Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds

[EN] Aim: The increasing availability of Big Biomedical Data is leading to large research data samples collected over long periods of time. We propose the analysis of the kinematics of data probability distributions over time towards the characterization of data temporal variability. Methods: First, we propose a kinematic model based on the estimation of a continuous data temporal trajectory, using Functional Data Analysis over the embedding of a non-parametric statistical manifold which points represent data temporal batches, the Information Geometric Temporal (IGT) plot. This model allows measuring the velocity and acceleration of data changes. Next, we propose a coordinate-free method to characterize the oriented seasonality of data based on the parallelism of lagged velocity vectors of the data trajectory throughout the IGT space, the Auto-Parallelism of Velocity Vectors (APVV) and APVVmap. Finally, we automatically explain the maximum variance components of the IGT space coordinates by means of correlating data points with known temporal factors from the domain application. Materials: Methods are evaluated on the US National Hospital Discharge Survey open dataset, consisting of 3,25M hospital discharges between 2000 and 2010. Results: Seasonal and abrupt behaviours were present on the estimated multivariate and univariate data trajectories. The kinematic analysis revealed seasonal effects and punctual increments in data celerity, the latter mainly related to abrupt changes in coding. The APVV and APVVmap revealed oriented seasonal changes on data trajectories. For most variables, their distributions tended to change to the same direction at a 12-month period, with a peak of change of directionality at mid and end of the year. Diagnosis and Procedure codes also included a 9-month periodic component. Kinematics and APVV methods were able to detect seasonal effects on extreme temporal subgrouped data, such as in Procedure code, where Fourier and autocorrelation methods were not able to. The automated explanation of IGT space coordinates was consistent with the results provided by the kinematic and seasonal analysis. Coordinates received different meanings according to the trajectory trend, seasonality and abrupt changes. Discussion: Treating data as a particle moving over time through a multidimensional probabilistic space and studying the kinematics of its trajectory has turned out to a new temporal variability methodology. Its results on the NHDS were aligned with the dataset and population descriptions found in the literature, contributing with a novel temporal variability characterization. We have demonstrated that the APVV and APVVmat are an appropriate tool for the coordinate-free and oriented analysis of trajectories or complex multivariate signals. Conclusion: The proposed methods comprise an exploratory methodology for the characterization of data temporal variability, what may be useful for a reliable reuse of Big Biomedical Data repositories acquired over long periods of time.This work was supported by UPV grant No. PAID-00-17, and projects DPI2016-80054-R and H2020-SC1-2016-CNECT No. 727560.Sáez, C.; Garcia-Gomez, JM. (2018). Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics. 119:109-124. https://doi.org/10.1016/j.ijmedinf.2018.09.015S10912411

Non-local spatially varying finite mixture models for image segmentation

[EN] In this work, we propose a new Bayesian model for unsupervised image segmentation based on a combination of the spatially varying finite mixture models (SVFMMs) and the non-local means (NLM) framework. The probabilistic NLM weighting function is successfully integrated into a varying Gauss¿Markov random field, yielding a prior density that adaptively imposes a local regularization to simultaneously preserve edges and enforce smooth constraints in homogeneous regions of the image. Two versions of our model are proposed: a pixel-based model and a patch-based model, depending on the design of the probabilistic NLM weighting function. Contrary to previous methods proposed in the literature, our approximation does not introduce new parameters to be estimated into the model, because the NLM weighting function is completely known once the neighborhood of a pixel is fixed. The proposed model can be estimated in closed-form solution via a maximum a posteriori (MAP) estimation in an expectation¿maximization scheme. We have compared our model with previously proposed SVFMMs using two public datasets: the Berkeley Segmentation dataset and the BRATS 2013 dataset. The proposed model performs favorably to previous approaches in the literature, achieving better results in terms of Rand Index and Dice metrics in our experiments.This study is partially supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R) and Agencia Valenciana de la Innovacion (INNVAL10/18/048). E.F.G was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement (No. 844646) and also acknowledges the support of NVIDIA GPU Grant Program.Juan -Albarracín, J.; Fuster García, E.; Juan, A.; Garcia-Gomez, JM. (2021). Non-local spatially varying finite mixture models for image segmentation. Statistics and Computing. 31(1):1-10. https://doi.org/10.1007/s11222-020-09988-w11031

Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique

[EN] Background: The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes-the division of populations of patients into more meaningful subgroups driven by clinical features-and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. Objective: We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. Methods: We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. Results: In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. Conclusions: The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.We sincerely thank the different types of clinical institutions and the Mexican government, which made a huge effort to make these data publicly available. We also thank the clinicians and epidemiologists from the Servicios de Salud de Nayarit for the useful discussions on specific aspects of the medical attention to hospitalized patients and the reporting of epidemiological data processes related to COVID-19. Furthermore, we would also like to thank Francisco Tomas Garcia Ruiz for his valuable help in data visualization design. This work was supported by Universitat Politecnica de Valencia contract no. UPV-SUB.2-1302 and FONDO SUPERA COVID-19 by CRUE-Santander Bank grant: "Severity Subgroup Discovery and Classification on COVID-19 Real World Data through Machine Learning and Data Quality assessment (SUBCOVERWD-19) ." The authors thank the Institute for Information and Communication Technologies (ITACA) at the Universitat Politecnica de Valencia for its support in the publication of this manuscript.Zhou, L.; Romero-Garcia, N.; Martínez-Miranda, J.; Conejero, JA.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2022). Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique. JMIR Public Health and Surveillance. 8(3):1-21. https://doi.org/10.2196/300321218

Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs

[EN] Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 No. 825750).Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcia-Gomez, JM. (2022). Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs. Health Informatics Journal. 28(2):1-18. https://doi.org/10.1177/1460458222109259211828

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

[EN] Objective: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. Materials and Methods: We used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. Results: Cases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting. Conclusions: Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.This work was supported by Universitat Politecnica de Valencia contract no. UPV-SUB.2-1302 and FONDO SUPERA COVID-19 by CRUE-Santander Bank grant "Severity Subgroup Discovery and Classification on COVID-19 Real World Data through Machine Learning and Data Quality assessment (SUBCOVERWD-19)."Sáez Silvestre, C.; Romero, N.; Conejero, JA.; Garcia-Gomez, JM. (2021). Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset. Journal of the American Medical Informatics Association. 28(2):360-364. https://doi.org/10.1093/jamia/ocaa25836036428

Robustness and findings of a web-based system for depression assessment in a university work context

