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Journal articles on the topic 'Clinical data structure'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kulikov, E. S., O. S. Fedorova, I. V. Tolmachev, et al. "Russian-language repository of the open clinical data “SibMED Data Clinical Repository”." Bulletin of Siberian Medicine 22, no. 2 (2023): 182–84. http://dx.doi.org/10.20538/1682-0363-2023-2-182-184.

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The global digitalization has become one of the most significant challenges in the field of medicine and healthcare. The rapid development of digital technologies determines the growing demand for constant access to big data in real time Their use is in need for research and technological projects in the sphere of artificial intelligence technologies development. Siberian State Medical University developed the first Russian-language repository of clinical data “SibMed Data Clinical Repository” (https://dataset.ssmu.ru/). The article describes the structure, functions of the repository, and per

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O. P. Kuchuk, D. V. Pronyayev, and T. B. Sykyrytsʹka. "CLINICAL ANATOMY OF THE EYE SOCKET." Clinical anatomy and operative surgery 17, no. 2 (2018): 84–93. http://dx.doi.org/10.24061/1727-0847.17.2.2018.15.

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Scientific literature review deals with the issues concerning the peculiarities of the eye socket clinical anatomy. Both common data concerning the peculiarities of the eye socket structure and the results of certain modern studies have been analyzed and structured, which will be useful for both students, teachers and practicing physicians. All the names of anatomical structures are given according to the International Anatomical Nomenclature in the Ukrainian language with Latin equivalents.

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Saver, Jeffrey L., Steven Warach, Scott Janis, et al. "Standardizing the Structure of Stroke Clinical and Epidemiologic Research Data." Stroke 43, no. 4 (2012): 967–73. http://dx.doi.org/10.1161/strokeaha.111.634352.

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Makai, Diána, András Cseh, Adél Sepsi, and Szabolcs Makai. "A Multigraph-Based Representation of Hi-C Data." Genes 13, no. 12 (2022): 2189. http://dx.doi.org/10.3390/genes13122189.

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Chromatin–chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements function within the larger genomic context, and how the genome reacts to environmental stimuli, the linear sequence information needs to be interpreted in three-dimensional space, which is still a challenging task. Here, we propose a novel, heuristic approach to represent Hi-C datasets by a whole-genomic pseudo-structure in 3D space. The baseline of our a

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Klein, Arno, and Satrajit Ghosh. "Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data." Research Ideas and Outcomes 2 (April 21, 2016): e8835. https://doi.org/10.3897/rio.2.e8835.

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The proposed research develops new computational tools to identify, diagnose, and predict treatment outcome for different mental illnesses. The research will be applied first to major depressive disorder, which affects millions of Americans, but is intended to be applied to any mental illness, such as Alzheimer's disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain structure, activity, or connectivity between any two populations.

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Rohr, Nadja, Blerta Hoda, and Jens Fischer. "Surface Structure of Zirconia Implants: An Integrative Review Comparing Clinical Results with Preclinical and In Vitro Data." Materials 15, no. 10 (2022): 3664. http://dx.doi.org/10.3390/ma15103664.

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Background: The purpose of this review was to analyze and correlate the findings for zirconia implants in clinical, preclinical and in vitro cell studies in relation to surface structure. Methods: Electronic searches were conducted to identify clinical, preclinical and in vitro cell studies on zirconia implant surfaces. The primary outcomes were mean bone loss (MBL) for clinical studies, bone-to-implant contact (BIC) and removal torque (RT) for preclinical studies and cell spreading, cell proliferation and gene expression for cell studies. The secondary outcomes included comparisons of data fo

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Chiu, Alec M., Erin K. Molloy, Zilong Tan, Ameet Talwalkar, and Sriram Sankararaman. "Inferring population structure in biobank-scale genomic data." American Journal of Human Genetics 109, no. 4 (2022): 727–37. http://dx.doi.org/10.1016/j.ajhg.2022.02.015.

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8

Hoverman, J. Russell. "So Much Data, So Little Knowledge: Using Formal Logic to Aggregate Data and Interpret Information." American Society of Clinical Oncology Educational Book, no. 38 (May 2018): e2-e5. http://dx.doi.org/10.1200/edbk_209527.

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Formal logic is the use of symbols to structure how we make inferences such that the structure can be applied beyond specific cases. The structure of clinical trials and the values equation (value = outcomes/cost) lend themselves to evaluation using formal logic. Doing this demonstrates the unique position of randomized clinical trials as a defensible format for gathering information and identifies the shortcomings of historical controls and meta-analyses and retrospective studies using large databases. A practical example using literature on maintenance therapy in metastatic colorectal cancer

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Astrid, M. "The Structure of Data in Medical Records." Yearbook of Medical Informatics 04, no. 01 (1995): 61–70. http://dx.doi.org/10.1055/s-0038-1638021.

