Journal articles: 'Data incompleteness'

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Acharya, Nanyan V., Lynda V. Wilton, and Saad A. Shakir. "Incompleteness of Lamotrigine Data." Drug Safety 24, no. 2 (2001): 155–56. http://dx.doi.org/10.2165/00002018-200124020-00005.

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Messenheimer, John A., Marcus E. Risner, and Luigi Giorgi. "Incompleteness of Lamotrigine Data." Drug Safety 24, no. 2 (2001): 155–56. http://dx.doi.org/10.2165/00002018-200124020-00006.

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Muris, Chris. "Efficient GMM Estimation with Incomplete Data." Review of Economics and Statistics 102, no. 3 (June 2020): 518–30. http://dx.doi.org/10.1162/rest_a_00836.

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In the standard missing data model, data are either complete or completely missing. However, applied researchers face situations with an arbitrary number of strata of incompleteness. Examples include unbalanced panels and instrumental variables settings where some observations are missing some instruments. I propose a model for settings where observations may be incomplete, with an arbitrary number of strata of incompleteness. I derive a set of moment conditions that generalizes those in Graham's ( 2011 ) standard missing data setup. I derive the associated efficiency bound and propose efficient estimators. Identification can be achieved even if it fails in each stratum of incompleteness.

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Yi, Hui, Zehui Mao, Bin Jiang, Cuimei Bo, Yufang Liu, and Hui Luo. "Fault Diagnosis in Condition of Sample Type Incompleteness Using Support Vector Data Description." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/432651.

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Faulty samples are much harder to acquire than normal samples, especially in complicated systems. This leads to incompleteness for training sample types and furthermore a decrease of diagnostic accuracy. In this paper, the relationship between sample-type incompleteness and the classifier-based diagnostic accuracy is discussed first. Then, a support vector data description-based approach, which has taken the effects of sample-type incompleteness into consideration, is proposed to refine the construction of fault regions and increase the diagnostic accuracy for the condition of incomplete sample types. The effectiveness of the proposed method was validated on both a Gaussian distributed dataset and a practical dataset. Satisfactory results have been obtained.

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Trovati, Marcello, and Olayinka Johnny. "Big data inconsistencies and incompleteness: a literature review." International Journal of Grid and Utility Computing 11, no. 5 (2020): 705. http://dx.doi.org/10.1504/ijguc.2020.10030948.

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Johnny, Olayinka, and Marcello Trovati. "Big data inconsistencies and incompleteness: a literature review." International Journal of Grid and Utility Computing 11, no. 5 (2020): 705. http://dx.doi.org/10.1504/ijguc.2020.110057.

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Flores, Jorge R. "Taxon incompleteness and discrete time bins affect character change rates in simulated data." Biology Letters 16, no. 11 (November 2020): 20200418. http://dx.doi.org/10.1098/rsbl.2020.0418.

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Estimating how fast or slow morphology evolves through time (phenotypic change rate, PR) has become common in macroevolutionary studies and has been important for clarifying key evolutionary events. However, the inclusion of incompletely scored taxa (e.g. fossils) and variable lengths of discrete arbitrary time bins could affect PR estimates and potentially mask real PR patterns. Here, the impact of taxon incompleteness (unscored data) on PR estimates is assessed in simulated data. Three different time bin series were likewise evaluated: bins evenly spanning the tree length (i), a shorter middle bin and longer first and third bins (ii), and a longer middle bin and shorter first and third bins (iii). The results indicate that PR values decrease as taxon incompleteness increases. Statistically significant PR values, and the dispersion among PR values, depended on the time bins. These outcomes imply that taxon incompleteness can undermine our capacity to infer morphology evolutionary dynamics and that these estimates are also influenced by our choice of discrete time bins. More importantly, the present results stress the need for a better approach to deal with taxon incompleteness and arbitrary discrete time bins.

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Sanz, Joaquin, Emanuele Cozzo, Javier Borge-Holthoefer, and Yamir Moreno. "Topological effects of data incompleteness of gene regulatory networks." BMC Systems Biology 6, no. 1 (2012): 110. http://dx.doi.org/10.1186/1752-0509-6-110.

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Marino-Buslje, Cristina, Alexander Miguel Monzon, Diego Javier Zea, María Silvina Fornasari, and Gustavo Parisi. "On the dynamical incompleteness of the Protein Data Bank." Briefings in Bioinformatics 20, no. 1 (August 2, 2017): 356–59. http://dx.doi.org/10.1093/bib/bbx084.

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Qi, Qianya, Li Yan, and Lili Tian. "Testing equality of means in partially paired data with incompleteness in single response." Statistical Methods in Medical Research 28, no. 5 (April 4, 2018): 1508–22. http://dx.doi.org/10.1177/0962280218765007.