[EN] Depression is associated with absenteeism and presentism, problems in workplace relationships and loss of productivity and quality. The present work describes the validation of a web-based system for the assessment of depression in the university work context. The basis of the system is the Spanish version of the Beck Depression Inventory (BDI-II). A total of 185 participants completed the BDI-II web-based assessment, including 88 males and 97 females, 70 faculty members and 115 staff members. A high level of internal consistency reliability was confirmed. Based on the results of our web-based BDI-II, no significant differences were found in depression severity between gender, age or workers' groups. The main depression risk factors reported were: Changes in sleep, Loss of energy, Tiredness or fatigue and Loss of interest. However significant differences were found by gender in Changes in appetite, Difficulty of concentration and Loss of interest in sex; males expressed less loss of interest in sex than females with a statistically significant difference. Our results indicate that the data collected is coherent with previous BDI-II studies. We conclude that the web-based system based on the BDI-II is psychometrically robust and can be used to assess depression in the university working community.Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies [727560]), the MTS4up project (DPI2016-80054-R) and patient-centered pathways of early palliative care, supportive ecosystems and appraisal standard (825750).Asensio-Cuesta, S.; Bresó, A.; Sáez Silvestre, C.; Garcia-Gomez, JM. (2019). Robustness and findings of a web-based system for depression assessment in a university work context. International Journal of Environmental research and Public Health. 16(4):1-17. https://doi.org/10.3390/ijerph16040644S117164Depression [Internet]. World Health Organization http://www.who.int/mediacentre/factsheets/fs369/en/Chang, S. M., Hong, J.-P., & Cho, M. J. (2011). Economic burden of depression in South Korea. Social Psychiatry and Psychiatric Epidemiology, 47(5), 683-689. doi:10.1007/s00127-011-0382-8Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010). The Journal of Clinical Psychiatry, 76(02), 155-162. doi:10.4088/jcp.14m09298Health and Safety at Work in Europe (1999–2007): A Statistical Portrait. Luxembourg. Publications Office of the European Union https://ec.europa.eu/eurostat/documents/3217494/5718905/KS-31-09-290-EN.PDF/88eef9f7-c229-40de-b1cd-43126bc4a946Lee, Y., Rosenblat, J. D., Lee, J., Carmona, N. E., Subramaniapillai, M., Shekotikhina, M., … McIntyre, R. S. (2018). Efficacy of antidepressants on measures of workplace functioning in major depressive disorder: A systematic review. Journal of Affective Disorders, 227, 406-415. doi:10.1016/j.jad.2017.11.003Schmidt, S., Roesler, U., Kusserow, T., & Rau, R. (2012). Uncertainty in the workplace: Examining role ambiguity and role conflict, and their link to depression—a meta-analysis. European Journal of Work and Organizational Psychology, 23(1), 91-106. doi:10.1080/1359432x.2012.711523Cuijpers, P., & Smit, F. (2004). Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatrica Scandinavica, 109(5), 325-331. doi:10.1111/j.1600-0447.2004.00301.xRihmer, Z. (2001). Can better recognition and treatment of depression reduce suicide rates? A brief review. European Psychiatry, 16(7), 406-409. doi:10.1016/s0924-9338(01)00598-3Nogueira-Martins, L. A., Fagnani Neto, R., Macedo, P. C. M., Cítero, V. A., & Mari, J. J. (2004). The mental health of graduate students at the Federal University of São Paulo: a preliminary report. Brazilian Journal of Medical and Biological Research, 37(10), 1519-1524. doi:10.1590/s0100-879x2004001000011Ibrahim, A. K., Kelly, S. J., Adams, C. E., & Glazebrook, C. (2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391-400. doi:10.1016/j.jpsychires.2012.11.015Levecque, K., Anseel, F., De Beuckelaer, A., Van der Heyden, J., & Gisle, L. (2017). Work organization and mental health problems in PhD students. Research Policy, 46(4), 868-879. doi:10.1016/j.respol.2017.02.008Zhong, J., You, J., Gan, Y., Zhang, Y., Lu, C., & Wang, H. (2009). Job