Interactive exploration of population scale pharmacoepidemiology datasets

Population-scale drug prescription data linked with adverse drug reaction (ADR) data supports the fitting of models large enough to detect drug use and ADR patterns that are not detectable using traditional methods on smaller datasets. However, detecting ADR patterns in large datasets requires tools for scalable data processing, machine learning for data analysis, and interactive visualization. To our knowledge no existing pharmacoepidemiology tool supports all three requirements. We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples. We use Spark to preprocess the data for machine learning and for analyses using SQL queries. We have implemented models in Keras and the scikit-learn framework. The model results are visualized and interpreted using live Python coding in Jupyter. We apply our tool to explore a 384 million prescription data set from the Norwegian Prescription Database combined with a 62 million prescriptions for elders that were hospitalized. We preprocess the data in two minutes, train models in seconds, and plot the results in milliseconds. Our results show the power of combining computational power, short computation times, and ease of use for analysis of population scale pharmacoepidemiology datasets. The code is open source and available at: https://github.com/uit-hdl/norpd_prescription_analyse

Analysis and study of hospital communication via social media from the patient perspective

Currently, the online interaction between citizens and hospitals is poor, as users believe that there are shortcomings that could be improved. This study analyzes patients’ opinions of the online communication strategies of hospitals in Spain. Therefore, a mixed-method is proposed. Firstly, a qualitative analysis through a focus-group was carried out, so around twenty representatives of national, regional and local patients’ associations were brought together. Secondly, the research is supplemented with a content assessment of the Twitter activity of the most influential hospitals in Spain. The results reveal that the general public appreciate hospitals’ communication potential through social media, although they are generally unaware of how it works. The group says that, apart from the lack of interaction, they find it hard to understand certain messages, and some publications give a biased picture. In order to improve communication, patients and relatives are demanding that their perspective be taken into consideration in the messages issued to enhance the quality of life and well-being of society

Emerging methods in therapeutics using multifunctional nanoparticles

Clinical translation of nanoparticle‐based drug delivery systems is hindered by an array of challenges including poor circulation time and limited targeting. Novel approaches including designing multifunctional particles, cell‐mediated delivery systems, and fabrications of protein‐based nanoparticles have gained attention to provide new perspectives to current drug delivery obstacles in the interdisciplinary field of nanomedicine. Collectively, these nanoparticle devices are currently being investigated for applications spanning from drug delivery and cancer therapy to medical imaging and immunotherapy. Here, we review the current state of the field, highlight opportunities, identify challenges, and present the future directions of the next generation of multifunctional nanoparticle drug delivery platforms.This article is categorized under:Biology‐Inspired Nanomaterials > Protein and Virus‐Based StructuresNanotechnology Approaches to Biology > Nanoscale Systems in BiologyNovel approaches in designing nanoparticles to overcome challenges faced by traditional nanoparticle‐based drug delivery systems.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155963/1/wnan1625.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155963/2/wnan1625_am.pd