Predicting hospitalization costs via clinical notes
Hospitals only know how much a patient costs once they’ve gone home, making it difficult to plan spending efficiently, but predictive algorithms could help estimate costs early for inpatients.
npj Digital Medicine
An online open-access journal dedicated to publishing research in all aspects of digital medicine, including the clinical application and implementation of digital and mobile technologies, virtual healthcare, and novel applications of artificial intelligence and informatics.
Can vital signs help us distinguish between Influeza vs. COVID?
Our study illustrates the potential of ML models for accurately distinguishing the hemodynamic presentations of the two viral infections. This may have utility as a diagnostic tool to aid healthcare workers in triaging patients as the viral infections start co-circulating in the communities.
A collaborative approach that engages the community to develop digital biomarkers
Despite substantial efforts to develop digital biomarkers, benchmarking and validating these algorithms remains difficult. Open, crowd-sourced efforts through DREAM Challenges is one approach to speed the process.
What is the performance of different machine learning algorithms in predicting disease outcome among COVID-19 patients?
During the height of COVID-19 pandemic, clinicians were forced to make difficult treatment decisions, given the large number of patients and limited resources. To help guide treatment strategies, we evaluated the performance of 18 machine learning models in predicting COVID-19 patient outcomes.
Med-BERT: Pre-trained Embedding for Structured EHR
Behind the paper: Rasmy et al: Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction
Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data
Evaluating the efficacy of government interventions used to mitigate the spread of COVID-19 has been challenging. Mobility data from phones can be used as a low-cost and standardised mechanism to observe the change in the aggregate movement of populations in response to interventions stimuli.
The effects of seasons and weather on sleep patterns measured through longitudinal multimodal sensing
Do seasonal variations in day length and temperature influence sleep in a world of climate control and electricity? Our study combined a year of wearable sleep data with meteorological data. We found that seasons, day length, and temperature influence when and how long individuals sleep.
Can Machine Learning Predict Alcohol Levels over the Legal Limit?
By Dr. Kirstin Aschbacher and colleagues.