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In testing differentially expressed genes between tumor and healthy tissues, data are usually collected in paired form. However, incomplete paired data often occur. While extensive statistical researches exist for paired data with incompleteness in both arms, hardly any recent work can be found on paired data with incompleteness in single arm. This paper aims to fill this gap by proposing some new methods, namely, P-value pooling methods and a nonparametric combination test. Simulation studies are conducted to investigate the performance of the proposed methods in terms of type I error and power at small to moderate sample sizes. A real data set from The Cancer Genome Atlas (TCGA) breast cancer study is analyzed using the proposed methods.

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Wang, Maogang, Prithviraj Banerjee, and Majid Sarrafzadeh. "Placement with Incomplete Data." VLSI Design 10, no. 1 (January 1, 1999): 57–70. http://dx.doi.org/10.1155/1999/42648.

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Traditional placement problems are studied under a fully specified cell library and a complete netlist. However, in the first, e.g., 2 years of a 2 – 3 year microprocessor design cycle, the detailed netlist is unavailable. For area and performance estimation, layout must nevertheless be done with incomplete information. Another source of incompleteness comes from logic synthesis changes; some instances and their parameters will change as the project evolves. In the re-configurable computing area, sometimes we need to perform quick placement before all information is available. The problem of placement with incomplete data (PID) can be abstracted as having to place a circuit when pc% of the cells and pn% of the nets are missing. The key challenge in PID is how to add missing cells and nets.In this paper, two “patching-methods” for adding missing nets and cells are proposed. The methods are called abstraction and fusion.Experimental results are very interesting. First, they show that PID is a difficult problem and an arbitrary (and perhaps intuitively sound) method may not produce highquality results. Experiments verify that the abstraction method is a very good predictor and that fusion is not. Specifically, when a circuit has 10% incompleteness, abstraction can predict the final total wirelength with an error of 5.8% while fusion has a 67.8% error in predicting the wirelength in the same circuit.

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Furukawa, Kyoji, Dale L. Preston, Munechika Misumi, and Harry M. Cullings. "Handling incomplete smoking history data in survival analysis." Statistical Methods in Medical Research 26, no. 2 (October 26, 2014): 707–23. http://dx.doi.org/10.1177/0962280214556794.

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While data are unavoidably missing or incomplete in most observational studies, consequences of mishandling such incompleteness in analysis are often overlooked. When time-varying information is collected irregularly and infrequently over a long period, even precisely obtained data may implicitly involve substantial incompleteness. Motivated by an analysis to quantitatively evaluate the effects of smoking and radiation on lung cancer risks among Japanese atomic-bomb survivors, we provide a unique application of multiple imputation to incompletely observed smoking histories under the assumption of missing at random. Predicting missing values for the age of smoking initiation and, given initiation, smoking intensity and cessation age, analyses can be based on complete, though partially imputed, smoking histories. A simulation study shows that multiple imputation appropriately conditioned on the outcome and other relevant variables can produce consistent estimates when data are missing at random. Our approach is particularly appealing in large cohort studies where a considerable amount of time-varying information is incomplete under a mechanism depending in a complex manner on other variables. In application to the motivating example, this approach is expected to reduce estimation bias that might be unavoidable in naive analyses, while keeping efficiency by retaining known information.

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Nwagwu, Honour Chika. "Visualising Inconsistency and Incompleteness in RDF Gene Expression Data using FCA." International Journal of Conceptual Structures and Smart Applications 2, no. 1 (January 2014): 68–82. http://dx.doi.org/10.4018/ijcssa.2014010105.

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The integration of data from different data sources can result to the existence of inconsistent or incomplete data (IID). IID can undermine the validity of information retrieved from an integrated dataset. There is therefore a need to identify these anomalies. This work presents SPARQL queries that retrieve from an EMAGE dataset, information which are inconsistent or incomplete. Also, it will be shown how Formal Concept Analysis (FCA) tools notably FcaBedrock and Concept Explorer can be applied to identify and visualise IID existing in these retrieved information. Although, instances of IID can exist in most data formats, the investigation is focused on RDF dataset.

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Aljojo, Nahla. "Examining Heterogeneity Structured on a Large Data Volume with Minimal Incompleteness." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (November 2, 2021): 30–37. http://dx.doi.org/10.14500/aro.10857.

