This document discusses driving step changes in manufacturing operations through predictive insights using Pharma 4.0. It begins with an introduction of the speaker, Toni Manzano, who has expertise in data science, artificial intelligence, and big data in regulated environments like pharmaceuticals. The document then discusses the concept of Pharma 4.0 and digitization, the meaning and opportunities of big data, and the lagging adoption of technologies like AI and cloud in the pharmaceutical industry compared to others. It also addresses changing perspectives on AI, opportunities in using AI for regulatory decision making, and concepts relevant to applying AI like data preparation, quality, and types of algorithms. Risks of not adequately validating software are also mentioned.
AI in pharmacovigilance: Best practices and Regulatory GuidanceBiologit
Across many industries the regulatory landscape for deploying ML systems is still evolving: with many so frameworks and proposals being put forward, how do practitioners make progress?
In this talk we discuss our journey in developing solutions for the drug safety industry and translating high level guidance into actionable processes that engineering teams can adopt. Our best practices are at the core of the Biologit MLM-AI solution for medical literature monitoring of adverse events.
We also investigate how good documentation practices from ML transparency research can help you meet your regulatory needs.
Presented at: Datalift summit, Berlin, 2022
About Biologit:
biologit MLM-AI is a scientific literature monitoring platform for active safety surveillance that is simple to use, fully web-enabled and powered by AI. Our validated and compliant platform offers true productivity gains for pharmacovigilance workflows and safety screening of medical devices, cosmetics or veterinary products.
Continuous Improvement through Data Science From Products to Systems Beyond C...ijtsrd
The field of data science has become integral to the evolution of industries and technological advancements. This abstract explores the multifaceted role of data scientists in various domains, encompassing product and services development as well as specialized areas like Cyber Physical Systems. In product based companies, data scientists drive innovation by enhancing user experiences, optimizing costs, ensuring connectivity, and refining communication strategies. Leveraging machine learning models, they contribute to personalized interfaces, predictive maintenance, and efficient resource allocation, ultimately influencing the success of products in competitive markets. In services based companies, data scientists play a vital role in improving user interactions, optimizing operational costs, ensuring connectivity, and refining communication strategies. Through predictive analytics, they enable proactive service maintenance, improve resource allocation, and drive continuous improvement in service delivery. Within the context of Industry 4.0, data scientists contribute to the seamless integration of physical and digital systems. They monitor and analyze real time data from sensors, predict equipment failures, optimize system performance, and ensure the security of interconnected systems, fostering efficiency and reliability. Throughout these applications, data scientists operate at the nexus of technology, statistics, and domain expertise. Their responsibilities include data collection, preprocessing, model development, integration, and continuous improvement. Collaboration with cross functional teams ensures that data driven solutions align with organizational goals, fostering a holistic approach to problem solving. As the field of data science continues to evolve, data scientists remain pivotal in unlocking the potential of data to address complex challenges, drive innovation, and contribute to the ongoing transformation of industries and societies. Their role extends beyond analytical expertise, encompassing interdisciplinary collaboration skills that position them as essential contributors to the dynamic landscape of data driven decision making. Manish Verma "Continuous Improvement through Data Science: From Products to Systems: Beyond ChatGPT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61211.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61211/continuous-improvement-through-data-science-from-products-to-systems-beyond-chatgpt/manish-verma
Whether it’s meeting the next big client, forming your next business partnership, learning new skills, getting advice from peers, or keeping up with industry information, the return on investment of attending the right events can be substantial.
As a technology vendor that regularly exhibits, speaks and advises clinical conferences all over the world, we’ve seen and attended our fair share of fantastic, and not so fantastic industry conferences. Let’s be honest, there are hundreds of conferences scattered on every continent, but which one is going to give you the best return for your time and money? We’ve laid out our list of top conferences to attend in 2019 that will make you want to start packing today.
