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Medical Inference and Context in Diagnosis

Explore the myths and realities of medical inference in diagnosis. Delve into classical inference, Bayesian analysis, and informal inference methods. Understand the limitations and challenges in reasoning to uncover the truth in medical cases. Discover the controversies and complexities in diagnosing conditions through real-world examples.

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Medical Inference and Context in Diagnosis

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  1. Medical Inference and Context in Diagnosis William R Oliver, MD Assistant Medical Examiner Regional Forensic Center Knoxville, TN

  2. Two parts to this talk First: A general discussion of medical inference Second: An experiment about patterned injury diagnosis The PDF of this and related articles with references can be found at: http://www.forensicpath.biz/cog

  3. Acknowledgements • Expert Panel • Dr. Randy Hanzlick • Dr. Wendy Gunther • Dr. John Butts • Dr Jon Arden • Dr Xiangming Fang • Dr. Karen Kelly • And everybody who donated an image! • Dr. David Fowler • Dr. Lynda Biedrzycki • Dr. Mark Lieberman • Dr. Jamie Downs • Dr. Kris Sperry • Dr. Keith Pinckard • Dr. Greg Schmunk • Dr. John Hunsaker • Dr. Vic Weedn 0.72

  4. Disclaimer and Funding • No conflicts of interest • This was funded by the National Institute of Justice

  5. WHAT I'M GOING TO TALK ABOUT • Stuff we don't do – classical inference • Stuff we pretend to do, but really don't – Bayesian analysis • Stuff we really do – informal inference • How we do it – the myth of the “scientific method” in diagnosis • Some examples mixed in • This talk originally done for abusive head trauma community, so it will involve shaken baby stuff as examples, as well as some social stuff • Some of these will be controversial subjects. It's an exercise for the reader. I want to discuss some things to think about how people think about things, not tell you want to think. • If you are easily triggered, get up and leave now.

  6. WHY WE SHOULD CARE • We really want the truth about cases • But we can't get to the truth until we can be honest about our reasoning. • Often, arguments about being “scientific” are proxy arguments about other things • Often we are attacked on our reasoning

  7. EXAMPLE -- TASER

  8. EXAMPLE -- TASER

  9. LEGAL CHALLENGES • Increasingly, our diagnoses are challenged on the basis of the quality of the inferences we make • Daubert • Cross examination • Consensus

  10. DID YOU USE THE “SCIENTIFIC METHOD?”

  11. SO FIRST LET'S TALK ABOUT CLASSICAL INFERENCE • We need to know it because it's referred to so often • We need to recognize it so we can use it when we need to • We need to understand that, for the most part, we don't use these in diagnosis, and then learn to be explicit about what we really do

  12. DEDUCTION • Reasoning from the general to the specific, except in special cases such as enumeration. • You know the drill: If A then B A Thus, B All balls in the box are red This ball is from the box This ball is red Boring.

  13. PROBLEMS WITH DEDUCTION • Absolute – deduction is not probabilistic. • “Probabilistic deduction” isn't. While you can find it in the artificial intelligence literature, it's a term of art, and a bad one. • It does not allow “weighing” of evidence (since that is by definition an exercise in probability • It does not allow the discovery of new knowledge (though that is not a big problem in individual cases) – it can only apply knowledge.

  14. THE PARADOX OF DEDUCTION • "If in an inference the conclusion is not contained in the premises, it cannot be valid; and if the conclusion is not different from the premises, it is useless; but the conclusion cannot be contained in the premises and also possess novelty; hence inferences cannot be both valid and useful.” ) Cohen, M, Nagel, E. An introduction to logic and scientific method. 2002 • All dolphins are mammals; this is a dolphin; thus this is a mammal. • However, “dolphin” and “mammal” are definitional. Thus you are not saying much at all that isn't tautologic.

  15. THAT'S NOT TO SAY YOU CAN'T USE IT WHEN IT DOES WORK • All gunshot wounds surrounded by soot are close range entrance wounds • This wound is surrounded by soot • Thus, this is a close range entrance wound. • But note again, really, that you are not really doing “inference” beyond applying a de facto definition...

  16. INDUCTION • Inferring something from a repeated result • “All of the balls I've pulled out of this box have been red. Thus, all of the balls in this box are probably red.” • It is the basic tool of scientific verification and validation, and the observational tool for scientific discovery. • It is extraordinarily powerful as a heuristic but is fundamenally flawed

  17. THE PROBLEMS WITH INDUCTION • It is invaluable for discovering the rules by which we make diagnoses, but It is useless in the individual diagnosis itself, because aggregate statistics do not apply to individuals, and individual cases are, well, individual. • Most inductions assume away confounders • You Bayesians keep your seats, I'll get to you in a minute. • Ignoring this is that basis for the “prosecutor's fallacy,” the “defense fallacy,” etc. • Hume's problem – the black swan • You don't know what you don't know

  18. PROBLEMS WITH INDUCTION, CONT'D • Because of the issue of confounders, inference is often a matter of gut feeling, albeit guided by statistics.

  19. A TRULY CLASSIC INDUCTION... “But in all my experience, I have never been in any accident... of any sort worth speaking about. I have seen but one vessel in distress in all my years at sea. I never saw a wreck and never have been wrecked nor was I ever in any predicament that threatened to end in disaster of any sort.” EJ Smith, 1907, Captain, RMS Titanic.