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While Big Data analytics can provide a variety of benefits, processing heterogeneous data comes with its own set of limitations. A transaction pattern must be studied independently while working with Bitcoin data, this study examines twitter data related to Bitcoin and investigate communications pattern on bitcoin transactional tweet. Using the hashtags #Bitcoin or #BTC on Twitter, a vast amount of data was gathered, which was mined to uncover a pattern that everyone either (speculators, teaches, or the stakeholders) uses on Twitter to discuss Bitcoin transactions. This aim is to determine the direction of Bitcoin transaction tweets based on historical data. As a result, this research proposes using Big Data analytics to track Bitcoin transaction communications in tweets in order to discover a pattern. Hadoop platform MapReduce was used. The finding indicate that In the map step of the procedure, Hadoop's tokenize the dataset and parse them to the mapper where thirteen patterns were established and reduced to three patterns using the attributes previously stored data in the Hadoop context, one of which is the Emoji data that was left out in previous research discussions, but the text is only one piece of the puzzle on bitcoin transaction interaction, and the key part of it is “No certainty, only possibilities” in Bitcoin transactions

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Gregori, Enrico, Alessandro Improta, Luciano Lenzini, Lorenzo Rossi, and Luca Sani. "A Novel Methodology to Address the Internet AS-Level Data Incompleteness." IEEE/ACM Transactions on Networking 23, no. 4 (August 2015): 1314–27. http://dx.doi.org/10.1109/tnet.2014.2323128.

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Islam, Syrus, Ralph Adler, and Deryl Northcott. "Managerial attitudes towards the incompleteness of performance measurement systems." Qualitative Research in Accounting & Management 15, no. 1 (April 16, 2018): 84–103. http://dx.doi.org/10.1108/qram-10-2017-0106.

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Purpose Performance measurement systems (PMSs) are at the heart of most organisations. The aim of this study is to examine the attitudes of top-level managers towards the incompleteness of PMSs. Design/methodology/approach This paper draws on an in-depth field study conducted in an energy and environmental services provider based in New Zealand. The data, which were obtained from 20 semi-structured interviews, were triangulated against on-site observations and company documents. Findings The findings suggest that whether the incompleteness of a PMS is considered problematic or non-problematic depends on the role that the PMS plays in implementing a firm’s strategy. The authors show that when the PMS is mainly used to trigger improvement activities on and around strategic objectives and managers perceive adequate improvement activities to exist, then they consider the incompleteness of the PMS in relation to these strategic objectives to be non-problematic. Originality/value This study contributes to the nascent literature on managerial attitudes towards the incompleteness of PMSs by identifying conditions under which the incompleteness is considered problematic or non-problematic. The authors also contribute to the literature on the association between design qualities of PMSs and firm performance by suggesting that poor design qualities of a PMS (such as incompleteness) may not always translate into poor firm performance.

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Najib, Fatma M., Rasha M. Ismail, Nagwa L. Badr, and Tarek F. Gharib. "Incomplete high dimensional data streams clustering." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4227–43. http://dx.doi.org/10.3233/jifs-200297.

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Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.

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Zaveri, Amrapali, Andrea Maurino, and Laure-Berti Equille. "Web Data Quality." International Journal on Semantic Web and Information Systems 10, no. 2 (April 2014): 1–6. http://dx.doi.org/10.4018/ijswis.2014040101.

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The standardization and adoption of Semantic Web technologies has resulted in an unprecedented volume of data being published as Linked Data (LD). However, the “publish first, refine later” philosophy leads to various quality problems arising in the underlying data such as incompleteness, inconsistency and semantic ambiguities. In this article, we describe the current state of Data Quality in the Web of Data along with details of the three papers accepted for the International Journal on Semantic Web and Information Systems' (IJSWIS) Special Issue on Web Data Quality. Additionally, we identify new challenges that are specific to the Web of Data and provide insights into the current progress and future directions for each of those challenges.

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Gurupur, Varadraj P., and Muhammed Shelleh. "Machine Learning Analysis for Data Incompleteness (MADI): Analyzing the Data Completeness of Patient Records Using a Random Variable Approach to Predict the Incompleteness of Electronic Health Records." IEEE Access 9 (2021): 95994–6001. http://dx.doi.org/10.1109/access.2021.3095240.

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Kostopoulou, Olga, Christopher Tracey, and Brendan C. Delaney. "Can decision support combat incompleteness and bias in routine primary care data?" Journal of the American Medical Informatics Association 28, no. 7 (March 11, 2021): 1461–67. http://dx.doi.org/10.1093/jamia/ocab025.

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Abstract Objective Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias. Materials and Methods We used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding. Results Supported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = −0.08 [−0.11, −0.05] P < .001) in the supported consultations, and this was the case for both codes and free text. Conclusions We provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.

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Grande, Giuseppe, and Luigi Ventura. "Labor income and risky assets under market incompleteness: Evidence from Italian data." Journal of Banking & Finance 26, no. 2-3 (March 2002): 597–620. http://dx.doi.org/10.1016/s0378-4266(01)00236-9.

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Jacobs, Kris, Stéphane Pallage, and Michel A. Robe. "Market incompleteness and the equity premium puzzle: Evidence from state-level data." Journal of Banking & Finance 37, no. 2 (February 2013): 378–88. http://dx.doi.org/10.1016/j.jbankfin.2012.09.005.

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Ashby, Michael, and Joanna Przedlacka. "Measuring incompleteness: Acoustic correlates of glottal articulations." Journal of the International Phonetic Association 44, no. 3 (November 25, 2014): 283–96. http://dx.doi.org/10.1017/s002510031400019x.