The document summarizes key points from the SCDM EMEA Conference that took place from October 23-25, 2019. It discusses how artificial intelligence can help improve healthcare and clinical research. Specifically, it explores how AI is currently used across the drug development process and clinical trials. This includes areas like enrolling more appropriate patients, optimizing trials, and analyzing real-world data. The document also notes opportunities for AI to reduce costs, expand knowledge, and improve efficiency in clinical research. Throughout, it emphasizes the importance of data and using AI technologies like wearables to generate more data for analysis.
Maximize Your Understanding of Operational Realities in Manufacturing with Pr...Bigfinite
Maximize Your Understanding of Operational Realities in Manufacturing with Predictive Insights using Big Data, Artificial Intelligence, and Pharma 4.0
by Toni Manzano, PhD, Co-founder and CSO, Bigfinite
PDA Annual Meeting 2020
Cognitive Era and Introduction to IBM WatsonSubhendu Dey
- The document introduces the cognitive era and IBM Watson. It discusses how exponential growth of data is affecting various sectors like healthcare, government, and media.
- It describes how IBM Watson is a cognitive system that uses natural language processing and builds on domain knowledge to understand language and derive answers from evidence.
- The foundational technologies behind Watson draw upon fields like big data analytics, artificial intelligence, cognitive experience, knowledge and computing infrastructure and include over 50 technologies like deep learning, machine learning, natural language processing and knowledge graphs.
6th International Conference on Advanced Computing (ADCO 2019)ijait
The 6th International Conference on Advanced Computing (ADCO 2019) will be held August 24-25, 2019 in Vienna, Austria. The conference will provide an international forum for researchers and practitioners from academia and industry to share knowledge and results in theory, methodology and applications of computing. Topics will include high performance computing, pervasive computing, green computing, and more. Authors are invited to submit papers by June 22, 2019 to be considered for publication in the proceedings.
This document summarizes a research paper about big data analytics applications in supply chain management. It discusses how big data is defined in terms of volume, velocity, and variety of data. It also describes sources of big data in supply chains from transactions, social media, sensors, and other systems. The paper reviews potential benefits of big data analytics for supply chain operations in areas like product development, demand forecasting, and distribution optimization. Challenges of utilizing large and diverse data sources for supply chain decision making are also examined.
AI in pharmacovigilance: Best practices and Regulatory GuidanceBiologit
Across many industries the regulatory landscape for deploying ML systems is still evolving: with many so frameworks and proposals being put forward, how do practitioners make progress?
In this talk we discuss our journey in developing solutions for the drug safety industry and translating high level guidance into actionable processes that engineering teams can adopt. Our best practices are at the core of the Biologit MLM-AI solution for medical literature monitoring of adverse events.
We also investigate how good documentation practices from ML transparency research can help you meet your regulatory needs.
Presented at: Datalift summit, Berlin, 2022
About Biologit:
biologit MLM-AI is a scientific literature monitoring platform for active safety surveillance that is simple to use, fully web-enabled and powered by AI. Our validated and compliant platform offers true productivity gains for pharmacovigilance workflows and safety screening of medical devices, cosmetics or veterinary products.
