This lecture discusses differential diagnosis, the process of distinguishing diseases with similar symptoms. It explores historical diagnostic methods like flowcharts and association-based models, highlighting their limitations. Bayesian models, offering probabilistic diagnoses, are introduced, along with utility-theoretic methods that consider the cost and benefit of different diagnostic approaches. The lecture also covers receiver operating characteristic (ROC) curves and the concept of rationality in medical decision-making. Modern approaches, including reinforcement learning and neural networks, are presented as advancements in diagnostic reasoning, though limitations and ongoing challenges remain. Diagnosis is the identification of the nature and cause of a certain phenomenon. And differential diagnosis is the distinguishing of a particular disease or condition from others that present similar clinical features. So doctors typically talk about differential diagnosis when they're faced with the patient. And they make a list of what are the things that might be wrong with this patient. And then they go through the process of trying to figure out which one it actually is. A symptom is something that you feel so if you're feeling dizzy then that's a symptom because it's not obvious to somebody outside you that you're dizzy or that you have a pain or such things. So MIT actually stopped using them shortly after my headache experience. But if you go over to a hospital and you look on the bookshelf of a junior doctor, you will still find manuals that look kind of like this, that say, how do we deal with tropical diseases, right? So you ask a bunch of questions.08:52And then, depending on the branching logic of the flowchart, it'll tell you whether this is serious or not. And the reason is because if you do your medical training in Boston, not going to see very many tropical diseases. And so you don't have a base of experience on the basis of which you can learn and become an expert on doing it.09:13And so they use this as a kind of cheat sheet. provide doctors with these library cards that represented diseases, and the holes now represented not mathematics versus literature, but they represented shortness of breath, this pain in the left ankle versus whatever. And again, as people came in and complained about some condition, he'd stick a needle through that condition, and you'd shake and up would come the cards that have that condition in common. So one of the obvious problems with this approach is that if, if you had two things wrong with you, right, then you would wind up with no cards very quickly, because, you know, nothing would fall out of the pile. So, this didn't go anywhere. More Shophsticated Model is Naive Bayes But in medicine people don't like to do math, even arithmetic March, and they prefer doing addition rather than the multiplication because it's easier. And so what they've done is they said, well, instead of representing all this data in a probabilistic framework, let's represent it as ODS.16:35And if you represent it as ODS, then the the odds of some disease, given a bunch of symptoms, given the independence assumption, is just the prior odds of the disease times the conditional odds, the likelihood ratio of each of the symptoms that you've observed.16:59So you just get to multiply these together. And then because they like adding more than multiplying, they said, let's take the log of both sides, right? And then you can just add them. And so if you remember when I was talking about medical data, there are things like the Glasgow coma score or the Apache score or various measures of how badly or, well a patient is doing that often involve adding up numbers corresponding to different conditions that they have. Reconstruction of the above problem shown in the video Sometimes we are interested in Sequence of Symptoms ROC Curve