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The autocorrelation function, a measure of regularity in the speech signal, is applied in demarcating the seemingly diffuse intervals of glottalization which accompany or replace voiceless oral stops in elicited recordings from 22 young speakers of Southern British English. It is shown that a local minimum in autocorrelation characterizes almost all instances heard as intervocalic glottal stops; an annotation procedure is developed and used to gather data on glottalization gestures, including duration, f0, energy and autocorrelation. The same measure is used to assess regularity of vocal fold vibration in an interval just prior to the formation of the total closure for instances of syllable-final /t/, and confirms significantly lower autocorrelation in a group auditorily judged ‘pre-glottalized’. Implications are considered both for normal speech perception and for expert phonetic judgments.

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Pink, Sarah, Minna Ruckenstein, Robert Willim, and Melisa Duque. "Broken data: Conceptualising data in an emerging world." Big Data & Society 5, no. 1 (January 2018): 205395171775322. http://dx.doi.org/10.1177/2053951717753228.

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In this article, we introduce and demonstrate the concept-metaphor of broken data. In doing so, we advance critical discussions of digital data by accounting for how data might be in processes of decay, making, repair, re-making and growth, which are inextricable from the ongoing forms of creativity that stem from everyday contingencies and improvisatory human activity. We build and demonstrate our argument through three examples drawn from mundane everyday activity: the incompleteness, inaccuracy and dispersed nature of personal self-tracking data; the data cleaning and repair processes of Big Data analysis and how data can turn into noise and vice versa when they are transduced into sound within practices of music production and sound art. This, we argue is a necessary step for considering the meaning and implications of data as it is increasingly mobilised in ways that impact society and our everyday worlds.

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Destandau, Marie, and Jean-Daniel Fekete. "The missing path: Analysing incompleteness in knowledge graphs." Information Visualization 20, no. 1 (January 2021): 66–82. http://dx.doi.org/10.1177/1473871621991539.

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Knowledge Graphs (KG) allow to merge and connect heterogeneous data despite their differences; they are incomplete by design. Yet, KG data producers need to ensure the best level of completeness, as far as possible. The difficulty is that they have no means to distinguish cases where incomplete entities could and should be fixed. We present a new visualization tool: The Missing Path, to support them in identifying coherent subsets of entities that can be repaired. It relies on a map, grouping entities according to their incomplete profile. The map is coordinated with histograms and stacked charts to support interactive exploration and analysis; the summary of a subset can be compared with the one of the full collection to reveal its distinctive features. We conduct an iterative design process and evaluation with nine Wikidata contributors. Participants gain insights and find various strategies to identify coherent subsets to be fixed.

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Lippiello, Eugenio, Alessandra Cirillo, Cataldo Godano, Elefetheria Papadimitriou, and Vassilis Karakostas. "Post Seismic Catalog Incompleteness and Aftershock Forecasting." Geosciences 9, no. 8 (August 12, 2019): 355. http://dx.doi.org/10.3390/geosciences9080355.

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A growing interest appears among public authorities and society in accurate and nearly real time aftershock forecasting to manage and mitigate post-seismic risk. Existing methods for aftershock forecasting are strongly affected by the incompleteness of the instrumental datasets available soon after the main shock occurrence. The deficit of observed events, in the first part of aftershock sequences, can be naturally attributed to various mechanisms such as the inefficiency of the seismic network and the overlap of earthquake signals in seismic records. In this review, we show that short-term aftershock incompleteness can be explained only in terms of the second mechanism, whereas it is only weakly affected by the quality of the instrumental coverage. We then illustrate how standard models for earthquake forecasting can be modified to take into account this incompleteness. In particular, we focus on forecasting methods based on the data available in real time, in which many events are missing and the uncertainty in hypocenter location is considerable. We present retrospective tests that demonstrate the usefulness of these novel methods compared with traditional ones, which implement average values of parameters obtained from previous sequences.

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Gai, Yu Lian, and Ya Ping Wang. "Data Fusion and Bayes Estimation Algorithm Research." Applied Mechanics and Materials 347-350 (August 2013): 2620–24. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2620.

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This paper starts from the prospective of data processing and use of information, analysis the meaning and realistic background of processing integrated data by using Data Fusion technology. On the basis of a clear basic idea and principle theory of Data Fusion, studies and discusses its hierarchical levels from three aspects. A relatively comprehensive description of Data Fusion process is given in the paper. Incorporate with the description of the basic principles and ideas of Bayes estimation algorithm, identifies the limitation of Bayes estimation algorithm. The practical significance of Data Fusion technology in dealing with information uncertainty and incompleteness are summarized.

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A’yun Faradinna, Riskylah, and Budhi Setianto. "Description of Incompleteness of Prescription Writing at Surabaya Islamic Hospital A. Yani." KESANS : International Journal of Health and Science 2, no. 5 (February 20, 2023): 261–71. http://dx.doi.org/10.54543/kesans.v2i5.139.