Continuous Improvement through Data Science From Products to Systems Beyond C...ijtsrd
The field of data science has become integral to the evolution of industries and technological advancements. This abstract explores the multifaceted role of data scientists in various domains, encompassing product and services development as well as specialized areas like Cyber Physical Systems. In product based companies, data scientists drive innovation by enhancing user experiences, optimizing costs, ensuring connectivity, and refining communication strategies. Leveraging machine learning models, they contribute to personalized interfaces, predictive maintenance, and efficient resource allocation, ultimately influencing the success of products in competitive markets. In services based companies, data scientists play a vital role in improving user interactions, optimizing operational costs, ensuring connectivity, and refining communication strategies. Through predictive analytics, they enable proactive service maintenance, improve resource allocation, and drive continuous improvement in service delivery. Within the context of Industry 4.0, data scientists contribute to the seamless integration of physical and digital systems. They monitor and analyze real time data from sensors, predict equipment failures, optimize system performance, and ensure the security of interconnected systems, fostering efficiency and reliability. Throughout these applications, data scientists operate at the nexus of technology, statistics, and domain expertise. Their responsibilities include data collection, preprocessing, model development, integration, and continuous improvement. Collaboration with cross functional teams ensures that data driven solutions align with organizational goals, fostering a holistic approach to problem solving. As the field of data science continues to evolve, data scientists remain pivotal in unlocking the potential of data to address complex challenges, drive innovation, and contribute to the ongoing transformation of industries and societies. Their role extends beyond analytical expertise, encompassing interdisciplinary collaboration skills that position them as essential contributors to the dynamic landscape of data driven decision making. Manish Verma "Continuous Improvement through Data Science: From Products to Systems: Beyond ChatGPT" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd61211.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/61211/continuous-improvement-through-data-science-from-products-to-systems-beyond-chatgpt/manish-verma
Whether it’s meeting the next big client, forming your next business partnership, learning new skills, getting advice from peers, or keeping up with industry information, the return on investment of attending the right events can be substantial.
As a technology vendor that regularly exhibits, speaks and advises clinical conferences all over the world, we’ve seen and attended our fair share of fantastic, and not so fantastic industry conferences. Let’s be honest, there are hundreds of conferences scattered on every continent, but which one is going to give you the best return for your time and money? We’ve laid out our list of top conferences to attend in 2019 that will make you want to start packing today.
The document summarizes key points from the SCDM EMEA Conference that took place from October 23-25, 2019. It discusses how artificial intelligence can help improve healthcare and clinical research. Specifically, it explores how AI is currently used across the drug development process and clinical trials. This includes areas like enrolling more appropriate patients, optimizing trials, and analyzing real-world data. The document also notes opportunities for AI to reduce costs, expand knowledge, and improve efficiency in clinical research. Throughout, it emphasizes the importance of data and using AI technologies like wearables to generate more data for analysis.
Maximize Your Understanding of Operational Realities in Manufacturing with Pr...Bigfinite
Maximize Your Understanding of Operational Realities in Manufacturing with Predictive Insights using Big Data, Artificial Intelligence, and Pharma 4.0
by Toni Manzano, PhD, Co-founder and CSO, Bigfinite
PDA Annual Meeting 2020
Cognitive Era and Introduction to IBM WatsonSubhendu Dey
- The document introduces the cognitive era and IBM Watson. It discusses how exponential growth of data is affecting various sectors like healthcare, government, and media.
- It describes how IBM Watson is a cognitive system that uses natural language processing and builds on domain knowledge to understand language and derive answers from evidence.
- The foundational technologies behind Watson draw upon fields like big data analytics, artificial intelligence, cognitive experience, knowledge and computing infrastructure and include over 50 technologies like deep learning, machine learning, natural language processing and knowledge graphs.
6th International Conference on Advanced Computing (ADCO 2019)ijait
The 6th International Conference on Advanced Computing (ADCO 2019) will be held August 24-25, 2019 in Vienna, Austria. The conference will provide an international forum for researchers and practitioners from academia and industry to share knowledge and results in theory, methodology and applications of computing. Topics will include high performance computing, pervasive computing, green computing, and more. Authors are invited to submit papers by June 22, 2019 to be considered for publication in the proceedings.
This document summarizes a research paper about big data analytics applications in supply chain management. It discusses how big data is defined in terms of volume, velocity, and variety of data. It also describes sources of big data in supply chains from transactions, social media, sensors, and other systems. The paper reviews potential benefits of big data analytics for supply chain operations in areas like product development, demand forecasting, and distribution optimization. Challenges of utilizing large and diverse data sources for supply chain decision making are also examined.
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Patrick Van Renterghem
Organisations need to make sure that they use AI in an appropriate way. Martijn and Hugo explain how to ensure that the developments are ethically sound and comply with regulations, how to have end-to-end governance, and how to address bias and fairness, interpretability and explainability, and robustness and security.