  20. DAVID HUMEA TREATISE ON HUMAN NATURE, 1739 Thus all probable reasoning is nothing but a species of sensation. It is not solely in poetry and music, we must follow our taste and sentiment, but also in philosophy. When I am convinced of any principle, it is only an idea, which strikes more strongly on me. When I give the preference to one set of arguments above another, I do nothing but decide from my feelings concerning the superiority of the influence. Objects have no discoverable connection together; nor is from any principle but custom operating upon the imagination, that we can draw any inference from the appearance of one to the existence of the other.

  21. STATISTICAL INFERENCE • Made Hume's gut feeling more tractable • I have taken 100 balls from this box • 90 have been black, 10 have been white • Thus, abour 90 percent of the balls in the box are black • Makes a lot of assumptions • The nature of the balls • The nature of the box • The nature of drawing them from the box

  22. BUT ONCE AGAIN, YOU CAN’T SAY ANYTHING ABOUT INDIVIDUAL CASES. • People have a hard time with this. • If I roll a pair of dice, the average value is 7. • That says nothing about what the next roll will be. • Statistics don't “catch up” with you • Gambler's fallacy • Aggregate statistics don't tell you about individuals • Prosecutor's fallacy • Defender's fallacy

  23. FREQUENTIST PROBLEM • A pathologist is tasked with making a statement about a particular case. • We can't say “I don't know what killed this person, but if one were to look at 100 cases similar to this one, 90 would have died due to hypertensive heart disease.” • Is this one of the 90 or one of the 10? Don't know.

  24. THE BAYESIAN APPROACH • Thomas Bayes, a Presbyterian minister, came up with an idea in 1763 • In spite of the scary notation, it's pretty simple. • The probability of something, given some evidence is the ratio of the probability of the event and evidence happening together (regardless of “cause”) divided by the probability of the evidence being observed in all possible circumstances.

  25. AT IT'S SIMPLEST • Bayesians assign a probability to a hypothesis • I'll talk more about this when I discuss abduction • Frequentists test a hypothesis.

  26. SO LET'S USE A MADE UP CASE • What is the probability that a dead guy with a big heart died of hypertension? • Assume that people can only die of hypertension, diabetes mellitus, or sleep apnea

  27. | = given a P= probability of

  28. SO, LET'S SAY • P(HTN) = 60% • P(SA) = 10% • P(DM) = 30% • P(BH|HTN) = 80% • P(BH|SA) = 10% • P(BH|SA) = 20%

  29. .87+.11+.02 = 1.0 .27+.53+.20 = 1.0 ..87 .27 ..11 ..53 ..02 ..20

  30. But, really, this is just all cases where there's a big heart, or P(BH)

  31. Simplified form

  32. “Prior probability” of C == Probability of C “prior” to knowing if evidence is there or not “Conditional probability == Probability finding the evidence if you know C happened “Prior probability” of E == Probability of finding the evidence before knowing if C occurred “Posterior probability” == Probability of C if you find the evidence

  33. Most of the time, you don’t really know either of these! You bootstrap them from each other You often don’t know this, either

  34. SHAKEN BABY AND RETINAL HEMORRHAGE

  35. EVERYTHING ELSE

  36. BUT THIS REALLY REDUCES TO: BUT... SO...

  37. P(RH) • All of these numbers are disputed, but I'll talk about that later • Let's choose a random article: Agrawal S, Peters MJ, Adams GG, Pierce CM. Prevalence of retinal hemorrhages in critically ill children. Pediatrics. 2012 Jun;129(6) e1388-e1396. Looked at 156 consecutive kids > 6 wks in ED. • 15% overall • 10% mild • 1% moderate • 4% severe • So, P(H) = 0.15 overall, 0.04 severe

  38. PROBABILITY OF FINDING RETINAL HEMORRHAGES IN SBS? • P(H|SB) ? • Let's use Morad Y, Wygnansky-Jaffe T, Levin AV. Clin Experiment Ophthalmol. 2010 Jul;38(5):514-20. Retinal haemorrhage in abusive head trauma. • 85% • Yes, yes, heads are exploding. I just have to have some numbers.

  39. PREVALENCE OF SBS • Oh oh. • Well, what the hell. I'll use the American Assoc Neurologic Surgeons website (www.aans.org/ Information/Conditions and Treatments/Shaken Baby Syndrome.aspx) • 600-1400 cases per year, let's use 900 • There are about 28900 deaths less than 4 yrs in US (www.cdc.gov/nchs/fastats/deaths.html) • So, that gives us 900/28900 = 3% of deaths

  40. P(SBS|H)= P(H|SBS)*P(SBS)/P(H) • = (0.85*0.03)/0.15 • = 17% • What about “severe” retinal hemorrhage? • According to previous article, retinal hemorrhage in about 2/3 SBS are severe. 0.85*0.66=0.56 • = (0.56*0.03)/0.04 • = 42%

  41. HMMM... THINK ABOUT WHAT WE JUST DID

  42. HMMM... THINK ABOUT WHAT WE JUST DID sensitivity

  43. HMMM... THINK ABOUT WHAT WE JUST DID sensitivity prevalence

  44. HMMM... THINK ABOUT WHAT WE JUST DID sensitivity sensitivity*prevalence + (1-specificity)* (1-prevalence) prevalence

  45. OMG! IT'S JUST PPV!LET'S GO TO THE FOUNT OF ALL KNOWLEDGE, WIKIPEDIA

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