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Introduction: Medication error is an event that harms patients due to incorrect use of drugs during the treatment of health workers. Medication error can lead to increased mortality or mortality, increased duration of hospitalization, and increased cost of treatment. Objective: This study aims to provide an overview of efforts to reduce the incompleteness of prescription writing at Surabaya Islamic Hospital A. Yani in each of its parts, namely inscription, invocation, precriptio, subcribtio, and pro. Method: The type of research used is qualitative case studies and secondary data analysis of patient safety incompleteness of prescription filling at Surabaya A. Yani Islamic Hospital. Data collection by observation and interview. Result and Discussion: The results showed that efforts to reduce the incompleteness of prescriptions carried out by Surabaya A. Yani Islamic Hospital using the E-prescription system. It is hoped that the existence of E-prescription can reduce the occurrence of human error and make it easier for doctors and pharmacists to provide services. Conclusion: The conclusion of this study is that the doctor's identity and the date of writing the prescription in the inscription section, the dose of the drug in the precriptio section, the doctor's paraphrase in the subcriptio section and the patient's weight in the pro section. E-prescriptions are used as an upgrade system to replace manual prescriptions. Evaluation and socialization are carried out as an effort to reduce the number of incompleteness of writing recipes

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Xia, Hanjue, Johannes Horn, Monika J. Piotrowska, Konrad Sakowski, André Karch, Hannan Tahir, Mirjam Kretzschmar, and Rafael Mikolajczyk. "Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections." PLOS Computational Biology 17, no. 5 (May 6, 2021): e1008941. http://dx.doi.org/10.1371/journal.pcbi.1008941.

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In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

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Andrade, Laerson, Eucilene Santana, Marcos Santos, and Marluce Siqueira. "Evaluation of Incompleteness and Inconsistency in the Suicide Mortality Information System: A Time Series Analysis in Espírito Santo, Brazil (2014-2019)." International Journal of Psychology and Neuroscience 9, no. 2 (August 31, 2023): 70–84. http://dx.doi.org/10.56769/ijpn09207.

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Abstract Background: Analysis of Health Systems databases is an essential process for planning, developing and evaluating health policies. This process provides evidence on the health needs of the population for public managers. Objective: To evaluate and analyze the in-completeness and inconsistency of the Suicide Mortality Information System in Espírito Santo, between 2014-2019. Methods: This is a time series study. Secondary data from the Mortality Information System on deaths by suicide between 2014-2019 were used. For de-scriptive analysis, the proportions of incompleteness and inconsistencies were used. The temporal trend analysis of incompleteness and inconsistency was performed using Prais-Winsten regression. Results: During the study period (2014-2019), there were 1,230 deaths by suicide, with the suicide mortality rate ranging from 4.55 (2014) to 6.17 (2019)/100,000 inhabitants. Variables with a growing trend of incompleteness were “Gender” (p = 0.026), “Marital status” (p = 0.010) and “Medical care” (0.027). The variables with a decreasing tendency were: “Necropsy” (p = 0.027) and “Code of the Municipality of Occurrence” (p = 0.027). The variables “Code of the Municipality of Occurrence” (p = 0.132) and “Necropsy” × “Investigation” (p = 0.177) show a constant tendency towards inconsistency. Conclusion: The study allowed the analysis of the Mortality Information System for deaths by suicide in the state of Espírito Santo. The incompleteness of information in the Mortality Information System is not homogeneous between the analyzed variables. As for the incompleteness and inconsistency of the variables, the variables “Gender”, “Age”, “Date of Birth” and “Date of Death” show excellent quality. Keywords: Health Information Management, Health Information Systems, Information Systems, Suicide.

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Kondratyeva, Liya, Irina Alekseenko, Igor Chernov, and Eugene Sverdlov. "Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism." Biology 11, no. 8 (August 12, 2022): 1208. http://dx.doi.org/10.3390/biology11081208.

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In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

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Kumari, Kimmi, and Mrunalini M. "A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data." International Journal of Information Security and Privacy 16, no. 2 (April 1, 2022): 1–13. http://dx.doi.org/10.4018/ijisp.308305.

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Data quality (DQ) is gaining traction as a new area to focus on for increasing organisational effectiveness. Despite the fact that the implications of poor data quality are often felt in the day-to-day operations of businesses, only a small percentage of companies use particular approaches for measuring and monitoring data quality. In this paper, the focus is on the efficiency and incompleteness of IOT big data and since security is the major concern in large clusters, map reduce technique is proposed in order to overcome the issues and challenges faced on regular basis while dealing with huge volume of information. Dealing with veracity is need of an hour and therefore, the work in this paper can be categorised into analysis, observation, proposing model and testing its accuracy and performance.

