machine Learning (ML) models are unable to effectively cope with the challenges posed by the many V’s (volume, velocity, variety, etc.) characterizing the Big Data phenomenon. This has triggered the need to revisit the underlying principles and assumptions ML stands upon. Dimensionality reduction, feature/instance selection, increased computational power and parallel/distributed algorithm implementations are well-known approaches to deal with these large volumes of data.
In this talk we will introduce Granular Computing (GrC), a vibrant research discipline devoted to the design of high-level information granules and their inference frameworks. By adopting more symbolic constructs such as sets, intervals or similarity classes to describe numerical data, GrC has paved the way for a more human-centric manner of interacting with and reasoning about the real world. We will go over several granular models that address common ML tasks such as classification/clustering and will outline a methodology to appropriately design information granules for the problem at hand. Though not a mainstream concept yet, GrC is a promising direction for ML systems to harness Big Data.
Comparison of rule-based/ontology systems and machine learning models for the extraction insights from electronic health records and related charts. Inference and prediction.
Generative AI represents a pivotal moment in computing history, opening up new opportunities for scientific discoveries. By harnessing extensive and diverse datasets, we can construct new general-purpose Foundation Models that can be fine-tuned for specific prediction and exploration tasks. This talk introduces our research program, which focuses on leveraging the power of Generative AI for materials discovery. Generative AI facilitates rapid exploration of vast materials design spaces, enabling the identification of new compounds and combinations. However, this field also presents significant challenges, such as effectively representing crystals in a compact manner and striking the right balance between utilizing known structural regions and venturing into unexplored territories. Our research delves into the development of a new kind of generative models specifically designed to search for diverse molecular/crystal regions that yield high returns, as defined by domain experts. In addition, our toolset includes Large Language Models that have been fine-tuned using materials literature and scientific knowledge. These models possess the ability to comprehend extensive volumes of materials literature, encompassing molecular string representations, mathematical equations in LaTeX, and codebases. We explore the open challenges, including effectively representing deep domain knowledge and implementing efficient querying techniques to address materials discovery problems.
2022-09-08 ECPM Digital Biomarkers and AI, Basel, Alain van Gool.pdfAlain van Gool
Lecture for 150 pharma professionals to outline the potentials and things-to-do with digital biomarkers, as part of a ECPM training on digitization and AI in drug development.
CORSO DI INFORMATICA AZIENDALE
I processi aziendali: classificazione dei dati e dei processi aziendali.
L'informatica per il marketing: business intelligence, data warehouse, data mining
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
Machine Learning meets Granular Computing: the emergence of granular models in the Big Data era
** Presentation Slides from Dr Rafael Falcon, from Larus Technologies, for the February 2018 Ottawa Machine Learning & Artificial Intelligence Meetup
Abstract
Traditional Machine Learning (ML) models are unable to effectively cope with the challenges posed by the many V’s (volume, velocity, variety, etc.) characterizing the Big Data phenomenon. This has triggered the need to revisit the underlying principles and assumptions ML stands upon. Dimensionality reduction, feature/instance selection, increased computational power and parallel/distributed algorithm implementations are well-known approaches to deal with these large volumes of data.
In this talk we will introduce Granular Computing (GrC), a vibrant research discipline devoted to the design of high-level information granules and their inference frameworks. By adopting more symbolic constructs such as sets, intervals or similarity classes to describe numerical data, GrC has paved the way for a more human-centric manner of interacting with and reasoning about the real world. We will go over several granular models that address common ML tasks such as classification/clustering and will outline a methodology to appropriately design information granules for the problem at hand. Though not a mainstream concept yet, GrC is a promising direction for ML systems to harness Big Data.
Comparison of rule-based/ontology systems and machine learning models for the extraction insights from electronic health records and related charts. Inference and prediction.
