Clinical pharmacists have unique opportunities to be more involved in prescriptome analytics to expand research horizon in clinical pharmacy as an academic discipline. The development of predictive analytics with machine learning algorithms could have the potential to redesign the way we care for patients in our institutions for a more personalized medication therapy.
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Prescriptome analytics: an opportunity for clinical pharmacy.
Int J Clin Pharm
December 2019
Pôle Pharmacie, Service Hospitalo-Universitaire de Pharmacie, CHU de Rennes, 35033, Rennes, France.
Clinical pharmacists have unique opportunities to be more involved in prescriptome analytics to expand research horizon in clinical pharmacy as an academic discipline. The development of predictive analytics with machine learning algorithms could have the potential to redesign the way we care for patients in our institutions for a more personalized medication therapy.
View Article and Find Full Text PDFPharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining.
BMC Med Inform Decis Mak
September 2018
Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.
Background: Worldwide, 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.
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