Stress, Burnout, Depression Symptoms, and Physical Health among Chinese University Teachers. Psychological Reports, 105(3_suppl), 1248-1254. doi:10.2466/pr0.105.f.1248-1254The International Test Commission. (2006). International Guidelines on Computer-Based and Internet-Delivered Testing. International Journal of Testing, 6(2), 143-171. doi:10.1207/s15327574ijt0602_4Reevy, G. M., & Deason, G. (2014). Predictors of depression, stress, and anxiety among non-tenure track faculty. Frontiers in Psychology, 5. doi:10.3389/fpsyg.2014.00701McLean, L., & Connor, C. M. (2015). Depressive Symptoms in Third‐Grade Teachers: Relations to Classroom Quality and Student Achievement. Child Development, 86(3), 945-954. doi:10.1111/cdev.12344Griffiths, K. M., Christensen, H., Jorm, A. F., Evans, K., & Groves, C. (2004). Effect of web-based depression literacy and cognitive–behavioural therapy interventions on stigmatising attitudes to depression. British Journal of Psychiatry, 185(4), 342-349. doi:10.1192/bjp.185.4.342HASLAM, C., ATKINSON, S., BROWN, S., & HASLAM, R. (2005). Anxiety and depression in the workplace: Effects on the individual and organisation (a focus group investigation). Journal of Affective Disorders, 88(2), 209-215. doi:10.1016/j.jad.2005.07.009Finkelstein, J., & Lapshin, O. (2007). Reducing depression stigma using a web-based program. International Journal of Medical Informatics, 76(10), 726-734. doi:10.1016/j.ijmedinf.2006.07.004BECK, A. T. (1961). An Inventory for Measuring Depression. Archives of General Psychiatry, 4(6), 561. doi:10.1001/archpsyc.1961.01710120031004Montgomery, S. A., & Åsberg, M. (1979). A New Depression Scale Designed to be Sensitive to Change. British Journal of Psychiatry, 134(4), 382-389. doi:10.1192/bjp.134.4.382Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. General Hospital Psychiatry, 32(4), 345-359. doi:10.1016/j.genhosppsych.2010.03.006ZUNG, W. W. K. (1965). A Self-Rating Depression Scale. Archives of General Psychiatry, 12(1), 63. doi:10.1001/archpsyc.1965.01720310065008Ginting, H., Näring, G., van der Veld, W. M., Srisayekti, W., & Becker, E. S. (2013). Validating the Beck Depression Inventory-II in Indonesia’s general population and coronary heart disease patients. International Journal of Clinical and Health Psychology, 13(3), 235-242. doi:10.1016/s1697-2600(13)70028-0Kojima, M., Furukawa, T. A., Takahashi, H., Kawai, M., Nagaya, T., & Tokudome, S. (2002). Cross-cultural validation of the Beck Depression Inventory-II in Japan. Psychiatry Research, 110(3), 291-299. doi:10.1016/s0165-1781(02)00106-3Kapci, E. G., Uslu, R., Turkcapar, H., & Karaoglan, A. (2008). Beck Depression Inventory II: evaluation of the psychometric properties and cut-off points in a Turkish adult population. Depression and Anxiety, 25(10), E104-E110. doi:10.1002/da.20371Aratake, Y., Tanaka, K., Wada, K., Watanabe, M., Katoh, N., Sakata, Y., & Aizawa, Y. (2007). Development of Japanese Version of the Checklist Individual Strength Questionnaire in a Working Population. Journal of Occupational Health, 49(6), 453-460. doi:10.1539/joh.49.453Kühner, C., Bürger, C., Keller, F., & Hautzinger, M. (2007). Reliabilität und Validität des revidierten Beck-Depressionsinventars (BDI-II). Der Nervenarzt, 78(6), 651-656. doi:10.1007/s00115-006-2098-7Holländare, F., Andersson, G., & Engström, I. (2010). A Comparison of Psychometric Properties Between Internet and Paper Versions of Two Depression Instruments (BDI-II and MADRS-S) Administered to Clinic Patients. Journal of Medical Internet Research, 12(5), e49. doi:10.2196/jmir.1392Potential of the Internet for Personality Research https://www.sciencedirect.com/science/article/pii/B978012099980450006XCarlbring, P., Brunt, S., Bohman, S., Austin, D., Richards, J., Öst, L.-G., & Andersson, G. (2007). Internet vs. paper and pencil administration of questionnaires commonly used in panic/agoraphobia research. 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A Game-Theory method to design job rotation schedules to prevent musculoskeletal disorders Based on workers preferences and competencies

