Predictive Patient Care: Survival Model to Prevent Medication Non-adherence

Janssoone, Thomas; Rinder, Pierre; Hornus, Pierre; Kanoun, Dorra

doi:10.1007/978-3-030-00320-3_6

Cited by 1 publication

(1 citation statement)

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“…A survival analysis, described in Janssoone et al [2018], and a statistical analysis of the pharmacy transactions show that illegitimate drop out rate evolves over time and according to previous events in the patient's care path. This led to a more patient-centered study focusing on all transactions carried out by a patient: every purchase in a pharmacy, every hospitalization,.…”

Section: Drug Transaction Studymentioning

confidence: 99%

Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence

Janssoone¹,

Bic²,

Kanoun³

et al. 2018

Preprint

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Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers" [Osterberg and Blaschke, 2005]. World Health Organization's reports 2 point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. This severely compromises the efficiency of long-term therapy and increases the cost of health services. We propose in this paper different models of patient drug consumption in breast cancer treatments. The aim of these different approaches is to predict medication non-adherence while giving insights to doctors of the underlying reasons of these illegitimate drop-outs. Working with oncologists, we show the interest of Machine-Learning algorithms fined tune by the feedback of experts to estimate a risk score of a patient's non-adherence and thus improve support throughout their care path.

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Section: Drug Transaction Studymentioning

confidence: 99%