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Author |
Carrasco-Ribelles, L.A.; Pardo-Mas, J.R.; Tortajada, S.; Saez, C.; Valdivieso, B.; Garcia-Gomez, J.M. |
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Title |
Predicting morbidity by local similarities in multi-scale patient trajectories |
Type |
Journal Article |
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Year |
2021 |
Publication |
Journal of Biomedical Informatics |
Abbreviated Journal |
J. Biomed. Inform. |
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Volume |
120 |
Issue |
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Pages |
103837 - 9pp |
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Keywords |
Patient trajectory; Risk prediction; Local alignment; Dynamic programming; Diabetes; Cardiovascular disease |
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Abstract |
Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening. |
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Address |
[Carrasco-Ribelles, Lucia A.; Pardo-Mas, Jose Ramon; Saez, Carlos; Garcia-Gomez, Juan M.] Univ Politecn Valencia, Biomed Data Sci Lab BDSLAB, Inst Tecnol Informat & Comunicac ITACA, Camino Vera S-N, Valencia 46022, Spain, Email: lucarri@etsii.upv.es; |
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Academic Press Inc Elsevier Science |
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English |
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ISSN |
1532-0464 |
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Notes |
WOS:000683527500003 |
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no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
4934 |
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Author |
Fanchiotti, H.; Garcia Canal, C.A.; Mayosky, M.; Veiga, A.; Vento, V. |
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Title |
Measuring the Hannay geometric phase |
Type |
Journal Article |
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Year |
2022 |
Publication |
American Journal of Physics |
Abbreviated Journal |
Am. J. Phys. |
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Volume |
90 |
Issue |
6 |
Pages |
430-435 |
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Abstract |
The Hannay geometric phase is the classical analog of the well-known Berry phase. Its most familiar example is the effect of the latitude lambda on the motion of a Foucault pendulum. We describe an electronic network whose behavior is exactly equivalent to that of the pendulum. The circuit can be constructed from off-the-shelf components using two matched transconductance amplifiers that comprise a gyrator to introduce the non-reciprocal behavior needed to mimic the pendulum. One may precisely measure the dependence of the Hannay phase on lambda by circuit simulation and by laboratory measurements on a constructed circuit. |
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Address |
[Fanchiotti, H.; Canal, C. A. Garcia] Univ Nacl La Plata, IFLP, CONICET, CC67, RA-1900 La Plata, Argentina |
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AIP Publishing |
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English |
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0002-9505 |
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Notes |
WOS:000804547100009 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5276 |
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Author |
Bernabeu, J.; Sabulsky, D.O.; Sanchez, F.; Segarra, A. |
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Title |
Neutrino mass and nature through its mediation in atomic clock interference |
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Journal Article |
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Year |
2024 |
Publication |
AVS Quantum Science |
Abbreviated Journal |
AVS Quantum Sci. |
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Volume |
6 |
Issue |
1 |
Pages |
014410 - 8pp |
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Abstract |
The absolute mass of neutrinos and their nature are presently unknown. Aggregate matter has a coherent weak charge leading to a repulsive interaction mediated by a neutrino pair. The virtual neutrinos are non-relativistic at micron distances, giving a distinct behavior for Dirac versus Majorana mass terms. This effective potential allows for the disentanglement of the Dirac or Majorana nature of the neutrino via magnitude and distance dependence. We propose an experiment to search for this potential based on the concept that the density-dependent interaction of an atomic probe with a material source in one arm of an atomic clock interferometer generates a differential phase. The appropriate geometry of the device is selected using the saturation of the weak potential as a guide. The proposed experiment has the added benefit of being sensitive to gravity at micron distances. A strategy to suppress the competing Casimir-Polder interaction, depending on the electronic structure of the material source, as well as a way to compensate the gravitational interaction in the two arms of the interferometer is discussed. |
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Address |
[Bernabeu, Jose; Segarra, Alejandro] Univ Valencia, Dept Theoret Phys, E-46100 Valencia, Spain, Email: jose.bernabeu@uv.es |
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AIP Publishing |
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Notes |
WOS:001186930100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6118 |