[EN] Job rotation is an organizational strategy based on the systematic exchange of workers between jobs in a planned manner according to specific criteria. This study presents the GS-Rot method, a method based on Game Theory, in order to design job rotation schedules by considering not only workers' job preferences, but also the competencies required for different jobs. With this approach, we promote workers' active participation in the design of the rotation plan. It also let us deal with restrictions in assigning workers to job positions according to their disabilities (temporal or permanent). The GS-Rot method has been implemented online and applied to a case in a work environment characterized by the presence of a high repetition of movements, which is a significant risk factor associated with work-related musculoskeletal disorders (WMSDs). A total of 17 workstations and 17 workers were involved in the rotation, four of them with physical/psychological limitations. Feasible job rotation schedules were obtained in a short time (average time 27.4 milliseconds). The results indicate that in the rotations driven by preference priorities, almost all the workers (94.11%) were assigned to one of their top five preferences. Likewise, 48.52% of job positions were assigned to workers in their top five of their competence lists. When jobs were assigned according to competence, 58.82% of workers got an assignment among their top five competence lists. Furthermore, 55.87% of the workers achieved jobs in their top five preferences. In both rotation scenarios, the workers varied performed jobs, and fatigue accumulation was balanced among them. The GS-Rot method achieved feasible and uniform solutions regarding the workers' exposure to job repetitiveness.This research was funded by the Erasmus+ program of the European Commission under Grant 2017-1-ES01-KA203-038589 in the frame of the project CoSki21-Core Skills for 21th-century professionals.Asensio-Cuesta, S.; Garcia-Gomez, JM.; Poza-Lujan, J.; Conejero, JA. (2019). A Game-Theory method to design job rotation schedules to prevent musculoskeletal disorders Based on workers preferences and competencies. International Journal of Environmental research and Public Health. 16(23):1-16. https://doi.org/10.3390/ijerph16234666S1161623Aptel, M., Cail, F., Gerling, A., & Louis, O. (2008). Proposal of parameters to implement a workstation rotation system to protect against MSDs. International Journal of Industrial Ergonomics, 38(11-12), 900-909. doi:10.1016/j.ergon.2008.02.006Jeon, I. S., Jeong, B. Y., & Jeong, J. H. (2016). Preferred 11 different job rotation types in automotive company and their effects on productivity, quality and musculoskeletal disorders: comparison between subjective and actual scores by workers’ age. Ergonomics, 59(10), 1318-1326. doi:10.1080/00140139.2016.1140816Botti, L., Mora, C., & Calzavara, M. (2017). Design of job rotation schedules managing the exposure to age-related risk factors. IFAC-PapersOnLine, 50(1), 13993-13997. doi:10.1016/j.ifacol.2017.08.2420Sixth European Working Conditions Survey-6th EWCS-Spainhttps://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015/ewcs-2015-methodologyAsensio-Cuesta, S., Diego-Mas, J. A., Canós-Darós, L., & Andrés-Romano, C. (2011). A genetic algorithm for the design of job rotation schedules considering ergonomic and competence criteria. The International Journal of Advanced Manufacturing Technology, 60(9-12), 1161-1174. doi:10.1007/s00170-011-3672-0Yoon, S.-Y., Ko, J., & Jung, M.-C. (2016). A model for developing job rotation schedules that eliminate sequential high workloads and minimize between-worker variability in cumulative daily workloads: Application to automotive assembly lines. Applied Ergonomics, 55, 8-15. doi:10.1016/j.apergo.2016.01.011Otto, A., & Scholl, A. (2012). Reducing ergonomic risks by job rotation scheduling. OR Spectrum, 35(3), 711-733. doi:10.1007/s00291-012-0291-6Carnahan, B. J., Redfern, M. S., & Norman, B. (2000). Designing safe job rotation schedules using optimization and heuristic search. Ergonomics, 43(4), 543-560. doi:10.1080/001401300184404Song, J., Lee, C., Lee, W., Bahn, S., Jung, C., & Yun, M. H. (2016). Development of a job rotation scheduling algorithm for minimizing accumulated work load per body parts. 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The American Mathematical Monthly, 69(1), 9-15. doi:10.1080/00029890.1962.11989827Roth, A. E. (2008). What Have We Learned from Market Design? The Economic Journal, 118(527), 285-310. doi:10.1111/j.1468-0297.2007.02121.xRoth, A. E., & Sotomayor, M. (1992). Chapter 16 Two-sided matching. Handbook of Game Theory with Economic Applications, 485-541. doi:10.1016/s1574-0005(05)80019-0Renna, P. (2017). Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model. Journal of Manufacturing Systems, 45, 149-158. doi:10.1016/j.jmsy.2017.09.005Butkovič, P., & Lewis, S. (2007). On the job rotation problem. Discrete Optimization, 4(2), 163-174. doi:10.1016/j.disopt.2006.11.00

EHRtemporalVariability: delineating temporal data-set shifts in electronic health records

[EN] Background: Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. Results: EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. Conclusions: EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.This work was supported by Universitat Politecnica de Valencia grant PAID-00-17, Generalitat Valenciana grant BEST/2018, and projects H2020-SC1-2016-CNECT No. 727560 and H2020-SC1-BHC-2018-2020 No. 825750Sáez Silvestre, C.; Gutiérrez-Sacristán, A.; Kohane, I.; Garcia-Gomez, JM.; Avillach, P. (2020). EHRtemporalVariability: delineating temporal data-set shifts in electronic health records. GigaScience. 9(8):1-7. https://doi.org/10.1093/gigascience/giaa079S1798Gewin, V. (2016). Data sharing: An open mind on open data. Nature, 529(7584), 117-119. doi:10.1038/nj7584-117aKatzan, I. L., & Rudick, R. A. (2012). Time to Integrate Clinical and Research Informatics. Science Translational Medicine, 4(162). doi:10.1126/scitranslmed.3004583Zhu, L., & Zheng, W. J. (2018). 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