Is Machine Learning The Future Of Coffee Health Research? - AI Summary

The stories generally go like this: "a study finds drinking coffee is associated with a X% decrease in [bad health outcome]" followed shortly by "the study is observational and does not prove causation." In a new study in the American Heart Association's journal Circulation: Heart Failure, researchers found a link between drinking three or more cups of coffee a day and a decreased risk of heart failure. Led by David Kao, a cardiologist at University of Colorado School of Medicine, researchers re-examined the Framingham Heart Study (FHS), "a long-term, ongoing cardiovascular cohort study of residents of the city of Framingham, Massachusetts" that began in 1948 and has grown to include over 14,000 participants. Able to analyze massive amounts of data in a short amount of time--as well as be programmed to handle uncertainties in the data, like if a reported cup of coffee is six ounces or eight ounces--machine learning can then start to ascertain and rank which variables are most associated with incidents of heart failure, giving even observational studies more explanatory power in their findings. And indeed, when the results of the FHS machine learning analysis were compare to two other well-known studies, the Cardiovascular Heart Study (CHS) and the Atherosclerosis Risk in Communities study (ARIC), the algorithm was able "to correctly predict the relationship between coffee intake and heart failure."