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Proportional Hazards Model

The survival curve, which is the basic tool of what essentially we’re going to get to in the Cox proportional hazards model, is a graph of the percentage of people who survive longer than each period of time. So it starts at 100% at the time you start this thing everybody is surviving. And then it’s the probability of surviving more than one day, one week, one month, one year, two years, three years, five years, ten years, et cetera.

In randomized clinical trials, for example, we’re comparing a treatment versus some sort of control or comparison. Time to remission is a great outcome measure. Or time to recovery, if there is such a thing as recovery in many of these illnesses, is a wonderful outcome measure, specifically because it is so important to the patients. You know we could talk about reduction of symptoms, we could talk about a lot of other outcome measures. But what the patient is really interested in is, “Am I going to get well?”

To learn more from Dr. Helena Kraemer listen to the podcast episode below.

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    Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

    Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

    Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).