Models vs. Experts #9: Diagnosing Acute Abdominal Pain

Models vs. Experts #9: Diagnosing Acute Abdominal Pain

May 28, 2013 Behavioral Finance
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(Last Updated On: January 18, 2017)

Computer-aided diagnosis of acute abdominal pain

  • De Dombal, F. T., Leaper, D. J., Staniland, J. R., McCann, A. P., & Horrocks, J. C.
  • British Medical Journal, 2, 9-13
  • An online version of the paper can be found here
  • Want a summary of academic papers with alpha? Check out our free Academic Alpha Database!

Abstract:

This paper reports a controlled prospective unselected real-time comparison of human and computer-aided diagnosis in a series of 304 patients suffering from abdominal pain of acute onset. The computing system’s overall diagnostic accuracy (91·8%) was significantly higher than that of the most senior member of the clinical team to see each case (79·6%). It is suggested as a result of these studies that the provision of such a system to aid the clinician is both feasible in a real-time clinical setting, and likely to be of practical value, albeit in a small percentage of cases.

Prediction:

The authors examine patients with acute abdnominal pain admitted into the professorial surgical unit in the General Infirmary at Leeds.

Here is how the test works

  1. Tabulate diagnosis from the clinical team, the house surgeon’s diagnosis, and any senior members of the team who saw the patient. (“human” estimates and analysis)
  2. Case history were entered into a computing system to produce a real-time diagnosis.
  3. Compare diagnosis across humans and the computer.

Here are the categories considered (many of which are abbreviated in subsequent tables):

De_Dombal_etal_1972

Alpha Highlight:

First, a look at how the computer stacks up against the “experts,” aka, the senior clinician to the see the case:

com

Ouch. Computers trounce the top brains.

Thoughts on the paper?


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Definitions of common statistics used in our analysis are available here (towards the bottom)




About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.


  • DrCesarG

    Objective data analysis (usually) trumps heuristics. Similar papers exist in many subspecialties of medicine (dermatology and melafind, for example), and as much as doctors don’t want it, some of what they do will be replaced by technology.

  • I think the comment: “and as much as doctors don’t want it, some of what they do will be replaced by technology” is fascinating, because you could replace the word “doctors” with “financial professionals” and the sentences are both true.

    Usually is a key word–models don’t always win, but there seems to be an overwhelming status quo understanding that experts routinely beat models. I’m highlighting over 200 studies (trying to do 3 or 4 a week) to show the stacks upon stacks of research suggesting that models can beat experts, or at least perform roughly the same. In the end, maybe there will be a more even-handed understanding that models are not the end-all-be-all, but might serve as a viable alternative to overpriced “experts.”