During the conference, we looked at an example AI development process with focussing on the risks to be managed and the controls that can be established.
AI and the Future of Clinical Research - CDISC 2020 US InterchangeRyan Tubbs
This document provides information about a virtual CDISC 2020 US Interchange event on October 7-8, 2020. It includes a disclaimer noting the views expressed do not necessarily reflect CDISC's official policy. The remainder summarizes a presentation by Ryan Tubbs on AI and the future of clinical research, including how Microsoft's cloud platform can provide scalable, flexible, and compliant access to diverse health data sources to enable data sharing and further innovation across the clinical research value chain. It outlines Microsoft's principles for responsible AI and discusses how various data sets could be used to power AI for health and life sciences.
In 2020, there will be one Predictive Analytics World multi-event in the US: Machine Learning Week (formerly Mega-PAW), May 31 – June 4, 2020 in Las Vegas.
Maximizing Production Efficiency with Big Data Analytics in semiconductor Man...yieldWerx Semiconductor
Semiconductor manufacturing is a complex, high-tech process that generates a large volume of data. Utilizing this data effectively is critical for improving production yield, maintaining product quality, and driving efficiency across operations. Enter big-data analytics. While the term “big data” often refers to vast data sets that are too large for traditional data-processing tools to handle, its importance in the semiconductor manufacturing industry can't be understated. Big-data and yield analytics not only provides ways to process, analyze, and draw insights from these large volumes of data but also facilitates more efficient decision-making, informed by detailed, real-time data insights.
See the Whole Story: The Case for a Visualization PlatformEric Kavanagh
Seeing is believing, which is why data visualization continues to play a major role in helping businesses understand their data. But there's more than meets the eye. Underneath that stimulating surface layer, some data environments are much more organized -- and thus reliable -- than others. The key to success? Taking a platform approach to address the entire end-to-end process of delivering governed, scalable analytics.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why a platform approach to visual analytics enables the kind of governance that today's organizations need. He'll be briefed by Dan Brault of Qlik, who will showcase his company's analytics platform which was built from the ground up with the design point of delivering analytics to everyone in an organization. He'll stress the importance of governance, trust and scalability.
Demystifying Machine Learning for Manufacturing: Data Science for allInfosys
This document discusses using machine learning and analytics for manufacturing applications. It begins with an overview of industry 4.0 and the increasing connectivity in manufacturing through technologies like the industrial internet of things. It then discusses how machine learning techniques like classification, regression, clustering and dimensionality reduction can be applied to common use cases in manufacturing around areas like order to cash, core manufacturing, and procure to pay. Specific case studies are presented on using machine learning for energy optimization at Infosys campuses and predicting churn for a automotive manufacturer's connected vehicle subscription services. Visualization and condition-based monitoring using artificial intelligence are also discussed.
Business analytics plays a pivotal role in Industry 4.0 by optimizing processes, enhancing efficiency, and driving growth. Specifically, the document discusses how business analytics is employed in predictive maintenance, supply chain optimization, and quality control. Predictive maintenance uses analytics to proactively address equipment failures and reduce costs. Supply chain optimization relies on analytics to optimize resource allocation and minimize costs. Quality control uses real-time monitoring and analytics to enhance product quality and reduce waste.
5th International Conference of Managing Value and Supply ijmpict
5th International Conference of Managing Value and Supply Chains (MaVaS 2019) is a peer-reviewed forum for presenting articles that contribute new results in all areas of value and supply chain management. The conference provides a platform to disseminate new ideas and new research, advance theories, and propagate best practices in the management of value and supply chain management, looking across both product and service-based businesses.
Spark 2019: In this presentation, Mark Fish, Ignite Lead Europe, discusses the future of big data and its role in the credit risk industry, looking at ways in which companies can overcome the common challenges that 'big data' poses in order to create more effective credit risk strategies.
