Anomalies are defined as patterns in data that do not conform to expected or normal behavior. The finding of such patterns is often referred to as anomaly detection. Different anomaly detection techniques may be applied depending on the nature of the data. Usually if fully labelled data is available, supervised anomaly detection may be adopted. Data sets are considered as labelled if both the normal and anomalous data points have been recorded. When labels are not recorded or available, the only option is an unsupervised anomaly detection approach.
Anomaly detection is all about finding patterns of interest (outlier’s, exceptions, peculiarities, etc.) that deviate from expected behavior within data. Anomaly detection can be used for a host of medical use cases, such as sepsis prevention, hospital bed allocation optimization, and preliminary radiology and dermatology screenings. Yet fraud detection remains a terrific anomaly detection project for the healthcare sector because it doesn’t influence the medical care directly, and can help improve clinician trust.« Back to Glossary Index