The ability to Extract, Transform and Load data to a format that enables data analysis and machine learning is essential to make use of the vast amount of observational data that is nowadays available. F# can be a very efficient tool to achieve these goals.
In a previous post a described an event driven domain design to run a resuscitation protocol. I also mentioned that running this protocol in practice can have some pitfalls. One of these pitfalls is the use of a defibrillator that is required to apply a cardio conversion by delivering a shock. We have to test that this cannot ever occur.
For some time now I have been thinking of writing an app to help with running a resuscitation protocol. The resuscitation protocol as provided by the Dutch Advanced Pediatric Life Support is rather straight forward. Yet to adhere to this protocol in a stressful situation for a patient with a life threatening condition is a challenge to many physicians or health care providers.
The first hit you get when looking for the answer how ‘medical decision support’ is defined points to clinical decision support systems. The definition for wich is formulated as (attributed to Robert Hayward of the Centre for Health Evidence ):
Clinical decision support systems link health observations with health knowledge to influence health choices by clinicians for improved health care
The next logical step is to look at the topic of artificial intelligence in medicine.
However, in clinical practice there are more immediate and practical needs to have support for simpel calculation and lookup actions.