AI Regulation Is Coming to Life Sciences: Three Steps to Take NowCognizant
To maximize the value of artificial intelligence and machine learning for patients, healthcare providers together with life sciences enterprises must gear up to meet the continually evolving regulatory landscape.
This document provides a brief history and overview of analytics. It discusses key questions addressed by analytics like what happened in the past, what is happening now, and what may happen in the future. It outlines different analytic communities that have emerged including statistics, business intelligence, web analytics, operational research, and artificial intelligence/data mining. Each community is described in terms of its origins, techniques/tools, trajectories, aims/themes, issues/limitations, and examples. The document suggests these communities are now merging together due to factors like increased data availability and demand for organizations to be more efficient.
Inspirata provides a digital pathology solution that automates the pathology workflow and transforms it from analog to digital. Their solution includes high-volume slide scanning, a digital pathology cockpit for viewing and analyzing slides, and integrated image analysis tools. Inspirata has partnered with multiple healthcare organizations to implement their solution and help digitize millions of slides. Their business model transforms capital expenses for healthcare organizations by providing the solution as a managed service.
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The goal of this paper is to propose a cloud primarily based information mining platform for researchers in three specific geographic locations for sharing studies information effects through the Internet even as final value effective, flexible, secure and privateness preserved. In addition, the take a look at evaluates the implementation demanding situations of the cloud primarily based platform and provides potential solutions to handle the diagnosed problems so that different similar studies can use this look at as a connection with decide whether or how to migrate from traditional to cloud based totally offerings. The contribution of cloud based services in the healthcare environments is a vital issue in the 21st century. In this paper, offering its advantages and equipment in hospitals, clinics as well as diagnostic centres, the already existing programs and offerings are separated in categories, which essentially problem data storage, computing strength, community, PaaS, SaaS, information analytics, commercial enterprise intelligence and venture management. Then, a few safety and risk evaluation troubles in cloud based services are analysed thoroughly collectively with a few case studies. Furthermore, a hazard evaluation with a comparative diagram among secured and non secured cloud structures in health is listed. Finally, conclusions with the cautioned destiny work are provided. Aishwarya Chauhan | Murugan R "Survey on Cloud-Based Services and its Security Analysis in the Healthcare Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49810.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/49810/survey-on-cloudbased-services-and-its-security-analysis-in-the-healthcare-sector/aishwarya-chauhan
This document summarizes a presentation given at the AWS Government, Education, and Nonprofit Symposium on June 25-26, 2015 in Washington DC. The presentation was given by Brian Kinlaw of CSC on architecting a big data platform. It covered CSC's big data platform as a service offering, including the architecture, security, and benefits it provides to customers in implementing big data solutions more quickly and managing the associated risks. Case studies were presented on how CSC has helped customers in various industries like manufacturing, transportation, and retail leverage big data to improve operations, customer support and gain new insights.
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International Journal of Data mining Management Systems (IJDMS)is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Data mining Management & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field, and establishing new collaborations in these areas.Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems..
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This document discusses exploring colorectal cancer genes through data mining techniques. It begins by stating that data mining is used in various medical applications like cancer classification based on microarray data. The document then discusses the existing and proposed systems for colorectal cancer classification. It analyzes a colorectal cancer dataset using various algorithms in WEKA, finding that Logistic Regression, IBK, KSTAR, NNGE, AD Tree and Random Forest achieved 100% accuracy. The document outlines the hardware, software and module requirements of the proposed system for colorectal cancer prediction and analysis using techniques like Naive Bayes and J48 decision trees. It concludes that the study aims to explore colorectal cancer using available gene expression data and applied
Big DataParadigm, Challenges, Analysis, and ApplicationUyoyo Edosio
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This document discusses big data, including its definition in terms of volume, variety and velocity; how it is analyzed using machine learning algorithms and distributed storage and processing; applications in various domains like healthcare, transportation and consumer products; and challenges like privacy, noisy data, skills shortage and immature tools. The conclusion recommends further research on hardware, algorithms and computational methods to effectively manage and gain insights from increasingly large data volumes.
