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To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.

Predictive analytics in health care has generated increasing enthusiasm recently, as reflected in a rapidly growing body of predictive models reported in literature and in real-time embedded models using electronic health record data. However, estimating the benefit of applying any single model to a specific clinical problem remains challenging today. Developing a shared framework for estimating model value is therefore critical to facilitate the effective, safe, and sustainable use of [...]

To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports.

Traditional Chinese Medicine (TCM) has been developed for several thousand years and plays a significant role in health care for Chinese people. This paper studies the problem of classifying TCM clinical records into 5 main disease categories in TCM. We explored a number of state-of-the-art deep learning models and found that the recent Bidirectional Encoder Representations from Transformers can achieve better results than other deep learning models and other state-of-the-art [...]

The study sought to evaluate how availability of different types of health records data affect the accuracy of machine learning models predicting suicidal behavior.

Physician burnout associated with EHRs is a major concern in health care. A comprehensive assessment of differences among physicians in the areas of EHR performance, efficiency, and satisfaction has not been conducted. The study sought to study relationships among physicians' performance, efficiency, perceived workload, satisfaction, and usability in using the electronic health record (EHR) with comparisons by age, gender, professional role, and years of experience with the EHR.

There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps [...]

Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names suffer from low recall due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them.