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- W2891544197 abstract "Current drug design and medical treatment guidelines are defined by generic procedures on which each patient that suffers from a disease is treated equally and without any distinction based on any previous genetic/epigenetic study. Most of the time, this generic treatment approach is prone to failure, due to each individual's distinctive genetic characteristics that block the activation pathway of a treatment drug. As a consequence, general treatment guides increases a patient's disease remission time, reducing their quality of life and increasing the financial treatment costs for all parties involved. Until now, the effectiveness of a drug on a specific patient is evaluated based on trial and error over a sequence of periodical medical evaluations that include several clinical tests that are logged on each patient's medical record; this information is used to select suitable drug guidelines until the most effective one is found. This process occurs for every patient that suffers a disease, specially on rheumatic diseases like Arthritis. To tackle this problem, we propose a medical information system (TiMed) that takes as input a collection of medical records and generalizes the temporal sequence effective variables that determine the most suitable drugs for Arthritis patients, using a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) units, achieving a 97.4% accuracy over a 150 real data input dataset." @default.
- W2891544197 created "2018-09-27" @default.
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- W2891544197 date "2018-05-01" @default.
- W2891544197 modified "2023-09-27" @default.
- W2891544197 title "Medication adherence improvement on Rheumatoid Arthritis patients based on past medical records" @default.
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- W2891544197 doi "https://doi.org/10.1109/sib.2018.8467751" @default.
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