Doctors and technologists of various stripes have been working on this since before the dawn of digital computers. Let's take height as an example. Let's look at metadata needed along with that data point: 1. When was it taken? 2. How was it taken? 3. Who took it? 4. What measurement system does "cm" refer to? 5. How does the concept of height relate to other clinical concepts?
https://www.hl7.org/fhir/observation.html
https://www.hl7.org/fhir/observation-example-body-height.htm...
The simple stuff like BMI, BSA, etc. have been calculated for a long time without anything fancy like an inference engine. The challenge is that the surface area of different calculations needed outstrip the supply of people who can interpret the clinical question, identify the source data and encode them. A better approach is needed and is what folks are working towards.
Re:Medicine's Machine Learning Problem
by QuercusMax on Friday January 08, 2021 @06:02PM
(#25691647)
There's a lot of effort being put forward to extract data from unstructured reports, with some pretty nice results from what I've seen. Convincing every doc to use this type of format is likely to be impossible.
This is my biggest complaint with the EMR systems I've used and I've always wanted to improve this. I wonder if fellow doctors would be okay with using a simple structured language to describe data in an EMR.
For example:
An inference engine could use that data to calculate lots of things. Simple stuff like body mass index and creatinine clearance. The patient could be automatically classified in all possible scores given available data.Doctors already do this work, we even input this exact same data into calculator apps. The innovation would be recognizing this data in the EMR text and doing it automatically. I think it would be a huge gain.