A Knowledge-Based Approach to Automatic Detection of Equipment Alarm Sounds in a Neonatal Intensive Care Unit Environment.

A large number of alarm sounds triggered by biomedical equipment occur frequently in the noisy environment of a neonatal intensive care unit (NICU) and play a key role in providing healthcare. In this paper, our work on the development of an automatic system for detection of acoustic alarms in that difficult environment is presented. Such automatic detection system is needed for the investigation of how a preterm infant reacts to auditory stimuli of the NICU environment and for an improved real-time patient monitoring. The approach presented in this paper consists of using the available knowledge about each alarm class in the design of the detection system. The information about the frequency structure is used in the feature extraction stage, and the time structure knowledge is incorporated at the post-processing stage. Several alternative methods are compared for feature extraction, modeling, and post-processing. The detection performance is evaluated with real data recorded in the NICU of the hospital, and by using both frame-level and period-level metrics. The experimental results show that the inclusion of both spectral and temporal information allows to improve the baseline detection performance by more than 60%.

Cancer risk analysis in families with hereditary nonpolyposis colorectal cancer.

Colorectal cancer (CRC) is one of the most common fatal cancers in developed countries and represents a significant public-health issue. About 3%-5% of patients with CRC have hereditary nonpolyposis colorectal cancer (HNPCC). Cancer morbidity and mortality can be reduced if early and intensive screening is pursued. However, despite advances in screening, population-wide genetic screening for HNPCC is not currently considered feasible due to its complexity and expense. If the risk of a family having HNPCC can be identified/assessed, then only the high-risk fraction of the population would undergo intensive screening. This identification is currently performed by a genetic counselor/physician who makes the decision based on some pre-defined criteria. Here, we report on a system to identify the risk of a family having HNPCC based on its history. We compare artificial neural networks and statistical approaches for assessing the risk of a family having HNPCC and discuss the experimental results obtained by these two approaches.

A comparison of multi-layer neural network and logistic regression in hereditary non-polyposis colorectal cancer risk assessment.

Hereditary non-polyposis colorectal cancer (HN-PCC) is one of the most common autosomal dominant diseases in developed countries. Here, we report on a system to identify the risk of a family having HNPCC based on its history. This is important since population-wide genetic screening for HNPCC is not currently considered feasible due to its complexity and expense. If the risk of a family having HNPCC can be identified/asessed, then only the high risk fraction of the population would undergo intensive screening. Here, we have developed a Multi-Layer Feed-Forward Neural Network to classify families into high-, intermediate- and low-risk categories and compared the result with the benchmark logistic regression model.