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Bad Homburg vor der Höhe, Germany

Escandell-Montero P.,University of Valencia | Chermisi M.,Healthcare and Business Advanced Modeling | Martinez-Martinez J.M.,University of Valencia | Gomez-Sanchis J.,University of Valencia | And 8 more authors.
Artificial Intelligence in Medicine | Year: 2014

Objective: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. Methods: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. Results: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. Conclusion: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols. © 2014 Elsevier B.V. Source


Cattinelli I.,Healthcare and Business Advanced Modeling | Bolzoni E.,Healthcare and Business Advanced Modeling | Chermisi M.,Healthcare and Business Advanced Modeling | Bellocchio F.,Healthcare and Business Advanced Modeling | And 7 more authors.
Artificial Intelligence in Medicine | Year: 2013

Objectives: The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it In this work, additional methods are presented to analyze the evolution of clinic performance over time Methods: Performance evolution is studied at the single-clinic level by computing two complementary indexes that measure the proportion of time spent within performance clusters and improving/worsening trends Self-organizing maps are used in conjunction with these indexes to identify the specific drivers of the observed performance The performance evolution for groups of clinics is modeled under a probabilistic framework by resorting to Markov chain properties These allow a study of the probability of transitioning between performance clusters as time progresses for the identification of the performance level that is expected to become dominant over time Results: We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely to improve from those where a decline in performance might be expected According to the stationary distribution of the Markov chain, the expected trend is best in Turkey (where the highest performance cluster has the highest probability, P= 0.46), followed by Portugal (where the second best performance cluster dominates, with P= 0.50), and finally Italy (where the second best performance cluster has P= 0.34) Conclusion: These results highlight the ability of the proposed methods to extract insights about performance trends that cannot be easily extrapolated using standard analyses and that are valuable in directing management strategies within a continuous quality improvement policy © 2013 Elsevier B.V. Source


Barbieri C.,Healthcare and Business Advanced Modeling | Mari F.,Healthcare and Business Advanced Modeling | Stopper A.,Healthcare and Business Advanced Modeling | Gatti E.,Healthcare and Business Advanced Modeling | And 4 more authors.
Computers in Biology and Medicine | Year: 2015

Chronic Kidney Disease (CKD) anemia is one of the main common comorbidities in patients undergoing End Stage Renal Disease (ESRD). Iron supplement and especially Erythropoiesis Stimulating Agents (ESA) have become the treatment of choice for that anemia. However, it is very complicated to find an adequate treatment for every patient in each particular situation since dosage guidelines are based on average behaviors, and thus, they do not take into account the particular response to those drugs by different patients, although that response may vary enormously from one patient to another and even for the same patient in different stages of the anemia. This work proposes an advance with respect to previous works that have faced this problem using different methodologies (Machine Learning (ML), among others), since the diversity of the CKD population has been explicitly taken into account in order to produce a general and reliable model for the prediction of ESA/Iron therapy response. Furthermore, the ML model makes use of both human physiology and drug pharmacology to produce a model that outperforms previous approaches, yielding Mean Absolute Errors (MAE) of the Hemoglobin (Hb) prediction around or lower than 0.6. g/dl in the three countries analyzed in the study, namely, Spain, Italy and Portugal. © 2015 Elsevier Ltd. Source

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