[D] How do you use unsupervised Learning methods with time-series data?
I have a question about a problem that I am trying to solve.
I have clinical data (time-series measurements), and I aim to understand patients’ problems. Every measurement is reporting data in slightly different way depends on the behavior of the patient / equipement used to monitor patient.
This later presents three challenges:
1/ missing data for some measurements for some time.
2/ normalization problem. we don’t know have a clear idea on min/max of medical values (I assume it is hardly predictable in some cases).
3/ Since labeling such data is very costly. I can get some labeled data but it would be really a small subset.
What do I have?
For the sake of an example, let’s say that I have three measurements (measurement A, measurement B, measurement C).
I have time series of measurement A, B, C for healthy patients (they recovered and they are staying in hospital for few days), and I have time series of measurement A, B, C for patients who struggle with some problems.
I only know that information. The idea is to categorize patient problems over time and use it in other places where some specialized doctors lack expertise to identify problems. How can I approach this?
If I see these time series, I would say that it is patient is struggling with problem X
P.S: I have > hundered measurements.
Since the three measurements don’t report data in the same time window, I averaged on time window T. I focused only on time series of sick patients. I tried a naive approach of apply clustering with temporal constraints. Since it;s a naive approach to the problem, I started looking/exploring other methods.
Questions: 1/ How can I leverage measurements of healthy patients (use it as a guide) and the little labeled data I have 2/ what are some of the methods that I can use for unsupervised learning to tag/cluster problems (doctor will later identify them)?
I am seeking advises/recommendations on methods to explore. Do you have any suggestions, ideas and papers to explore. I would be thankful.