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Author |
Conde, D.; Castillo, F.L.; Escobar, C.; García, C.; Garcia Navarro, J.E.; Sanz, V.; Zaldívar, B.; Curto, J.J.; Marsal, S.; Torta, J.M. |
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Title |
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
Type |
Journal Article |
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Year |
2023 |
Publication |
Space Weather |
Abbreviated Journal |
Space Weather |
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Volume |
21 |
Issue |
11 |
Pages |
e2023SW003474 - 27pp |
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Keywords |
geomagnetic storms; deep learning; forecasting; SYM-H; uncertainties; hyper-parameter optimization |
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Abstract |
Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests. |
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Address |
[Conde, D.; Escobar, C.; Garcia, C.; Garcia, J. E.; Sanz, V.; Zaldivar, B.] Univ Valencia, CSIC, Ctr Mixto, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Daniel.Conde@ific.uv.es |
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Publisher |
Amer Geophysical Union |
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English |
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Notes |
WOS:001104189700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5804 |
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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
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Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Publisher |
Amer Medical Assoc |
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English |
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ISSN |
2574-3805 |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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Author |
BABAR Collaboration (Lees, J.P. et al); Martinez-Vidal, F.; Oyanguren, A. |
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Title |
Search for B+ -> K+tau(+)tau(-) at the BABAR Experiment |
Type |
Journal Article |
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Year |
2017 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
118 |
Issue |
3 |
Pages |
031802 - 8pp |
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Abstract |
We search for the rare flavor-changing neutral current process B+ -> K+tau(+)tau(-) using data from the BABAR experiment. The data sample, collected at the center-of-mass energy of the Upsilon(4S) resonance, corresponds to a total integrated luminosity of 424 fb(-1) and to 471 x 10(6) B (B) over bar pairs. We reconstruct one B meson, produced in the Upsilon(4S) -> B+B- decay, in one of many hadronic decay modes and search for activity compatible with a B+ -> K+tau(+)tau(-) decay in the rest of the event. Each tau lepton is required to decay leptonically into an electron or muon and neutrinos. Comparing the expected number of background events with the data sample after applying the selection criteria, we do not find evidence for a signal. The resulting upper limit, at the 90% confidence level, is B(B+ -> K+tau(+)tau(-)) < 2.25 x 10(-3). |
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Address |
[Lees, J. P.; Poireau, V.; Tisserand, V.] Univ Savoie, IN2P3, CNRS, LAPP, F-74941 Annecy Le Vieux, France |
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Amer Physical Soc |
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English |
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ISSN |
0031-9007 |
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Notes |
WOS:000392290200010 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2945 |
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Author |
LHCb Collaboration (Aaij, R. et al); Martinez-Vidal, F.; Oyanguren, A.; Remon Alepuz, C.; Ruiz Valls, P.; Sanchez Mayordomo, C. |
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Title |
Observation of J/psi phi Structures Consistent with Exotic States from Amplitude Analysis of B+ -> J/psi phi K+ Decays |
Type |
Journal Article |
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Year |
2017 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
118 |
Issue |
2 |
Pages |
022003 - 10pp |
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Abstract |
The first full amplitude analysis of B+ -> J/psi phi K+ with J/psi -> mu(+)mu(-), phi -> K+K- decays is performed with a data sample of 3 fb(-1) of pp collision data collected at root s = 7 and 8 TeV with the LHCb detector. The data cannot be described by a model that contains only excited kaon states decaying into phi K+ , and four J/psi phi structures are observed, each with significance over 5 standard deviations. The quantum numbers of these structures are determined with significance of at least 4 standard deviations. The lightest has mass consistent with, but width much larger than, previous measurements of the claimed X(4140) state. |
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Address |
[Archilli, F.; Bediaga, I.; De Miranda, J. M.; Ferreira Rodrigues, F.; Gomes, A.; Massafferri, A.; dos Reis, A. C.; Rodrigues, A. B.; Vieira, D.] CBPF, Rio De Janeiro, Brazil |
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Amer Physical Soc |
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English |
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0031-9007 |
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Notes |
WOS:000391926900004 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2946 |