The document discusses emerging technologies that will transform next generation supply chains, including cloud computing, blockchain, big data, IoT, AI/ML, and digital supply chains. It describes how these technologies will integrate to provide benefits such as more accurate forecasting and inventory control, simplified logistics and order fulfillment, enhanced remote management capabilities, faster production times, improved training, and tighter quality control. Specifically, cloud computing will centralize data access, blockchain can securely track transactions, big data analytics enables predictive insights, IoT connects all parties, and AI/ML automates decision making. Together these technologies pave the way for fully digital, customized, and data-driven supply chains.
This document discusses how AI can enable process innovation in organizations. It begins by explaining that while AI is associated with innovation, AI itself does not innovate - it enables innovators by handling information and tasks in new ways. The document then discusses how process innovation focuses on improving how work gets done through methods like Lean and Six Sigma. It provides examples of how AI can enable process innovation in areas like healthcare resource optimization, accelerating medical research, and improving manufacturing productivity. The document concludes by noting that the delivery of AI is also an opportunity for process innovation through methods like ModelOps.
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The document discusses emerging technologies that will transform next generation supply chains, including cloud computing, blockchain, big data, IoT, AI/ML, and digital supply chains. It describes how these technologies will integrate to provide benefits such as more accurate forecasting and inventory control, simplified logistics and order fulfillment, enhanced remote management capabilities, faster production times, improved training, and tighter quality control. Specifically, cloud computing will centralize data access, blockchain can securely track transactions, big data analytics enables predictive insights, IoT connects all parties, and AI/ML automates decision making. Together these technologies pave the way for fully digital, customized, and data-driven supply chains.
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Digital Decisioning for the New Decade - 2020 and BeyondSCL HUB Conference
This document discusses artificial intelligence (AI) trends for 2020 and beyond in supply chain decision making. It notes that AI represents a significant business opportunity for many companies according to a recent survey. The document outlines how AI-powered decisioning can provide value in supply chains through techniques like optimization, advanced analytics, machine learning, and deep learning. It provides examples of AI solution techniques that can help with problems like demand forecasting, root cause analysis, and supplier risk segmentation. Finally, it offers tips for organizations looking to apply AI, such as identifying early adopters and building applications iteratively.
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Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
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What is Augmented Reality Image Trackingpavan998932
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The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
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ISPE 2019 Driving Step Changes in Manufacturing Operations with Predictive Insights
1. Driving Step Changes in
Manufacturing Operations with
Predictive Insights
Practical Application of Pharma 4.0
2. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
CSO & co-founder of Bigfinite
Physics Degree, Master in Information and Knowledge Society, Post graduated in quality systems for
manufacturing and research pharmaceutical processes.
7 US Patents: encryption, transmission, storage and processing big data for regulated environments in the cloud
Articles & white papers based on cloud, pharmaceutical industry and data science
AI Health Xavier University of Cincinnati: AI Core Team and AI Manufacturing Team Lead
PDA. Scientific Committee & Europe co-chair
AI & big data SME for Sciences in the Spanish Parlament
Professor at the University Autonomous of Barcelona
Bioinformatics of Barcelona. Project Leader of the Data Integrity team
Toni
Manzano
3. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Pharma 4.0 ≈ Digitization + ICH Q10
4. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
What does big data mean from a regulatory perspective?
5. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
What does big data mean in GxP environments?
3600000 GB = 3600 TB per minute
Data transferred now
6. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Yet the adoption rate of big data, cloud technologies in Pharma is
lagging that of other industries
7. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Companies investing in AI by industry
8. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Opportunities enabled by digital and analytics are recognized
across sectors
Digital Quotient score. Points (out of 100)
9. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Pharma 4.0. Is this strategy well aligned?
2017: A Holistic Approach to Production Control: From Industry 4.0 to Pharma 4.0.
Herwig C., Wölbeling C., Zimmer T.