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Sim, Jaemun, Jonathan Sangyun Lee, and Ohbyung Kwon. "Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/538613.

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In a ubiquitous environment, high-accuracy data analysis is essential because it affects real-world decision-making. However, in the real world, user-related data from information systems are often missing due to users’ concerns about privacy or lack of obligation to provide complete data. This data incompleteness can impair the accuracy of data analysis using classification algorithms, which can degrade the value of the data. Many studies have attempted to overcome these data incompleteness issues and to improve the quality of data analysis using classification algorithms. The performance of classification algorithms may be affected by the characteristics and patterns of the missing data, such as the ratio of missing data to complete data. We perform a concrete causal analysis of differences in performance of classification algorithms based on various factors. The characteristics of missing values, datasets, and imputation methods are examined. We also propose imputation and classification algorithms appropriate to different datasets and circumstances.

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Xia, Shaobo, Dong Chen, Jiju Peethambaran, Pu Wang, and Sheng Xu. "Point Cloud Inversion: A Novel Approach for the Localization of Trees in Forests from TLS Data." Remote Sensing 13, no. 3 (January 20, 2021): 338. http://dx.doi.org/10.3390/rs13030338.

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Tree localization in point clouds of forest scenes is critical in the forest inventory. Most of the existing methods proposed for TLS forest data are based on model fitting or point-wise features which are time-consuming, sensitive to data incompleteness and complex tree structures. Furthermore, these methods often require lots of preprocessing such as ground filtering and noise removal. The fast and easy-to-use top-based methods that are widely applied in processing ALS point clouds are not applicable in localizing trees in TLS point clouds due to the data incompleteness and complex canopy structures. The objective of this study is to make the top-based methods applicable to TLS forest point clouds. To this end, a novel point cloud transformation is presented, which enhances the visual salience of tree instances and makes the top-based methods adapting to TLS forest scenes. The input for the proposed method is the raw point clouds and no other pre-processing steps are needed. The new method is tested on an international benchmark and the experimental results demonstrate its necessity and effectiveness. Finally, the proposed method has the potential to benefit other object localization tasks in different scenes based on detailed analysis and tests.

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Lestari, Dian Fadilah Ayu, and Indah Muflihatin. "Analisis Faktor Penyebab Ketidaklengkapan Rekam Medis Pasien Rawat Inap di Puskesmas Kotaanyar." J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan 2, no. 1 (December 30, 2020): 134–42. http://dx.doi.org/10.25047/j-remi.v2i1.2217.

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Based on the results of a preliminary study conducted at the Kotaanyar Public Health Center, 30 medical records of inpatients in April and May 2019 are known, which were identified based on patient identification, important reports and their authentication, the average overall number of incompleteness was 720 (53.08%). The incompleteness of the medical record has an impact which results of administrative and clinical data are not accurate, This incompleteness also creates a loss in fulfilling the patient's right to the contents of their medical record, obstruction of reporting activities and submission of claims and cause the quality of health services are low. This study aims are to analyze the factors causing incomplete medical records of inpatients, determining priority causes of problems using USG (Urgency, Seriousness, Growth) and remedial efforts are using brainstorming. This type of research uses qualitative and data collection by observation, interviews, questionnaires and documentation. The results obtained that the priority cause of the incomplete medical records problem of inpatients are there is no SOP (Standard Operational Procedure). Efforts to fix the problem are making SOP, put the SOP in the inpatient unit where it can be reached, conduct socialization at any time and renew SOP according to the SOP renewal agreement.

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Tinkler, Penny, Resto Cruz, and Laura Fenton. "Recomposing persons: Scavenging and storytelling in a birth cohort archive." History of the Human Sciences 34, no. 3-4 (March 8, 2021): 266–89. http://dx.doi.org/10.1177/0952695121995398.

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Birth cohort studies can be used not only to generate population-level quantitative data, but also to recompose persons. The crux is how we understand data and persons. Recomposition entails scavenging for various (including unrecognised) data. It foregrounds the perspective and subjectivity of survey participants, but without forgetting the partiality and incompleteness of the accounts that it may generate. Although interested in the singularity of individuals, it attends to the historical and relational embeddedness of personhood. It examines the multiple and complex temporalities that suffuse people’s lives, hence departing from linear notions of the life course. It implies involvement, as well as reflexivity, on the part of researchers. It embraces the heterogeneity and transformations over time of scientific archives and the interpretive possibilities, as well as incompleteness, of birth cohort studies data. Interested in the unfolding of lives over time, it also shines light on meaningful biographical moments.

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Hyun, Jung Soon, and Byung Kun Rhee. "On the Incompleteness of Korean Stock Index Options Market." Journal of Derivatives and Quantitative Studies 12, no. 2 (November 30, 2004): 25–43. http://dx.doi.org/10.1108/jdqs-02-2004-b0002.