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Abstract:With the growing complexity of health care, patient data are more and more in demand for purposes such as research, education, postmarketing surveillance, quality assessment, and outcome analysis. Many of these purposes require patient data to be available in a structured, electronic format. Despite the rapid advances in computer technology, which allow patient data to be organized, analyzed, and shared, the majority of physicians still use paper medical records. Apparently, most physicians still perceive the paper record as being more suitable for their task than present day computer

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Joseph, Tyler A., and Itsik Pe’er. "Inference of Population Structure from Time-Series Genotype Data." American Journal of Human Genetics 105, no. 2 (2019): 317–33. http://dx.doi.org/10.1016/j.ajhg.2019.06.002.

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11

Khedr, Dr Ayman E., and Fahad Kamal Alsheref. "A Proposed Electronic Health Record Content Structure Based on Clinical Organizations Survey." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 13 (2014): 5233–46. http://dx.doi.org/10.24297/ijct.v15i13.5283.

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Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. Several electronic health records systems were implemented in different states with different clinical data structures that prevent data exchange between systems even in the same state. This leads to the important barrier in implementing EHR system which is the lack of standards of EHR clinical data structure. In this paper we made a survey on several in in

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König, A. "Data structure and computer program for fast interactive searches of clinical diagnoses." Medical Informatics 18, no. 1 (1993): 45–51. http://dx.doi.org/10.3109/14639239309034467.

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Royall, Donald R., Laura K. Chiodo, and Marsha J. Polk. "Executive dyscontrol in normal aging: Normative data, factor structure, and clinical correlates." Current Neurology and Neuroscience Reports 3, no. 6 (2003): 487–93. http://dx.doi.org/10.1007/s11910-003-0052-7.

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Tapuria, Archana, Philipp Bruland, Brendan Delaney, Dipak Kalra, and Vasa Curcin. "Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data." DIGITAL HEALTH 4 (January 2018): 205520761877767. http://dx.doi.org/10.1177/2055207618777676.

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Objectives Integrating Electronic Health Record (EHR) systems into the field of clinical trials still contains several challenges and obstacles. Heterogeneous standards and specifications are used to represent healthcare and clinical trial information. Therefore, this work investigates the mapping and data interoperability between healthcare and research standards: EN13606 used for the EHRs and the Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) used for clinical research. Methods Based on the specifications of CDISC ODM 1.3.2 and EN13606, a mapping between th

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Azadi, Ali, and Francisco José García-Peñalvo. "Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality." Applied Sciences 15, no. 10 (2025): 5273. https://doi.org/10.3390/app15105273.

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Electronic medical records (EMRs) are fundamental to clinical decision support systems (CDSS). Conventional EMR structures still fail to capture the cyclical nature of treatment plans, leading to fragmented data representation and reduced decision accuracy. This study addresses this gap by proposing a cycle-based EMR framework that systematically integrates treatment cycles, enabling structured, sequential data organization. Treatment cycles categorize patient data into iterative phases, reflecting disease progression and repeated interventions, ensuring data continuity and analytical precisio

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Holub, Karl, Nicole Hardy, and Kevin Kallmes. "Toward Automated Data Extraction According to Tabular Data Structure: Cross-sectional Pilot Survey of the Comparative Clinical Literature." JMIR Formative Research 5, no. 11 (2021): e33124. http://dx.doi.org/10.2196/33124.

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Background Systematic reviews depend on time-consuming extraction of data from the PDFs of underlying studies. To date, automation efforts have focused on extracting data from the text, and no approach has yet succeeded in fully automating ingestion of quantitative evidence. However, the majority of relevant data is generally presented in tables, and the tabular structure is more amenable to automated extraction than free text. Objective The purpose of this study was to classify the structure and format of descriptive statistics reported in tables in the comparative medical literature. Methods

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Colquhoun, Douglas A., Allison M. Janda, Graciela Mentz, et al. "Accounting for Healthcare Structures When Measuring Variation in Care." Anesthesiology 142, no. 5 (2025): 793–805. https://doi.org/10.1097/aln.0000000000005395.

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Health services research frequently focuses on variation in the structure, process, and outcomes of clinical care. Robust approaches for detection and attribution of variation are foundational to both quality improvement and outcomes research. Describing care in structured healthcare systems across hospitals in which clinicians work to provide care for patients as a multileveled structure allows the impact of organization on practice and outcome to be ascertained. Mixed-effect statistical models can describe both the partitioning of variation among levels of these structures and by inclusion o

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18

Watts, Fraser N., Tim Dalgleish, Patrick Bourke, and David Healy. "Memory deficit in clinical depression: processing resources and the structure of materials." Psychological Medicine 20, no. 2 (1990): 345–49. http://dx.doi.org/10.1017/s0033291700017657.

