Resuming Work After Cancer: A Prospective Study of Occupational Register Data

Introduction Long-term employment rates have been studied in cancer survivors, but little is known about the return to work of cancer patients. This study investigated return to work (RTW) within 2 years after the diagnosis of different types of cancer. Methods This prospective study investigated the associations of demographics (age, gender, socioeconomic status, and residential region) and occupational factors (occupation, duration of employment, and company size) of employees absent from work due to cancer with the time to partial RTW, defined as working at least 50% of the earnings before sickness absence. Likewise, the associations of demographics and occupational factors with full RTW at equal earnings as before sickness absence were investigated. Results The cohort included 5,234 employees who had been absent from work due to cancer between January 2004 and December 2006. The time to partial RTW was shortest among employees with skin cancer (median 55 days) and longest among employees with lung cancer (median 377 days). There were no significant associations between RTW and demographics. With regard to the occupational factors, employees in high occupational classes started working earlier than those in low occupational classes, but the time to full RTW did not differ significantly across occupational classes. Employees working in large companies returned to work earlier than those working in small companies. Conclusion RTW after different types of cancer depended on occupational factors rather than demographics

Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach

In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and caregivers, but may also lead to longer response times in real critical situations. The aim of this project was to reduce the rates of false alarms by employing machine learning algorithms (MLA), which intelligently analyze data stemming from standard physiological monitoring in combination with cerebral oximetry data (in-house built, OxyPrem). MATERIALS & METHODS Four popular MLAs were selected to categorize the alarms as false or real: (i) decision tree (DT), (ii) 5-nearest neighbors (5-NN), (iii) naïve Bayes (NB) and (iv) support vector machine (SVM). We acquired and processed monitoring data (median duration (SD): 54.6 (± 6.9) min) of 14 preterm infants (gestational age: 26 6/7 (± 2 5/7) weeks). A hybrid method of filter and wrapper feature selection generated the candidate subset for training these four MLAs. RESULTS A high specificity of >99% was achieved by all four approaches. DT showed the highest sensitivity (87%). The cerebral oximetry data improved the classification accuracy. DISCUSSION & CONCLUSION Despite a (as yet) low amount of data for training, the four MLAs achieved an excellent specificity and a promising sensitivity. Presently, the current sensitivity is insufficient since, in the NICU, it is crucial that no real alarms are missed. This will most likely be improved by including more subjects and data in the training of the MLAs, which makes pursuing this approach worthwhile