Medical knowledge as a probabilistic decision tree

Medical reasoning can sound like a large decision tree ("if this then that"). This post documents the nuances that render a decision tree data structure inadequate. 

• Nuances about input data
• Extracting facts relevant to diagnosis is challenging. 
• A parent of an infant may report their baby is not eating much, but does that mean the baby is refusing food, sleepy, crying, vomiting...
• Looking merely at the current symptoms is inadequate. Patient history matters. Family history can matter.
• distinguishing irrelevant information is important. Temporal correlation does not mean causation.
• There are varying degrees of uncertainty about data quality. For example, I would discount the data from a person who does not tell their story well, and put a greater weight on a lab test. But for someone who tells their story well, the history features may sway more than their lab. 
• How questions get asked matter
• "Does your family have a history of arrhythmia?" will get a "no," but
• "Have people in your family drowned or died in a car accident?" would get a yes because arrhythmia can lead to drowning and accidents.
• "did any babies in the family have hearing problems?"
• Patients don't know the precise medical jargon
• "Does your family have a history heart attacks?" is likely actually a different medical cause when the patient doesn't know the jargon or the specifics about cholesterol.
• Humans are not uniform, and the differences can matter. 
• "male versus female" isn't an easy binary in all cases
• "age" isn't a scalar positive number
• age since birth ("post-gestational age")
• age since conception ("gestational age")
• cognitive ability versus temporal age versus physical dexterity

• Decisions
• A lot of the reasoning is probabilistic rather than definitive. The exact probabilities are also unknown -- one is dealing with relative probabilities. 
• Discarding data should be weighted by the consequence of removing the data
• Causal explanations based on physiology can be used to discard information, or give insight about additional symptoms to ask about. [Writing down all of physiology is a separate task.]
• The differential list should include not only a ranking of most likely diagnoses but also diagnoses that one should not miss. This is one area where some doctors say that doctors are better than nurse practitioners. The nurse practitioner tells the patient the most likely diagnosis, but the doctor knows other diagnoses that are less likely, but should not be missed and counsel accordingly. In other words, just guessing based on a "most likely" diagnosis is insufficient. Are there "do not miss" outcomes that could be explanatory for the diagnosis?
• Suggestions for action by the patient includes, "what to do if this other symptom arises" or "what to do if this symptom persists"

https://en.wikipedia.org/wiki/Expert_system#Disadvantages

The clinical decision analysis using decision tree

doi 10.4178/epih/e2014025

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4251295/