Generative AI represents a pivotal moment in computing history, opening up new opportunities for scientific discoveries. By harnessing extensive and diverse datasets, we can construct new general-purpose Foundation Models that can be fine-tuned for specific prediction and exploration tasks. This talk introduces our research program, which focuses on leveraging the power of Generative AI for materials discovery. Generative AI facilitates rapid exploration of vast materials design spaces, enabling the identification of new compounds and combinations. However, this field also presents significant challenges, such as effectively representing crystals in a compact manner and striking the right balance between utilizing known structural regions and venturing into unexplored territories. Our research delves into the development of a new kind of generative models specifically designed to search for diverse molecular/crystal regions that yield high returns, as defined by domain experts. In addition, our toolset includes Large Language Models that have been fine-tuned using materials literature and scientific knowledge. These models possess the ability to comprehend extensive volumes of materials literature, encompassing molecular string representations, mathematical equations in LaTeX, and codebases. We explore the open challenges, including effectively representing deep domain knowledge and implementing efficient querying techniques to address materials discovery problems.
La farmacoterapia dei tumori dell'apparato digerente ASMaD
Presentazione a cura del Dottor Mauro Minelli - "HOT TOPICS IN GASTROENTEROLOGIA - I TUMORI DELL'APPARATO DIGERENTE: cosa è cambiato e cosa bisogna sapere" - Roma 10/11/2018
La diagnosi clinica di appendicite acuta è ancora difficile nonostante la frequenza di questa patologia. La raccolta accurata dei sintomi riferiti dal paziente, una visita approfondita alla ricerca dei segni addominali di sospettata appendicite e l’uso del supporto diagnostico dell’ecografia addominale e, in casi selezionati, della tomografia computerizzata possono ridurre il tasso ancora elevato di errori diagnostici.
Attualmente il trattamento delle forme di appendicite acuta più comuni, senza segni di peritonite diffusa, può avvalersi dell’utilizzo degli antibiotici, riservando la chirurgia ai casi ben più rari che si associano a peritonite diffusa oppure a quelli che non si risolvono con la terapia antibiotica.
Nuovi trattamenti locali non invasivi del carcinoma della prostata
La terapia focale nel trattamento del carcinoma alla prostata
1. 1° CONVEGNO SIURO LOMBARDIA Il Carcinoma della prostata nel terzo millennio Centro Convegni LE ROBINIE Solbiate Olona (Va) 25 ottobre 2008
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3. American Cancer Society , Cancer Facts & Figures 2005 Relative Survival Rates Survival in relation to men who do not have prostate cancer 1995-2005 stage at diagnosis 90% localized 5% distant
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8. Prevalence of prostate cancer among men with a prostate-specific antigen level < or =4.0 ng per milliliter. 7% 10% 17% 24% 27% 1% 1% 2% 5% 7% 0 5 10 15 20 25 30 ? 0-0.5 0.6-1.0 1.1-2.0 2.1-3.0 3.1-4.0 PSA Level (ng/ml) % of Men with Prostate Cancer and High- Grade Disease Percent with Prostate Cancer Percent with Gleason > 7 Disease Thompson IM, et al. N Engl J Med. 2004 May 27;350(22):2239-46 . (13%) (10%) (12%) (19%) (25%) Relationship of the PSA Level to the Prevalence of Prostate Cancer and High-Grade Disease
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11. The PCA3 Score in low volume /low grade PCa is significantly lower than in significant PCa Low volume: tumour volume < 0.5 mL; Low grade: Gleason Score 6 Nakanishi H, et al. ASCO 2007 Prostate Cancer Symposium: abs. 354
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17. Ahmed HU et al. (2007) Will focal therapy become a standard of care for men with localized prostate cancer? Nat Clin Pract Oncol 4 : 632 –642 doi:10.1038/ncponc0959 Figure 1 Radical prostatectomy step sections.
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23. Ahmed HU et al. (2007) Will focal therapy become a standard of care for men with localized prostate cancer? Nat Clin Pract Oncol 4 : 632 –642 doi:10.1038/ncponc0959 Figure 2 Template mapping biopsies demonstrating 4 cores positive for adenocarcinoma out of a total of 30 cores.