2018: Getting Ready for Pharma 4.0
Manzano T., Langer G.
2019: Pharma 4.0 – The New Frontier for the Pharma Industry
Minero T., Augeri A.
2019: The ISPE Pharma 4.0 Operating Models
Heesakkers H., Schmitz S., Kuchenbrod U., Wölbeling C., Zimmer T.
10. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Something is changing the world perspective regarding AI
11. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Something is changing in Pharma regarding AI
12. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Something is changing in Pharma regarding AI
13. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Area Priority
"Big Data" Leverage "Big Data" for regulatory decision-making
Biocompatibility Modernize biocompatibility and biological risk evaluation of device materials
Real-world evidence Leverage real-world evidence and employ evidence synthesis across multiple domains in regulatory decision-making
Clinical performance Advance tests and methods for predicting and monitoring medical device clinical performance
Clinical trial design Develop methods and tools to improve and streamline clinical trial design
Computational modeling Develop computational modeling technologies to support regulatory decision-making
Digital Health and
cybersecurity
Enhance the performance of Digital Health and medical device cybersecurity
Healthcare-associated
infections
Reduce healthcare associated infections by better understanding the effectiveness of antimicrobials, sterilization and
reprocessing of medical devices
Patient input Collect and use patient input in regulatory decision-making
Precision medicine and
biomarkers
Leverage precision medicine and biomarkers for predicting medical device performance, disease diagnosis, and
progression
CDRH Regulatory Science Priorities
CDRH's regulatory science priorities serve as a catalyst to improve the safety, effectiveness, performance, and
quality of medical devices and radiation-emitting products, and to facilitate introducing innovative medical
devices into the marketplace. Report of August 22th, 2019
14. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
What is Artificial Intelligence?
“ AI can be thought of as simulating the capacity for
abstract, creative, deductive thought - and particularly the
ability to learn - using the digital, binary logic of computers ”
Artificial Intelligence (AI) is no longer some bleeding
technology that is hyped by its proponents and mistrusted by
the mainstream. In the 21st century, AI is not necessarily
amazing. Rather, it is often routine. Evidence for the routine
and dependable nature of AI technology is everywhere.
“Verification and Validation and Artificial Intelligence”, Tim Menzies, Portland
State University, Charles Pecheur, NASA Ames Research Center. July 2004
15. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Data must be prepared before to use it:
DS invest the 80% of their time
16. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Warning Letters including references to
data management and data integrity
Source: fda.gov & PharmaceuticalOnline
17. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Expected and required data quality
18. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
When the statistical results have huge impact in the users
19. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
More than just multivariable models...
20. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
5 AI concepts
● Training/Test data
○ Problem’s dataset
● Algorithm
○ Mathematical procedure that creates the Model from the training data
● Model
○ Mathematical system that has been created from the exploration of a data set. It’s created
after an extensive learning process referred as training.
● Prediction / Classification / Recommendation / Recognition
○ Single inference over a model with an unseen sample
● Evaluation
○ Score evaluation of the Test dataset
21. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
More than just multivariable models...
22. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised and unsupervised algorithms
Learning Supervised Learning UnSupervised
➔ Linear Regressor
➔ Support Vector Machines
➔ Random Forests
➔ Neural Networks
➔ K-Nearest Neighbours
➔ Gradient Boosted Trees
➔ ...
vs
➔ K-Means Clustering
➔ Hierarchical Clustering
➔ Isolation Forests
➔ Graphical Lasso
➔ Bayesian Networks
➔ Markov Hidden Models
➔ ...
AI unsupervised Vision ExampleAI supervised Vision Example
23. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Decision Trees.
From a business decision point of view,
a decision tree is the minimum number
of yes/no questions that one has to ask,
to assess the probability of making a
correct decision, most of the time. As a
method, it allows you to approach the
problem in a structured and systematic
way to arrive at a logical conclusion.
Usually applied for root cause analysis
24. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Naïve Bayes Classication.
