One SFL client was a startup trying to detect sleep apnea from audio data. Many cases of sleep apnea go undiagnosed because there is no blood test for it and symptoms only occur while the patient is asleep. If an app on a person’s phone can flag that person as a potential sleep apnea case a commonly underdiagnosed condition can be much more effectively treated and many people can be saved the time of going to costly and time-consuming sleep clinics. This kind of product succeeds or fails on the strength of its ability to correctly and efficiently use its data for its stated purpose, of diagnosing sleep apnea.
Manually extracting information out of a text normally requires many cost-inefficient human hours. Documents can be written in over ten languages which adds a significant translation cost for the company. Real world data is messy and unstructured, and often contains missing values or inconsistencies. All of these aspects pose various problems for a human, but for a machine, they are considerably more manageable.