Diagnostic models based on personalized analysis of trends (PAT).

Many changes have taken place in medicine over the last century. In the first-half of the 20th century physicians were faced with the challenge of making diagnoses with too little information, often resorting to exploratory surgery to confirm the presence or absence of a condition. Due to rapid technological advances during the second-half of the 20th century, and continuing to this day, the position of the physician has now shifted from an information-poor environment to an environment with too much information, often exceeding the limits of human decision-making capabilities. To take full advantage of all available information, a new approach based on refined automated decision support methods is needed to assist the physician in the decision-making process. Medical decision support systems need to evolve from stand-alone systems to cooperative systems in which the physician becomes the decision maker, but relies on the decision support system to sift through information to determine relevant trends. In this paper, a decision support system that combines a number of methodologies for trend analysis is described, along with examples in cardiology. The methods have also been used in applications in neurology as well as cancer diagnosis and prognosis.