• P(A|B) is posterior probability
• P(B|A) is likelihood
• P(A) is class prior probability
• P(B) is predictor prior probability
Naïve Bayes classifiers are a family of simple
probabilistic classifiers based on applying
Bayes’ theorem with strong (naïve)
independence assumptions between the
features.
Some examples of usage:
• Deviations classification
• Clinical trials analysis
• Cause-effect analysis
25. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Naïve Bayes Classification.
26. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Supervised Learning. Logistic Regression.
Is a way of modeling a binomial outcome with
one or more explanatory variables. It measures
the relationship between the categorical
dependent variable and one or more
independent variables by estimating probabilities
using a logistic function, which is the cumulative
logistic distribution.
In general, regressions can be used in pharma applications such as:
• Predicting the quality of batches (ok / ko)
• Unexpected manufacturing stops
• Predicting the time to finish of a certain product
• Is there going to be a non conformity in a batch?
27. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Unsupervised Learning*. Principal Component Analysis
PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of
possibly correlated variables into a set of values of linearly uncorrelated variables called principal
components. It is not suitable in cases where data is noisy (all the components of PCA have quite a
high variance). Notice that domain knowledge is very important while choosing whether to go forward
with PCA or not.
Some of the PCA applications include:
• Compression
• Simplifying data for easier learning
• Dimensional reduction
• Identification of relevant dimensions
• Dimensionless analysis
* PCA can not be consiered as an AI algorithm
28. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Which types of software must be validated?
• All medical devices that contain software – even Class I devices – are subject to the Design
Control Provisions, and thus require software validation
• Any software used to automate any part of the device production process or any part of the
quality system must be validated for its intended use (21 CFR §820.70(i)).
• Computer systems used to create, modify, and maintain electronic records and to manage
electronic signatures (21 CFR §11.10(a)) must be validated.
29. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Not adequately validating software can lead to a Warning Letter...
4/12/2017
3. Failure to ensure that design verification shall confirm that the design output meets the
design input requirements, as required by 21 CFR 820.30(f). For example: Your firm has a
design input, (b)(4), of “the Remote Monitoring device shall only open network ports to
authorized interfaces” which is documented in Software System Requirements
Specification, Document. This is implemented as a design output in your firm’s DeviceName
Software Requirements Specification Uploads (b)(4).
This design output was not fully verified during your firm’s design verification activities.
According to your firm’s testing procedures, (b)(4), Final Configuration Test Procedures,
(b)(4) and Final Configuration Test Procedures Document (b)(4), the requirement was only
partially verified by testing that the network ports opened with an authorized interface. Your
testing procedures did not require full verification to ensure the network ports would not
open with an unauthorized interface.
30. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Trust in AI
Robustness Fairness Explainability Lineage
Ex. Adversarial
training methods
Bias
• Data collection
• Processing
• Labelling
Explainer
•Data
•Metadata
•Catalog
•Hyperparameters
•Governance
•Processing
•Integration
•Privacy.
31. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Trust in AI
Adversarial training methods example:
32. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Trust in AI
Adversarial training methods example:
33. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Use cases in Pharma: Root Cause Analysis
?
CQA: All of them under specs
CPP: Equivalent results except culture
duration
34. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Getting knowledge from AI in primary packaging
LC BCO Set-up Warm-up WO Reworks Reconcilliation
•Pens / minute
•Product / format
•Batch size
•Day of the year
•Time of the day
•Shift
•Downtime
•Stops
•Affected units by stops
Theoretical OEE Realistic OEE Deep process knowledge
35. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Use cases in Pharma: Unpurified Bulk bag selection
end
Start
unpurified bulk
manufacturing
drug substance
purification
From cell bank
To CMO for
formulation and filling
36. 2019 ANNUAL MEETING & EXPO
27-30 October | Las Vegas
Use cases in Pharma: Anomaly detection
Predict TTNF (Time To Next Failure)
Recommend actions to avoid Next Failure