Anomaly Detection Methodologies Research Proposal

Anomaly Detection Methodologies - Research Proposal Example Besides, current practices and procedures aimed at identifying such patients are slow, expensive and unsuitable for incorporating new analytical mechanisms. Buckeridge (2007) argues that Current algorithms used for achieving this risk stratification are dependent on the labelling of the patient data as positive or negative. This classification implies that determining trends and subsets that are rare in a given population requires an analysis of large data sets and the identification of positive aspects up to a threshold level. This process, as explained above, is not just slow or expensive, but puts additional burden on patients and hospital administrators, thereby affecting the validity and effectiveness of such practices. The proposed study aims to use appropriate anomaly detection methods that are known to be suitable for detecting interesting or unusual patterns in a given data set. Bohmer (2009) says that new frameworks allow anomaly detection to be applied towards determining anomalous patterns in subsets of attributes associated with a data set. In simpler words, anomaly detection methods identify unusual occurrences with the data that appear to deviate from the normal behaviour exhibited by a majority of the data set. Examples of such anomalies include an epidemic outbreak, traffic congestion in a certain section of roads or an attack on a network (Applegate, 2009). The proposed research aims to extend the standard approach to anomaly detection by devising techniques to identify partial patterns that exhibit anomalous behaviour with the remainder of the data set. Such techniques are believed to aid in the detection and assessment of unusual outcomes or decisions related to patient management in healthcare institutions. Anomaly Detection Several studies by researchers like Nurcan (2009) and Anderson (2007) have applied anomaly detection techniques to healthcare. In fact, anomaly detection has proved useful in areas under clinical behaviour and medical t echnology such as blood samples, vestibular information, mammograms and electroencephalographic signals (Brandt, 2007). However, the same principles have found little application in enhancing the quality of patient care or identifying existing deficiencies in the assistance extended to patients. The proposed study aims to improve and extend anomaly detection techniques to such relatively unexplored domains. While previous studies have relied primarily on detecting existing conditions such as diseases, the proposed research will apply similar methods to ascertain the level of risk that accompanies a potential outcome being analyzed. Thus, the measurement of this risk as a result of uncovering anomalies is likely to help in forecasting the vulnerability of patients to certain diseases or deficiencies. The study proposed to utilize several anomaly detection methods by applying them to existing clinical data on patients. In doing so, the number of outcomes and patients being analyzed wi ll be much larger and wider than those adopted by previous studies. Some of the detection methods that will be included as part of the proposed study are listed below: Nearest Neighbour method As the name suggests, the nearest neighbour method helps detect patients (anomalies) from a given population based on information pertaining to their ‘n’ nearest neighbours. This method is based on the principle of vectors that are used to sum the distances between a point and it ‘n’ closes neighbours. As a result, dense and sparse regions are identified based on the total score which is lesser in the former case