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Author |
Barenboim, G.; Kinney, W.H.; Park, W.I. |
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Title |
Resurrection of large lepton number asymmetries from neutrino flavor oscillations |
Type |
Journal Article |
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Year |
2017 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
95 |
Issue |
4 |
Pages |
043506 - 6pp |
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Abstract |
We numerically solve the evolution equations of neutrino three-flavor density matrices, and show that, even if neutrino oscillations mix neutrino flavors, large lepton number asymmetries are still allowed in certain limits by big bang nucleosynthesis. |
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Address |
[Barenboim, Gabriela; Park, Wan-Il] Univ Valencia, CSIC, Dept Fis Teor, E-46100 Burjassot, Spain, Email: Gabriela.Barenboim@uv.es; |
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Publisher |
Amer Physical Soc |
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English |
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ISSN |
2470-0010 |
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Notes |
WOS:000393512400003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2952 |
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Author |
Binosi, D.; Chang, L.; Papavassiliou, J.; Qin, S.X.; Roberts, C.D. |
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Title |
Natural constraints on the gluon-quark vertex |
Type |
Journal Article |
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Year |
2017 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
95 |
Issue |
3 |
Pages |
031501 - 7pp |
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Abstract |
In principle, the strong-interaction sector of the standard model is characterized by a unique renormalization-group-invariant (RGI) running interaction and a unique form for the dressed-gluonquark vertex, Gamma mu; but, whilst much has been learnt about the former, the latter is still obscure. In order to improve this situation, we use a RGI running-interaction that reconciles top-down and bottom-up analyses of the gauge sector in quantum chromodynamics (QCD) to compute dressed-quark gap equation solutions with 1,660,000 distinct Ansatze for Gamma mu. Each one of the solutions is then tested for compatibility with three physical criteria and, remarkably, we find that merely 0.55% of the solutions survive the test. Evidently, even a small selection of observables places extremely tight bounds on the domain of realistic vertex Ansatze. This analysis and its results should prove useful in constraining insightful contemporary studies of QCD and hadronic phenomena. |
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Address |
[Binosi, Daniele] European Ctr Theoret Studies Nucl Phys & Related, Str Tabarelle 286, I-38123 Villazzano, TN, Italy |
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Amer Physical Soc |
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English |
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2470-0010 |
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Notes |
WOS:000393507500001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2953 |
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Author |
XENON Collaboration (Aprile, E. et al); Orrigo, S.E.A. |
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Title |
Search for two-neutrino double electron capture of Xe-124 with XENON100 |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Physical Review C |
Abbreviated Journal |
Phys. Rev. C |
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Volume |
95 |
Issue |
2 |
Pages |
024605 - 6pp |
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Abstract |
Two-neutrino double electron capture is a rare nuclear decay where two electrons are simultaneously captured from the atomic shell. For Xe-124 this process has not yet been observed and its detection would provide a new reference for nuclear matrix element calculations. We have conducted a search for two-neutrino double electron capture from the K shell of 124Xe using 7636 kg d of data from the XENON100 dark matter detector. Using a Bayesian analysis we observed no significant excess above background, leading to a lower 90% credibility limit on the half-life T-1/2 > 6.5 x 10(20) yr. We have also evaluated the sensitivity of the XENON1T experiment, which is currently being commissioned, and found a sensitivity of T-1/2 > 6.1 x 10(22) yr after an exposure of 2 t yr. |
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Address |
[Agostini, F.; Barrow, P.; Berger, T.; Butikofer, L.; Cardoso, J. M. R.; Di Gangi, P.; Diglio, S.; Franco, D.; Garbini, M.; Grignon, C.; Kaminsky, B.; Le Calloch, M.; Lopes, J. A. M.; Orrigo, S. E. A.; Sartorelli, G.; Schreiner, J.; Thers, D.; Zhang, Y.] Columbia Univ, Dept Phys, 538 W 120th St, New York, NY 10027 USA, Email: a.fieguth@uni-muenster.de; |
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Amer Physical Soc |
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English |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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| |
Series Volume |
|
Series Issue |
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Edition |
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ISSN |
2469-9985 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000393744200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2956 |
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