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[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?

A t1, t2,t3,<missing>,t5,t6
B t1, t2,t3,t4,t5,t6
C t1, t2,t3,X,t4,<missing>,t6

If I see these time series, I would say that it is patient is struggling with problem X

P.S: I have > hundered measurements.

Suggested approach

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.

submitted by /u/__Julia
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.