Leveraging ML for Anomaly Detection in Healthcare Data Warehouses (Published)
The rapid emergence of digitalisation leads to unprecedented growth in the generation of the healthcare sector-particularly EHRs and medical equipment data. This extended the way for challenges for integrity in managing data and anomaly detection, including fraudulent transactions, medication errors, and many more system failures. Modern healthcare data poses a challenge to traditional methods of anomaly detection due to high and complex dimensionality. Machine learning provides a strong solution, using algorithms such as Gaussian Mixture Models, One-Class SVM and deep learning algorithms such as Autoencoders, and Recurrent Neural Networks in the detection of anomalies in healthcare data warehouse settings [1]. This study reports how ML can help advance care for patients, enable the validity of the data and reduce costs through real-time monitoring, fraud detection, and early detection of diseases. Applying anomaly detection through ML would most likely bring better operational performance, patient safety, and decision-making in health care for organizations as issues of poor data quality, lack of interpretability of models, and real-time detection would be addressed [2].
Keywords: Operational Efficiency, Patient Safety, anomaly detection, fraud detection healthcare data warehouses, machine learning
Anomalous Note Change Detection of Nknown Monophonic Melodies (Published)
Anomalous note changes in music melodies are a major concern in several automatic music content analysis tasks. When ‘the musically scale un-related notes’ occur in the note sequence, it will provide less pleasant melodies. These ‘uncommon’ notes are the situations which refer as the ‘anomalous notes’ in a particular melody. Identifying and eliminating the effects of these note changes will helpful to enhance the results in automatic melody evaluation, as well as in automatic melody transcription. In order to address the above issue, this paper proposes an approach to detect anomalous notes changes of unknown monophonic melodies. The proposed model is designed with two main phases with the applicable machine learning and signal processing techniques. Within the first phase, melodies are processed to have their pitch estimations. After the pitch estimation, a note event model has employed with the application of Long Short -Term Memory (LSTM) Neural Network for the detection of anomalous note changes. A set of recorded sample melodies are used as the dataset to evaluate the model. The dataset was collected only for the main seven major scales in music. The model was able to detect anomalous note changes with an overall accuracy of 68.2% for the used dataset.
Keywords: anomaly detection, long short term memory (LSTM, note detection, unknown monophonic melodies