to

Time Series Analysis in Biomedical Science – What You Really Need to Know

For a few years now I have given a guest lecture on time series analysis in our School's Environmental Epidemiology course. The basic thrust of this lecture is that you should generally ignore what you read about time series modeling, either in papers or in books. The reason is because I find much of the time series literature is not particularly helpful when doing analyses in a biomedical or population health context, which is what I do almost all the time. First, most of the literature on time series models tends to assume that you are interested in doing prediction--forecasting future values in a time series. I almost am never doing this.

The Evolutionary Importance of Neutral vs. Adaptive Genes

When Charles Darwin articulated his theory of evolution by natural selection in On the Origin of Species in 1859, he focused on adaptations--the changes that enable organisms to survive in new or changing environments. Selection for favorable adaptations, he suggested, allowed ancient ancestral forms to gradually diversify into countless species. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. That concept was so powerful that we might assume evolution is all about adaptation. So it can be surprising to learn that for half a century, a prevailing view in scholarly circles has been that it's not.

Classification vs Prediction

It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. The classification rule must be reformulated if costs/utilities or sampling criteria change. Predictions are separate from decisions and can be used by any decision maker. Classification is best used with non-stochastic/deterministic outcomes that occur frequently, and not when two individuals with identical inputs can easily have different outcomes.

Classification vs. Prediction Statistical Thinking

It is important to distinguish prediction and classification. In many decisionmaking contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. The classification rule must be reformulated if costs/utilities or sampling criteria change. Predictions are separate from decisions and can be used by any decision maker. Classification is best used with non-stochastic/deterministic outcomes that occur frequently, and not when two individuals with identical inputs can easily have different outcomes.

Understanding Principal Component Analysis

Machine learning (ML) is a subset of artificial intelligence (AI) and it provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The algorithms employed within ML are used to find patterns in data that generate insight and help make data-driven decisions and predictions. These types of algorithms are utilized every day to make critical decisions in medical diagnosis, stock trading, transportation, legal matters and much more. Therefore, it can be seen why data scientists place ML on such a high pedestal; it provides a medium for high priority decisions, that can guide better business and smarter actions, in real-time without much human intervention. To learn, ML models use computational methods to understand information directly from data without relying on a predetermined equation.