Clinical NLP is critical to a successful learning healthcare system, but development and deployment of such systems are no mean feats. We invite you to join us in addressing some common barriers and paving the groundwork for sustainable and robust clinical NLP development and usage.
Digital technologies, such as artificial intelligence, big data or mobile applications, can improve medicine and healthcare. But why is the adoption of these technologies in clinical practice still so slow? One reason is the lack of interoperable data.
For many years, sleep-wake scoring algorithms were created and validated with small and private datasets with no more than one hundred participants. In this paper, we devised the largest dataset up to date for the sleep-wake classification problem and analyzed the performance of popular traditional algorithms as well as state-of-the-art machine learning techniques to tackle this problem. By making this dataset public, researchers can use our data and results as a benchmark to develop newer algorithms.
Alarm fatigue continues to be one of the most important problems in health care. We describe our SuperAlarm framework, a strategy to not only solve the problem of alarm fatigue, but also enhance the utility of hospital monitoring systems.