Natural Language Processing (NLP) for Medication Adherence: Complex Semantics and Negation [Methods Study], United States, 2015-2022 (ICPSR 39736)
Clinical notes in electronic health records, or EHRs, can help researchers study treatments. For example, EHR notes may contain information about whether patients take their medicines as directed. But it takes researchers a lot of time to find this information.
Natural language processing, or NLP, methods can help researchers find information in EHR notes. With NLP, computer programs read and identify written language to make it easier to sort and study. But current NLP methods don't work well to find and label text about medicine use.
In this study, the research team created and tested a new NLP method to find and label EHR notes on patients' medicine use.
Validating and Generalizing Personalized Treatment Rules by Leveraging Different Data Sources [Methods Study], United States, 2019-2022 (ICPSR 39735)
Researchers can use data on patient traits such as age, health problems, and treatment preferences, to create personalized treatment rules, or PTRs. PTRs provide doctors with guidance on how to treat patients' health problems based on their traits. But PTRs based on a single data source may not apply to all patients. For example, if researchers create a PTR using data from older people with heart failure, it may not apply to younger people with heart failure.
To avoid this problem, researchers can create PTRs by combining data from many sources. PTRs based on many data sources can help guide treatment for patients with different traits.
In this study, the research team created and tested a new method for creating PTRs using data from multiple sources.