Author Dr Andy Wynn, along with contributions from leaders of some of the biggest companies on the planet (including DuPont, 3M, Johnson Matthey and Imerys), finally reveals the secret of how you can unlock the potential in your business to grow. In the follow up to his book Transforming Technology into Profit, Andy takes you on a journey that explains how the organisation and culture within your business impact your company’s ability to innovate. Using his "Three Tiers of Successful Innovation", Andy reveals how to clearly identify what aspects of your business are holding back growth and how to use that information to transform your business into one that facilitates growth by revitalising the structure and culture of your business to focus employee behaviours on adding profitable new revenue streams. Part sequel and part companion volume to his previous book, Andy finally "cracks the code" on how to unleash your business’ ability to create and successfully commercialise new products. Written in the author’s trademark conversational style, Cracking the Innovation Code offers a refreshingly practical and real-world view, written by someone who has been there and done it, and enhanced by valuable case studies and contributions from numerous senior executives who have made life-long careers out of leading innovation, and with a passion for leading industrial manufacturing businesses.

Employing off-the-shelf machine learning models is not an innovation. The journey through technicalities and innovation in the machine learning field is ongoing, and we hope this book serves as a compass, guiding the readers through the evolving landscape of artificial intelligence. It typically includes model selection, parameter tuning and optimization, use of pre-trained models and transfer learning, right use of limited data, model interpretability and explainability, feature engineering and autoML robustness and security, and computational cost – efficiency and scalability. Innovation in building machine learning models involves a continuous cycle of exploration, experimentation, and improvement, with a focus on pushing the boundaries of what is achievable while considering ethical implications and real-world applicability. The book is aimed at providing a clear guidance that one should not be limited to building pre-trained models to solve problems using the off-the-self basic building blocks. With primarily three different data types: numerical, textual, and image data, we offer practical applications such as predictive analysis for finance and housing, text mining from media/news, and abnormality screening for medical imaging informatics. To facilitate comprehension and reproducibility, authors offer GitHub source code encompassing fundamental components and advanced machine learning tools.

The empowered patients, new-age technologies such as artificial intelligence (AI), machine learning (ML), big data analytics, real-world data and evidence, blockchain, electronic health records (EHRs), digital therapeutics, cloud computing, and innovative marketing frameworks like design thinking, customer journey mapping, omnichannel, closed-loop marketing, personalization and agile ways of working are transforming the way healthcare is delivered, affecting the pharmaceutical industry. Additionally, big tech companies such as Amazon, Alphabet, Apple, and Microsoft are disrupting by offering non-pharmacological solutions with innovative digital technologies to provide a seamless customer experience in the patient journey. The recent COVID-19 pandemic added rocket fuel to the digital transformation of the pharmaceutical industry, changing the entire model of care and ingraining telemedicine in the healthcare ecosystem. Digital Transformation has become inevitable and imminent. Therefore, pharma must reimagine its entire strategy and embrace digital transformation to succeed in this rapidly changing marketing environment that is becoming increasingly complex. Reimagine Pharma Marketing: Make It Future-Proof introduces all these technology frameworks. Additionally, the book presents one hundred and two case studies showing how some of the leading pharmaceutical companies are applying the new age technologies and marketing frameworks effectively. It can be your single-source guidebook unraveling the future so you can manage it!Contents: 1. Reimagine Everything — Reimagine Every Element of Pharmaceutical Marketing Mix 2. Reimagine the Technology— How Pharma Can Harness the Power of New and Emerging Technologies 3. Reimagine Stakeholder Engagement—Winning with New Rules of Engagement 4. The Future of Pharma—A Look into the Crystal Ball Epilogue You’re Gonna Need a Bigger Boat!

The World Health Organization defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”, and its constitution also asserts that health for all people is “dependent on the fullest co-operation of individuals and States”. The ongoing pandemic has highlighted the power of both healthy and unhealthy information, so while healthcare and public health services have depended upon timely and accurate data and continually updated knowledge, social media has shown how unhealthy misinformation can be spread and amplified, reinforcing existing prejudices, conspiracy theories and political biases. This book presents the proceedings of MedInfo 2021, the 18th World Congress of Medical and Health Informatics, held as a virtual event from 2-4 October 2021, with pre-recorded presentations for all accepted submissions. The theme of the conference was One World, One Health – Global Partnership for Digital Innovation and submissions were requested under 5 themes: information and knowledge management; quality, safety and outcomes; health data science; human, organizational and social aspects; and global health informatics. The Programme Committee received 352 submissions from 41 countries across all IMIA regions, and 147 full papers, 60 student papers and 79 posters were accepted for presentation after review and are included in these proceedings. Providing an overview of current work in the field over a wide range of disciplines, the book will be of interest to all those whose work involves some aspect of medical or health informatics.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

A "good" programmer can outproduce five, ten, and sometimes more run-of-the-mill programmers. The secret to success for any software company then is to hire the good programmers. But how to do that? In Joel on Hiring, Joel Spolsky draws from his experience both at Microsoft and running his own successful software company based in New York City. He writes humorously, but seriously about his methods for sorting resumes, for finding great candidates, and for interviewing, in person and by phone. Joel’s methods are not complex, but they do get to the heart of the matter: how to recognize a great developer when you see one.