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SynopsisResource theory predicts that the relative memory deficit shown by depressed patients should be greater with unstructured than structured material. Previous data using semantic categories word lists supports this, but lists approximating to text have produced the opposite result. Both types of structure were studied in this experiment. The prediction from resource theory was found to hold only when comparing medium and high levels of structure, and to hold more clearly for word lists approximating to text than for semantic categories lists. When word lists of low and medium levels of s

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Kuntz, Richard E. "The Changing Structure of Industry-Sponsored Clinical Research: Pioneering Data Sharing and Transparency." Annals of Internal Medicine 158, no. 12 (2013): 914. http://dx.doi.org/10.7326/0003-4819-158-12-201306180-00011.

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Jacobson, Lisa A., Luther G. Kalb, and E. Mark Mahone. "When theory met data: Factor structure of the BRIEF2 in a clinical sample." Clinical Neuropsychologist 34, no. 1 (2019): 243–58. http://dx.doi.org/10.1080/13854046.2019.1571634.

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21

Helms, Ronald W., and Imogene McCanless. "The conflict between relational databases and the hierarchical structure of clinical trials data." Controlled Clinical Trials 11, no. 1 (1990): 7–23. http://dx.doi.org/10.1016/0197-2456(90)90028-z.

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Acosta, Maria T., and Phillip L. Pearl. "Imaging data in autism: From structure to malfunction." Seminars in Pediatric Neurology 11, no. 3 (2004): 205–13. http://dx.doi.org/10.1016/j.spen.2004.07.004.

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23

Azhar, Tayyaba, Maimoona Nasreen, Khizar Ansar Malik, Kinza Aslam, Sara Mukhtar, and Saima Rafique. "Observing the Objective Structured Clinical Exam: A Deeper Dive into Structured Clinical Examination." Pakistan Armed Forces Medical Journal 73, no. 6 (2023): 1852–57. http://dx.doi.org/10.51253/pafmj.v73i6.10878.

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Objective: To explore the perceptions of students and examiners regarding objective structured clinical examination (OSCE). Study Design: Cross-sectional study. Place and Duration of Study: University College of Medicine & Dentistry, Lahore Pakistan from Dec to Apr 2023. Methodology: Data was collected through Google Forms, which served as a secure and user-friendly platform for survey administration. Data was collected from the students who were enrolled in the integrated curriculum. The research involved administering a pre-validated questionnaire to 40 examiners and 272 fourth- and fina

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Li, Yan, Gen Li, and Kun Chen. "Principal Amalgamation Analysis for Microbiome Data." Genes 13, no. 7 (2022): 1139. http://dx.doi.org/10.3390/genes13071139.

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In recent years microbiome studies have become increasingly prevalent and large-scale. Through high-throughput sequencing technologies and well-established analytical pipelines, relative abundance data of operational taxonomic units and their associated taxonomic structures are routinely produced. Since such data can be extremely sparse and high dimensional, there is often a genuine need for dimension reduction to facilitate data visualization and downstream statistical analysis. We propose Principal Amalgamation Analysis (PAA), a novel amalgamation-based and taxonomy-guided dimension reductio

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25

Gealy, Robert, Cynthia Graham, Nancy B. Sussman, Orest T. Macina, Herbert S. Rosenkranz, and Meryl H. Karol. "Evaluating clinical case report data for SAR modeling of allergic contact dermatitis." Human & Experimental Toxicology 15, no. 6 (1996): 489–93. http://dx.doi.org/10.1177/096032719601500605.

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Clinical case reports can be important sources of information for alerting health professionals to the existence of possible health hazards. Isolated case reports, however, are weak evidence of causal relationships between exposure and disease because they do not provide an indication of the frequency of a particular exposure leading to a disease event. A database of chemicals causing allergic contact dermatitis (ACD) was compiled to discern structure-activity relationships. Clinical reports repre sented a considerable fraction of the data. Multiple Computer Automated Structure Evaluation (Mul

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Ali, Sarwan, Prakash Chourasia, and Murray Patterson. "When Protein Structure Embedding Meets Large Language Models." Genes 15, no. 1 (2023): 25. http://dx.doi.org/10.3390/genes15010025.

