I have 30,000 patient visits and I am looking to find the weight change from the first to the last visit but I do not know how to remove patients with visits that a weight was not entered so it appears as a zero and the analysis reports that they lost all of their body weight. I am brand new at knime so this may seem like a simple question, but I have no idea how to manage it! Thank you in advance!
I also have to remove patients with only one visit.
Hi Karen I used the Row Filter and set the "use range checking" lower bound and upper bound both to zero.
I am attaching an example workflow
Thank you! I was not able to see or Import the examples as I have a Mac and I have been having difficulty Importing examples. If you can take a screenshot that would probably work better.
I am also
trying to remove patients who have had only one visit so there would be only one uniqueID, the patients with multiple visits have multiple uniqueID's...any thoughts?
Hi Karen I uploaded a Power Point presentation in Google Drive which you can find in this
I am also uploading the workflow in other format
Let me know if it is ok
Hi, I have installed KNIME 3.2, but I don't know how to download installation guide to see it.
"Double click" is not a way, Drag&Drop too as is written here: https://tech.knime.org/whats-new-in-knime-32#KNIMEFileExtension. Do anybody know what i do wrong?
If the weight was not entered, I assume there is a missing value in the corresponding field ? If yes, you can use the `Missing Value` node to filter them out.
Patients having only one visit (assuming granularity = 1 row per visit per patient):
`GroupBy(patient id) to count the number of visits -> Joiner on patient id with the original table -> Row Filter excluding occurrence of 1 in the number of visits`
There's nothing wrong kicking out missing value observations, however, you may have other non-missing variables of interest for such visits. In that case it may be reasonable to assume that the weight compared to the last visit did not change (whispering: significantly), thus impute it using the previous visit of the same patient when available and mark the observation as imputed:
`Sorter (patient id, date) -> Group Loop Start (on patient id) -> Lag (weight) -> Loop End -> Rule Engine to mark the to be soon imputed values -> Rule Engine to impute weight`
As a bonus, the analysis will be able to report which (measurable) circumstances have lead to the weight of the patients not being recorded.