Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits

arXiv.org Machine Learning

Pediatric asthma is the most prevalent chronic childhood illness, afflicting about 6.2 million children in the United States. However, asthma could be better managed by identifying and avoiding triggers, educating about medications and proper disease management strategies. This research utilizes deep learning methodologies to predict asthma-related emergency department (ED) visit within 3 months using Medicaid claims data. We compare prediction results against traditional statistical classification model - penalized Lasso logistic regression, which we trained and have deployed since 2015. The results have indicated that deep learning model Artificial Neural Networks (ANN) slightly outperforms (with AUC = 0.845) the Lasso logistic regression (with AUC = 0.842). The reason may come from the nonlinear nature of ANN.


Artificial intelligence in emergency medicine - Liu - Journal of Emergency and Critical Care Medicine

#artificialintelligence

Artificial intelligence (AI) in medicine has a long history (1). AI has been an active subfield of computer science for more than 60 years, while medicine is even a much older field, which can trace back to thousands of years. Researchers from both AI and medicine communities have been interacting to create novel solutions for better patient care and enabling more efficient healthcare systems (2,3). Collaborations between both communities were either technology-driven or problem-driven. In technology-driven research, innovations are mainly the development and validation of new AI algorithms for selected clinical problems where the algorithms are generic and not necessary to be optimal in solving real-world problems.


Death and data science: How machine learning can impact hospice referrals, improve last days of life

ZDNet

KenSci, a company that has developed a machine learning risk prediction platform for healthcare, recently presented a paper on predicting end-of-life mortality and improving care. The paper, which tackles a tricky topic with predictions for the last six to 12 months of life for patients, was accepted by the Association for the Advancement of Artificial Intelligence. At stake is $205 billion in cost spent on care for the last year of an individual's life. As part of our ongoing series on data scientists and their approaches, we caught up with Ankur Teredesai, CTO of KenSci and one of the authors of the paper, which was recognized in the emerging technologies category. What data sets did you use to model?


Death and data science: How machine learning can improve end-of-life care ZDNet

#artificialintelligence

KenSci, a company that has developed a machine learning risk prediction platform for healthcare, recently presented a paper on predicting end-of-life mortality and improving care. The paper, which tackles a tricky topic with predictions for the last six to 12 months of life for patients, was accepted by the Association for the Advancement of Artificial Intelligence. At stake is $205 billion in cost spent on care for the last year of an individual's life. As part of our ongoing series on data scientists and their approaches, we caught up with Ankur Teredesai, CTO of KenSci and one of the authors of the paper, which was recognized in the emerging technologies category. What data sets did you use to model?


Heart Rate Topic Models

AAAI Conferences

A key challenge in reducing the burden of cardiovascular disease is matching patients to treatments that are most appropriate for them. Different cardiac assessment tools have been developed to address this goal. Recent research has focused on heart rate motifs, i.e., short-term heart rate sequences that are over- or under-represented in long-term electrocardiogram (ECG) recordings of patients experiencing cardiovascular outcomes, which provide novel and valuable information for risk stratification. However, this approach can leverage only a small number of motifs for prediction and results in difficult to interpret models. We address these limitations by identifying latent structure in the large numbers of motifs found in long-term ECG recordings. In particular, we explore the application of topic models to heart rate time series to identify functional sets of heart rate sequences and to concisely describe patients using task-independent features for various cardiovascular outcomes. We evaluate the approach on a large collection of real-world ECG data, and investigate the performance of topic mixture features for the prediction of cardiovascular mortality. The topics provided an interpretable representation of the recordings and maintained valuable information for clinical assessment when compared with motif frequencies, even after accounting for commonly used clinical risk scores.