Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients originating from the same source, by repeated random splitting of the data. However, such estimates tend to be highly overoptimistic when compared to accuracy obtained from deploying models to sources not represented in the dataset, such as a new hospital. The increasing availability of multi-source medical datasets provides new opportunities for obtaining more comprehensive and realistic evaluations of expected accuracy through source-level cross-validation designs. In this study, we present a systematic empirical evaluation of standard K-fold cross-validation and leave-source-out cross-validation methods in a multi-source setting. We consider the task of electrocardiogram based cardiovascular disease classification, combining and harmonizing the openly available PhysioNet CinC Challenge 2021 and the Shandong Provincial Hospital datasets for our study. Our results show that K-fold cross-validation, both on single-source and multi-source data, systemically overestimates prediction performance when the end goal is to generalize to new sources. Leave-source-out cross-validation provides more reliable performance estimates, having close to zero bias though larger variability. The evaluation highlights the dangers of obtaining misleading cross-validation results on medical data and demonstrates how these issues can be mitigated when having access to multi-source data.

When I was getting my MBA at the University of Iowa in 1981, my advisor Gary Fethke (who would later serve as University of Iowa interim president and Emeritus Professor in Business Analytics) convinced me to take a PhD class in econometrics. I think he was trying to punish me or something. I was totally overwhelmed in the class as student after student quickly answered questions about this economic theorem or that economic concept. Hands were popping up all over the room in a rush to answer these questions, while I sat in the back of the room madly trying to understand the applicability of these theorems and concepts. On the day of the final exam, I thought I was doomed.

Nature is an inhabitant for enormous number of species. All the species do perform complex activities with simple and elegant rules for their survival. The property of emergence of collective behavior is remarkably supporting their activities. One form of the collective behaviour is the swarm intelligence -- all agents poses same rules and capabilities. This equality along with local cooperation in the agents tremendously leads to achieving global results. Some of the swarm behaviours in the nature includes birds formations , fish school maneuverings, ants movement. Recently, one school of research has studied these behaviours and proposed artificial paradigms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Glowworm Swarm Optimization (GSO) etc. Another school of research used these models and designed robotic platforms to detect (locate) multiple signal sources such as light, fire, plume, odour etc. Kinbots platform is one such recent experiment. In the same line of thought, this extended abstract presents the recently proposed butterfly inspired metaphor and corresponding simulations, ongoing experiments with outcomes.

I am honored to collaborate on this week's blog with Fran Willis White, an industry expert on the role of change leadership and employee empowerment to drive cultural transformation. In collaborating on this blog, I discovered many similarities in the role of empowerment in the data science development process to optimize business outcomes, as well as the role of empowerment of the frontline business stakeholders to reinvent those same business outcomes. AI is a generational opportunity for organizations of all types and sizes to optimize their key to grow your business and operational processes, mitigate financial, compliance and regulatory risk, uncover new revenue streams, and create a more compelling customer experience. And the potential of AI is fueled by data, which is driving the desire for organizations to become data-driven. Unfortunately, organizations are failing at becoming data-driven (see "Data and AI Leadership Executive Survey 2022" from Tom Davenport and Randy Bean).

There are several technology and business forces in-play that are going to derive and drive new sources of customer, product and operational value. As a set up for this blog on the Economic Value of Data Science, let's review some of those driving forces. "Due to its ability to substantially improve productivity and boost economic output, Artificial Intelligence (AI) has the potential to increase economic growth rates by a weighted average of 1.7% and profitability rates by 38% across a variety of industries by 2035. Source: NorthBridge Consultants "The Artificial Intelligence Revolution: New Challenges & Opport…" Figure 1: Source: "The Artificial Intelligence Revolution: New Challenges & Opportunities" Data Science (Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning) holds the potential to exploit Big Data and IoT to create new sources of economic value (wealth). But what is the source of this economic value when the AI tools that are driving this economic growth (TensorFlow, Spark ML, Caffee2, Keras) are open source and equally available to all players?

