Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.

Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to…

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

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 …

Examining the N=28 shell closure through high-precision mass measurements of Ar46–48

The strength of the $N=28$ magic number in neutron-rich argon isotopes is examined through high-precision mass measurements of $^{46\text{--}48}\mathrm{Ar}$, performed with the ISOLTRAP mass spectrometer at ISOLDE/CERN. The new mass values are up to 90 times more precise than previous measurements. While they suggest the persistence of the $N=28$ shell closure for argon, we show that this conclusion has to be nuanced in light of the wealth of spectroscopic data and theoretical investigations performed with the SDPF-U phenomenological shell model interaction. Our results are also compared with ab initio calculations using the valence space in-medium similarity renormalization group and the s…

Anemia management in end stage renal disease patients undergoing dialysis: a comprehensive approach through machine learning techniques and mathematical modeling

Kidney impairment has global consequences in the organism homeostasis and a disorder like Chronic Kidney Disease (CKD) might eventually exacerbates into End Stage Renal Disease (ESRD) where a complete renal replacement therapy like dialysis is necessary. Dialysis partially reintegrates the blood ltration process; however, even when it is associated to a pharmacological therapy, this is not su fficient to completely replace the renal endocrine role and causes the development of common complications, like CKD secondary anemia (CKD-anemia) The availability of exogenous Erythropoiesis Stimulating Agents (ESA, synthetic molecules with similar structure and same mechanism of action as human eryth…

Model nuclear energy density functionals derived from ab initio calculations

We present the first application of a new approach, proposed in [Journal of Physics G: Nuclear and Particle Physics, 43, 04LT01 (2016)] to derive coupling constants of the Skyrme energy density functional (EDF) from ab initio Hamiltonian. By perturbing the ab initio Hamiltonian with several functional generators defining the Skyrme EDF, we create a set of metadata that is then used to constrain the coupling constants of the functional. We use statistical analysis to obtain such an ab initio-equivalent Skyrme EDF. We find that the resulting functional describes properties of atomic nuclei and infinite nuclear matter quite poorly. This may point out to the necessity of building up the ab init…

Preface to Data Mining in Biomedical Informatics and Healthcare

Dawning of the N=32 shell closure seen through precision mass measurements of neutron-rich titanium isotopes

A precision mass investigation of the neutron-rich titanium isotopes 51 − 55 Ti was performed at TRIUMF’s Ion Trap for Atomic and Nuclear science (TITAN). The range of the measurements covers the N = 32 shell closure, and the overall uncertainties of the 52 − 55 Ti mass values were significantly reduced. Our results conclusively establish the existence of the weak shell effect at N = 32 , narrowing down the abrupt onset of this shell closure. Our data were compared with state-of-the-art ab initio shell model calculations which, despite very successfully describing where the N = 32 shell gap is strong, overpredict its strength and extent in titanium and heavier isotones. These measurements a…

Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients

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…

Optimization of anemia treatment in hemodialysis patients via reinforcement learning

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…

How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as medical device.

Background The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as medical devices (MD), being important to assess the associated risks. Methods An anemia control model (ACM), certified as MD may face adverse events as the result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model. Results A post-marketing dataset formed by all adult patients registe…

Nuclear charge radii and electromagnetic moments of radioactive scandium isotopes and isomers

International audience; Collinear laser spectroscopy experiments with the Sc + transition 3d4s 3 D 2 → 3d4p 3 F 3 at λ = 363.1 nm were performed on the 42−46 Sc isotopic chain using an ion guide isotope separator with a cooler-buncher. Nuclear magnetic dipole and electric quadrupole moments as well as isotope shifts were determined from the hyperfine structure for five ground states and two isomers. Extensive multi-configurational Dirac-Fock calculations were performed in order to evaluate the specific mass-shift, M SMS, and field-shift, F, parameters which allowed evaluation of the charge radii trend of the Sc isotopic sequence. The charge radii obtained show systematics more like the Ti r…

A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis

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…

Shell structure of potassium isotopes deduced from their magnetic moments

\item[Background] Ground-state spins and magnetic moments are sensitive to the nuclear wave function, thus they are powerful probes to study the nuclear structure of isotopes far from stability. \item[Purpose] Extend our knowledge about the evolution of the $1/2^+$ and $3/2^+$ states for K isotopes beyond the $N = 28$ shell gap. \item[Method] High-resolution collinear laser spectroscopy on bunched atomic beams. \item[Results] From measured hyperfine structure spectra of K isotopes, nuclear spins and magnetic moments of the ground states were obtained for isotopes from $N = 19$ up to $N = 32$. In order to draw conclusions about the composition of the wave functions and the occupation of the …

Editorial: The Future of Nuclear Structure: Challenges and Opportunities in the Microscopic Description of Nuclei

The past two decades have witnessed tremendous progress in the microscopic description of atomic nuclei. The Topical Review `The Future of Nuclear Structure' aims at summarizing the current state-of-the-art microscopic calculations in Nuclear Theory and to give a useful reference for young researches who wish to learn more about this exciting discipline.