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.

Related manuscript: Methodological issues in the creation of a diagnosis tool for dysgraphia npj Digital Medicine 2019

When innovations are rapidly sweeping across all fields of human society, we want to leverage them to improve every aspect of our life and the lives around us --- the companion animals. Veterinary records can be extremely valuable for research and public health --- 60-70% of all emerging diagnoses are transmitted from animals to humans. Beyond that, companion animals have been increasingly used to study naturally occurring diseases as they share similar environments to humans and are often representative disease models to recapitulate diseases in humans.

Clinical laboratory tests are one of the key components of electronic health records and contain rich patient phenotypic information. However, there are often multiple similar laboratory tests for the same medical issue, creating a data integration problem when being used for translational research. In our paper, we presented a novel approach that allows one to semantically integrate laboratory tests by mapping their results into Human Phenotype Ontology terms. The transformation generated detailed patient phenotypic profiles that can be used for biomarker screen.

My team at UC Irvine has developed small Band-Aid© like sensors that can continuously monitor both respiration rate and volume. We made these disposable sensors with an inexpensive children’s toy, Shrinky-Dinks. Link to paper: DOI: 10.1038/s41746-019-0083-3

We have seen many examples of digital health innovation failing to gain traction in the real world setting. Often is it because the sensor or app at the heart of it all just wasn't right for the job. Could we approach this in a better way, such that we can enhance the chances of success?