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Isn't the low positive rate kind of an assumption though, because of currently limited testing? If this is much more widespread than we thought, will this mecha
by tych0 7y ago
Isn't the low positive rate kind of an assumption though, because of currently limited testing? If this is much more widespread than we thought, will this mechanism fall over?
- pwoolf 7y agoThe low positive rate is a constraint, but in practice we see that population screens are yielding between 0.5% and 4% positive rate, depending on the sampling population/scenario. There are many use cases where we expect a low positive rate too. For example, an employer screening what appear to be healthy employees. A nice thing about these designs is that if they get overloaded, they call false positives and the decoder can indicate when the design limits are exceeded. In this case, we would need to do a second round of testing for validation--often on a small handful of cases.
- tych0 7y agoOk, basically a Bloom Filter for test results. Pretty cool :)
- pwoolf 7y agoYes, much like a Bloom filter, but instead of 2, it gives 3 output types: 1) not in set 2) possibly in set 3) in set Depending on the input (sample population), it is possible to get results with all 3 states. Origami Assay's decoder differentiates the "(3) in set" from "(2) possibly in set" for efficient post-testing.