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When the Black-Scholes assumptions hold market is instantaneously complete and options are redundant securities. This paper tests whether options are needed for spanning of the pricing kernel in addition to the risk-free bond and underlying asset in Korean stock index options market. Using Hansen's GMM estimation method, we find that pricing kernel cannot be spanned with the risk-free bond and underlying asset. Options are needed for spanning to incorporate the additional risk factor. This result is consistent with previous results using American options market data.

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Shaikh, Riaz Ahmed, Kamel Adi, and Luigi Logrippo. "A Data Classification Method for Inconsistency and Incompleteness Detection in Access Control Policy Sets." International Journal of Information Security 16, no. 1 (January 30, 2016): 91–113. http://dx.doi.org/10.1007/s10207-016-0317-1.

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Orlova, K. P., and V. E. Serkin. "Method for calculating the steady-state availability in case of the initial data incompleteness." Automation and informatization of the fuel and energy complex, no. 3 (2023): 61–64. http://dx.doi.org/10.33285/2782-604x-2023-3(596)-61-64.

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Sedova, N. V., and V. A. Eremkin. "Analysis of the Reliability and Comparability of Data on Rural areas in Russia Collected by Federal Executive Authorities." Machinery and Equipment for Rural Area, no. 10 (October 26, 2023): 43–48. http://dx.doi.org/10.33267/2072-9642-2023-10-43-48.

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The main point of the problem of duplication or incompleteness of data in various statistical informational systems formed at the federal level is revealed. The comprehensive list of duplications and “white spots” (absence of necessaryinformation) in the compared databases has been compiled, statistical discrepancies in data have been highlighted using a modal example, and measures have been proposed to improve the statistical record-keeping of data on rural areas.

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G.V., Suresh, and Srinivasa Reddy E.V. "Uncertain Data Analysis with Regularized XGBoost." Webology 19, no. 1 (January 20, 2022): 3722–40. http://dx.doi.org/10.14704/web/v19i1/web19245.

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Uncertainty is a ubiquitous element in available knowledge about the real world. Data sampling error, obsolete sources, network latency, and transmission error are all factors that contribute to the uncertainty. These kinds of uncertainty have to be handled cautiously, or else the classification results could be unreliable or even erroneous. There are numerous methodologies developed to comprehend and control uncertainty in data. There are many faces for uncertainty i.e., inconsistency, imprecision, ambiguity, incompleteness, vagueness, unpredictability, noise, and unreliability. Missing information is inevitable in real-world data sets. While some conventional multiple imputation approaches are well studied and have shown empirical validity, they entail limitations in processing large datasets with complex data structures. In addition, these standard approaches tend to be computationally inefficient for medium and large datasets. In this paper, we propose a scalable multiple imputation frameworks based on XGBoost, bootstrapping and regularized method. XGBoost, one of the fastest implementations of gradient boosted trees, is able to automatically retain interactions and non-linear relations in a dataset while achieving high computational efficiency with the aid of bootstrapping and regularized methods. In the context of high-dimensional data, this methodology provides fewer biased estimates and reflects acceptable imputation variability than previous regression approaches. We validate our adaptive imputation approaches with standard methods on numerical and real data sets and shown promising results.

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Hua, Wei, Xiao Juan Li, Yong Guan, Zhi Ping Shi, Jie Zhang, and Ling Ling Dong. "Formal Verification for SpaceWire Data Flow Control Using Model Checking." Applied Mechanics and Materials 241-244 (December 2012): 2466–70. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2466.

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SpaceWire is a high-speed, full-duplex serial bus standard which is applied in aerospace, so its functions require high accuracy. The traditional methods of verification, such as simulation and test, are not complete. In order to prove the design of the SpaceWire faithfully implemented the SpaceWire protocol’s specification, we presented our experience on the model checking of SpaceWire data flow control using the Cadence SMV tool. It overcomes the incompleteness of traditional verification. And by injecting the errors to ensure the accuracy of the artificial extraction properties and completeness, comparison tests show that the method can effectively ensure the functional coverage, improve reliability of the verification.

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Vicente, José, and Daniela Kubudi. "Extracting inflation risk premium from nominal and real bonds using survey information." Journal of Economic Studies 45, no. 2 (May 14, 2018): 307–25. http://dx.doi.org/10.1108/jes-03-2017-0066.

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Purpose The purpose of this paper is to forecast future inflation using a joint model of the nominal and real yield curves estimated with survey data. The model is arbitrage free and embodies incompleteness between the nominal and real bond markets. Design/methodology/approach The methodology is based on the affine class of term structure of interest rate. The model is estimated using the Kalman filter technique. Findings The authors show that the inclusion of survey data in the estimation procedure improves significantly the inflation forecasting. Moreover, the authors find that the monetary policy has significant effects on the inflation expectation and risk premium. Originality/value This paper is the first to estimate inflation using a joint model of nominal and real yield curves with Brazilian data. Moreover, the authors propose a simple arbitrage-free model that takes it account incompleteness between the nominal and real bond markets.