Turns out, machines can bring more humanity to the conversations and interactions our brands are having every day. Leaders who are integrating AI's objective curiosity and performance speed with human creativity and problem-solving are finding new sources of insights and value that benefit their teams, their businesses, and most of all, their customers. An inescapable reality is that today's customers expect--not just want, but now demand--new heights of personalization. This is as true in b-to-b marketing as it is in b-to-c. And our news feeds remind us every day that the pressure is on marketers to deliver.

Futuri's AI application Topic Calls takes real-time data from Facebook, Twitter, Instagram and 100,000 new sources, and looks at early signals to determine what will be popular in a particular market or demographic up to 24 hours from the present. A newsroom or a brand content marketer "could look at a specific audience and determine where their investment of time and energy in terms of content generation is going to be best used," says Anstandig. Futuri's AI application Topic Calls takes real-time data from Facebook, Twitter, Instagram and 100,000 new sources, and looks at early signals to determine what will be popular in a particular market or demographic up to 24 hours from the present. A newsroom or a brand content marketer "could look at a specific audience and determine where their investment of time and energy in terms of content generation is going to be best used," says Anstandig.

Daniel Faggella: So, Lorena, I want to be able to dive into these various use cases of how artificial intelligence can start to unlock the value of data in the oil and gas space, and make this really tangible. I know the first category we wanted to talk about was really around the value of subsurface data, that there's a lot of subsurface data, obviously in the oil and oil and gas domain. Lorena Pelegrín: And we see that AI or our ML can help these teams find the data and process the data much, much faster. Yeah, and I imagine a good deal of this has to do with, tell me if I'm wrong here, Lorena, but having an understanding of your company from working with you guys for a little while, I would imagine that the digitization of these myriad, somewhat chunky paper forms is one part of the process here, using some kind of optical character recognition stuff and working with historical records and maybe congealing and digitizing that. Daniel Faggella: But you let me know, Lorena, where does M&A, where does this data come in, in terms of the real value for potential M&A? Daniel Faggella: So Drone Deploy, for example, was on talking about what they do in the energy space with drones and video data to look at and inspect assets.

Increasingly, Tax Administrations (TAs) use new ICTs to be more effective and efficient in their management, and the digitalization process has accelerated exponentially in the current circumstances. Within this new technology, Artificial Intelligence (AI) presents multiple benefits for TAs, since it transforms data into a knowledge and impact asset for tax and customs management, and thus can achieve the intelligent use of such data and the way it interacts with taxpayers. The combination of AI, Internet of Things (IoT), Data Analysis and Data Analytics, will give exponential benefits through the collection and analysis of a large volume of taxpayer data in real time for better decision making that will positively impact several administrative areas of the TAs. In the collection function, AI is used to predict the collection, in customs at airports with facial recognition systems, among many other uses that will surely continue to be enhanced in the future. In this commentary, I would like to share some concrete examples of AI applied in audits or audits, both in massive or extensive controls and in intensive controls.

After decades in research labs, machine learning is now getting enormous attention for real-world applications that harness the technology's formidable power to discern patterns in huge quantities and types of data at high speed: fraud detection, customer 360, facial recognition, workflow management, shopping personalization and much more. The payback of such initiatives can be big. But even greater opportunities lie in creating advanced analytic systems that use machine learning's unmatched ability to see, organize and leverage insights from ever-growing mounds of data to unlock the deep, transformative potential of Big Data and the Internet of Things. To get to the next level of machine learning, companies must develop a sound business case; implement machine learning algorithms for speed at scale; use systems equipped with processors with multiple integrated cores, faster memory subsystems, and develop architectures that can handle massive amounts data in real time. For many organizations, it is an ideal time to build on or begin machine-learning experience, deepen knowledge, and reap the benefits and competitive advantages this sophisticated data analytics technology can provide.