Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

  title={Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining},
  author={Khader Shameer and M. Mercedes Perez-Rodriguez and Roy Bachar and Li Li and Amy Johnson and Kipp W. Johnson and Benjamin Scott Glicksberg and Milo R. Smith and Ben Readhead and Joseph R. Scarpa and Jebakumar Jebakaran and Patricia A. Kovatch and Sabina Lim and Wayne K. Goodman and David L. Reich and Andrew Kasarskis and Nicholas P. Tatonetti and Joel T. Dudley},
  journal={BMC Medical Informatics and Decision Making},
BackgroundWorldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study… 

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    2019 2nd International Conference on Bioinformatics, Biotechnology and Biomedical Engineering (BioMIC) - Bioinformatics and Biomedical Engineering
  • 2019
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