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Costabello, Luca, and Fabien Gandon. "Context-Aware Presentation of Linked Data on Mobile." International Journal on Semantic Web and Information Systems 10, no. 4 (October 2014): 45–76. http://dx.doi.org/10.4018/ijswis.2014100103.

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In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

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Lufianti, Siti Alifa, Rossalina Adi Wijayanti, Demiawan Rachmatta Putro Mudiono, and Indah Muflihatin. "Analisis Faktor Penyebab Ketidaklengkapan Resume Medis Rawat Inap di Puskesmas Cermee Bondowoso." Jurnal Rekam Medik & Manajemen Informasi Kesehatan 1, no. 2 (August 24, 2022): 80–86. http://dx.doi.org/10.47134/rmik.v1i2.21.

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The patient's medical resume form is a summary of the entire period of patient care and treatment as has been attempted by health workers and related parties. Based on a preliminary study in March 2021, researchers obtained data information on incomplete medical resume forms, the highest incompleteness was found in the identification component, which was 86.7%, the next incompleteness was in the Authentication component, which was 80%, the next incompleteness was in the Authentication component. important reports that is equal to 56.7%. The purpose of this study was to analyze the factors causing the incomplete filling of the patient's medical resume form. This type of research uses qualitative research, the subject of this research consists of 1 head of puskesmas, 3 inpatient doctors, and 1 medical record officer, the object of this research is to use an inpatient medical resume form to determine the factors that cause incomplete medical resume forms. inpatient. The results found in this study are that the first priority is that there is no SOP (Standard Operational Procedure) regarding filling out medical resumes, so it is determined efforts to make SOPs for filling out medical resumes in accordance with medical record service standards

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Miloslavsky, V. "Syphilis as an occupational disease of doctors." Kazan medical journal 20, no. 2 (August 11, 2021): 219. http://dx.doi.org/10.17816/kazmj76336.

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On the basis of the literature data Trb (Ref. Znb. F. Hyg., Bd. IV, S. J224) believes that there are 33 cases of professional syphilis among doctors for 1000 cases of non-sexual syphilis. Due to the incompleteness of the data, these figures are, of course, only approximate.

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Zhao, Yan Li, Xiao Zun Ma, Xiang Dong Gao, Lei Yun, and Shu Qin Fu. "Data Fusion Algorithm for Asynchronous Radar Networks under Blanket Jamming." Applied Mechanics and Materials 157-158 (February 2012): 1446–52. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1446.

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Owing to the incompleteness and asynchronicity of the observation data, it is hard for the asynchronous netted radar to track its target effectively under intense blanket jamming. This paper presents a kind of data fusion algorithm for the asynchronous sensors based on optimal linearization approach, providing a solution to the problem of target tracking of radar networks under blanket jamming. First, the optimal linearization processing technique of the observation equation is derived. Then, based on this, the state vector of the target is initially estimated in batch mode. Finally, the filtering is processed in sequence. The simulation results show that the data fusion algorithm presented in the paper can track the target in high accuracy.

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Wang, Chien-Chih. "Special Issue on Recent Advances in Machine Learning and Applications." Processes 10, no. 11 (November 16, 2022): 2411. http://dx.doi.org/10.3390/pr10112411.

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Digital technologies for cyber-physical systems are rapidly advancing, and the ubiquity of the Internet of Things (IoT) has created significant challenges for academic, industrial, and service applications due to high dimensionality, noise contamination, incompleteness, inconsistency, and massive amounts of data [...]

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Simonov, A. A., and K. V. Tikhonova. "PROBLEM OF INCOMPLETENESS OF DATA OF INFORMATION DATABANK OF THE UNIFORM STATE IMMOVABLE PROPERTY CADASTRE." Economy and ecology of territorial educations, no. 3 (2017): 123–28. http://dx.doi.org/10.23947/2413-1474-2017-3-123-128.

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Richtler, T., R. Sagar, A. Vallenari, and Klaas S. De Boer. "Effects of data incompleteness and metallicity on the mass functions of young LMC star clusters." Symposium - International Astronomical Union 148 (1991): 222–23. http://dx.doi.org/10.1017/s0074180900200454.

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The young globular clusters in the Magellanic Clouds offer a good number statistic and a reasonably wide mass interval which are required for the derivation of any statistically reliable slope of the Initial Mass Function (IMF). Elson et al. (1989) and Mateo (1988) are amongst those few who utilized this potential first. These authors, however, arrive at different conclusions. Elson et al. find quite flat mass function slopes in comparison with the values given by Mateo. Here we present IMF slopes based on B, V CCD photometry for four young LMC clusters, NGC 1711, 2004, 2164 and 2214 and discuss the effects on them of cluster metallicity and of uncertainties in the incompleteness of the data.

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Journal articles on the topic 'Data incompleteness'

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