0000000000309779

AUTHOR

Andrea Stopper

showing 5 related works from this author

Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques

2013

HighlightsDifferent prediction algorithms were used to predict Hb levels in CRF patients.Prediction errors in the validation cohorts of patients were around 0.6g/dl.Difficulty to obtain lower errors due to the measuring machine precision (0.2g/dl).Relevance analysis of features have been applied for each predictor. Patients who suffer from chronic renal failure (CRF) tend to suffer from an associated anemia as well. Therefore, it is essential to know the hemoglobin (Hb) levels in these patients. The aim of this paper is to predict the hemoglobin (Hb) value using a database of European hemodialysis patients provided by Fresenius Medical Care (FMC) for improving the treatment of this kind of …

AdultMaleAdolescentmedicine.medical_treatmentHealth InformaticsMachine learningcomputer.software_genreDisease clusterSensitivity and SpecificityHemoglobinsYoung AdultArtificial IntelligenceRenal DialysismedicineHumansComputer SimulationCluster analysisErythropoietinAgedAged 80 and overDose-Response Relationship DrugArtificial neural networkbusiness.industryModels CardiovascularLinear modelReproducibility of ResultsAnemiaMiddle AgedRegressionDrug Therapy Computer-AssistedComputer Science ApplicationsSupport vector machineTreatment OutcomeAdaptive resonance theoryFemaleHemodialysisArtificial intelligenceDrug MonitoringbusinesscomputerAlgorithmsBiomarkersSoftwareComputer Methods and Programs in Biomedicine
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Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients

2016

International audience; Anemia management, based on erythropoiesis stimulating agents (ESA) and iron supplementation, has become an increasingly challenging problem in hemodialysis patients. Maintaining hemodialysis patients within narrow hemoglobin targets, preventing cycling outside target, and reducing ESA dosing to prevent adverse outcomes requires considerable attention from caregivers. Anticipation of the long-term response (i.e. at 3 months) to the ESA/iron therapy would be of fundamental importance for planning a successful treatment strategy. To this end, we developed a predictive model designed to support decision-making regarding anemia management in hemodialysis (HD) patients tr…

MalePediatricsBlood transfusionDarbepoetin alfaPhysiologymedicine.medical_treatment030232 urology & nephrologylcsh:Medicine030204 cardiovascular system & hematologyFerric CompoundsBiochemistryGlucaric AcidHemoglobinsMathematical and Statistical Techniques0302 clinical medicineMedicine and Health SciencesDarbepoetin alfaErythropoiesislcsh:ScienceFerric Oxide SaccharatedMultidisciplinaryPharmaceuticsDisease ManagementAnemia[SDV.MHEP.HEM]Life Sciences [q-bio]/Human health and pathology/HematologyHematologyMiddle Aged3. Good healthNephrologyInjections IntravenousPhysical SciencesFemaleHemodialysisStatistics (Mathematics)Research Articlemedicine.drugComputer and Information Sciencesmedicine.medical_specialtyAnemiaResearch and Analysis Methods03 medical and health sciencesDose Prediction MethodsRenal DialysisArtificial IntelligenceMedical DialysismedicineHumansHemoglobinDosingStatistical MethodsIron Deficiency AnemiaIntensive care medicineArtificial Neural NetworksAgedRetrospective StudiesComputational NeuroscienceModels Statisticalbusiness.industrylcsh:RBiology and Life SciencesComputational BiologyProteinsRetrospective cohort studymedicine.diseaseIron-deficiency anemiaHematinicsKidney Failure ChronicCognitive Sciencelcsh:QNeural Networks ComputerHemoglobinPhysiological ProcessesbusinessMathematicsNeuroscienceForecasting
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Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients

2012

Highlights? FME as dialysis services global provider monitors patient satisfaction in its network. ? A specific questionnaire was developed and administered to the hemodialysis patients. ? To detect residual area of low satisfaction the Self-Organising Map was implemented. ? This method allows identifying niches of dissatisfaction for specific patient groups. Evaluation of patient satisfaction has become an important indicator for assessing health care quality. Fresenius Medical Care (FME) as a global provider of dialysis services through its NephroCare network has a strong interest in monitoring patient satisfaction.The aim of the paper is to test and validate a methodology for detecting a…

Response rate (survey)Service (business)business.industrymedicine.medical_treatmentGeneral Engineeringmedicine.diseaseComputer Science ApplicationsTest (assessment)Identification (information)Patient satisfactionArtificial IntelligencemedicineHemodialysisMedical emergencybusinessDialysisHealth care qualityExpert Systems with Applications
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Optimization of anemia treatment in hemodialysis patients via reinforcement learning

2013

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 (MDP…

MaleFOS: Computer and information sciencesMathematical optimizationDarbepoetin alfaComputer scienceAnemiaComputer Science - Artificial Intelligencemedicine.medical_treatmentMedicine (miscellaneous)Machine Learning (stat.ML)Outcome (game theory)Decision Support TechniquesMachine Learning (cs.LG)Renal DialysisArtificial IntelligenceStatistics - Machine LearningmedicineHumansReinforcement learningDosingAgedProtocol (science)Patient SelectionAnemiaHemoglobin AMiddle Agedmedicine.diseaseMarkov ChainsComputer Science - LearningArtificial Intelligence (cs.AI)Chronic DiseaseHematinicsKidney Failure ChronicFemaleHemodialysisMarkov decision processReinforcement PsychologyAlgorithmsmedicine.drug
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A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dial…

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 respec…

Malemedicine.medical_specialtyAnemiamedicine.medical_treatmentPopulationHealth InformaticsIron supplementMachine learningcomputer.software_genreModels BiologicalEnd stage renal diseaseCohort StudiesMachine LearningRenal DialysismedicineHumansIntensive care medicineeducationDialysiseducation.field_of_studybusiness.industryAnemiamedicine.diseaseAnemia managementComputer Science ApplicationsLarge cohortKidney Failure ChronicFemaleArtificial intelligencebusinesscomputerKidney diseaseComputers in Biology and Medicine
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