37. 0 20 40 60 80 100 Radical Prostatectomy Beam RT HIFU Comparison HIFU 5-year biochemical survival with the range published for low-risk prostate cancer since 2000 Biochemical Disease Free Rate (%) 82% 1 94% 4 84% 5 1. Blana et al European Urology, In press, 2007 2. De Meerleer et al Radiother Oncol. 2007;82(2):160-6. 3. Goldner et al Strahlenther Onkol. 2006;182(9):537-42. 4. Stokes et al Int J Radiat Oncol Biol Phys. 2000;47(1):129-36. 5. Ciezki et al Int J Radiat Oncol Biol Phys. 2004;60(5):1347-50. 100% 2 55% 3
38. 0 20 40 60 80 100 Radical Prostatectomy Beam RT HIFU Comparison HIFU 5-year biochemical survival with the range published for moderate-risk prostate cancer since 2000 Biochemical Disease Free Rate (%) 75% 1 87% 4 72% 5 1. Blana et al European Urology, In press, 2007 2. De Meerleer et al Radiother Oncol. 2007;82(2):160-6. 3. Goldner et al Strahlenther Onkol. 2006;182(9):537-42. 4. Stokes et al Int J Radiat Oncol Biol Phys. 2000;47(1):129-36. 5. Ciezki et al Int J Radiat Oncol Biol Phys. 2004;60(5):1347-50. 94% 2 40% 3
39. 0 10 20 30 40 50 Radical Prostatectomy ‡ HIFU † Beam RT ‡ 7% 1 2.3% 2 49% 5 5% 6 15% 3 0% 4 Occurrence (%) † excluding grade I, ‡ pad rate. 1. Ficarra et al, BJU Int. 2006 Dec;98(6):1193-8. 2. Chaussy and Thuroff Curr Urol Rep. 2003;4(3):248-52. 3. Zelefsky et al, Int J Radiat Oncol Biol Phys. 2002 Aug 1;53(5):1111-6; 4. Brabbins et al, Int J Radiat Oncol Biol Phys. 2005 Feb 1;61(2):400-8. 5. Steineck et al, N Engl J Med. 2002 Sep 12;347(11):790-6; 6. Abou-Elela et al, Eur J Surg Oncol. 2007; 33:96-101 Incontinence (range published in the literature since 2000)
40. Impotence (range published in the literature since 2000) 0 20 40 60 80 100 Radical Prostatectomy HIFU Beam RT 91% 5 14% 6 63% 3 41% 4 1. Blana et al Urology. 2004 Feb;63(2):297-300. 2. Thuroff et al. J Endourol. 2003 Oct;17(8):673-7. 3. Potosky et al, J Natl Cancer Inst. 2000 Oct 4;92(19):1582-92; 4. Matalinska et al, J Clin Oncol. 2001 Mar 15;19(6):1619-28; 5. Matalinska et al, J Clin Oncol. 2001 Mar 15;19(6):1619-28; 6. Walsh et al, J Urol. 2000 Jun;163(6):1802-7. Occurrence (%) 13% 2 53% 1
41. Rectal Injury (range published in the literature since 2000) 1. Gelet et al J Endourol. 2000 Aug;14(6):519-28; 7. Poissonnier et al Prog Urol. 2003;13(1):60-72; 3. Chrouser et al J Urol. 2005 Jun;173(6):1953-7. 4. Gillitzer et al J Urol. 2004 Jul;172(1):124-8. 5. Shrader-Bogen et al. Cancer. 1997 May 15;79(10):1977-86. 6. Lim et al J Urol. 1995 Oct;154(4):1420-5. 0 1 2 3 4 5 Radical Prostatectomy HIFU (pre 2003) Beam RT Occurrence (%) ≤ 1.0 % 1 0 10 20 30 40 50 Radical Prostatectomy HIFU Beam RT 19% 6 0% 43% 5 0% Occurrence (%) NONE REPORTED Severe (fistula – requiring intervention) Moderate (bleeding, urgency, diarrhea) ≤ 0.6 % 3 ≤ 1.1 % 4 0% ≤ 0.5 % 2 HIFU (post 2003)