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Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machine learning algorithms to categorize structures based on data from databases like the Protein Data Bank (PDB). To predict protein functions, embeddings based on protein sequences have been employed. Creating numerical embeddings that preserve vital information while considering protein structure and sequence presents several challenges. The existing literature

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Radecki, Pierce, Mirko Ledda, and Sharon Aviran. "Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures." Genes 9, no. 6 (2018): 300. http://dx.doi.org/10.3390/genes9060300.

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Greenbaum, Gili, Amir Rubin, Alan R. Templeton, and Noah A. Rosenberg. "Network-based hierarchical population structure analysis for large genomic data sets." Genome Research 29, no. 12 (2019): 2020–33. http://dx.doi.org/10.1101/gr.250092.119.

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Bernasconi, Anna, and Stefano Ceri. "Interoperability of COVID-19 Clinical Phenotype Data with Host and Viral Genetics Data." BioMed 2, no. 1 (2022): 69–81. http://dx.doi.org/10.3390/biomed2010007.

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The outbreak of the COVID-19 epidemic has focused enormous attention on the genetics of viral infection and related disease. Since the beginning of the pandemic, we focused on the collection and integration of SARS-CoV-2 databases, which contain information on the structure of the virus and on its ability to spread, mutate, and evolve; data are made available from several open-source databases. In the past, we gathered experience on human genomics data by building models and integrated databases of genomic datasets (representing, e.g., mutations, gene expression profiles, epigenetic signals).

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Moore, Joseph A., Wuyang Yang, Kim Evans, et al. "Clinical deployment of automatic treatment planning for pancreas SBRT patients." Journal of Clinical Oncology 32, no. 3_suppl (2014): 219. http://dx.doi.org/10.1200/jco.2014.32.3_suppl.219.

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219 Background: To clinically utilize an SQL relational database of prior treated patients to generate objectives for future treatment plans. A database approach allows for more rapid planning by starting with a better initial solution and improves safety by providing good known achievable dose values for the initial optimization. The use of a database allows for trending of dose, structure and toxicity data. Methods: A database of fifty-three patients from three institutions is populated with dose and structure data via an automatic script within the treatment planning system. For each new pa

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Zhou, Fei, Jie Ren, Yuwen Liu, Xiaoxi Li, Weiqun Wang, and Cen Wu. "Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data." Genes 13, no. 3 (2022): 544. http://dx.doi.org/10.3390/genes13030544.

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We introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties.

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Herman, Peter, Ikuhiro Kida, Basavaraju G. Sanganahalli, Fahmeed Hyder, and Andras Eke. "Fractal correlation structure in fMRI data of rat brain." Journal of Cerebral Blood Flow & Metabolism 25, no. 1_suppl (2005): S379. http://dx.doi.org/10.1038/sj.jcbfm.9591524.0379.

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Rosenbloom, S. T., J. C. Denny, H. Xu, N. Lorenzi, W. W. Stead, and K. B. Johnson. "Data from clinical notes: a perspective on the tension between structure and flexible documentation." Journal of the American Medical Informatics Association 18, no. 2 (2011): 181–86. http://dx.doi.org/10.1136/jamia.2010.007237.

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Cole, David A., Li Cai, Nina C. Martin, et al. "Structure and measurement of depression in youths: Applying item response theory to clinical data." Psychological Assessment 23, no. 4 (2011): 819–33. http://dx.doi.org/10.1037/a0023518.

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Luo, Dan, Wenwei Liu, Tian Chen, and Lingling An. "A Distribution-Free Model for Longitudinal Metagenomic Count Data." Genes 13, no. 7 (2022): 1183. http://dx.doi.org/10.3390/genes13071183.

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Longitudinal metagenomics has been widely studied in the recent decade to provide valuable insight for understanding microbial dynamics. The correlation within each subject can be observed across repeated measurements. However, previous methods that assume independent correlation may suffer from incorrect inferences. In addition, methods that do account for intra-sample correlation may not be applicable for count data. We proposed a distribution-free approach, namely CorrZIDF, which extends the current method to model correlated zero-inflated metagenomic count data, offering a powerful and acc

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Ferraz, Camila R., Thacyana T. Carvalho, Marília F. Manchope, et al. "Therapeutic Potential of Flavonoids in Pain and Inflammation: Mechanisms of Action, Pre-Clinical and Clinical Data, and Pharmaceutical Development." Molecules 25, no. 3 (2020): 762. http://dx.doi.org/10.3390/molecules25030762.

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Pathological pain can be initiated after inflammation and/or peripheral nerve injury. It is a consequence of the pathological functioning of the nervous system rather than only a symptom. In fact, pain is a significant social, health, and economic burden worldwide. Flavonoids are plant derivative compounds easily found in several fruits and vegetables and consumed in the daily food intake. Flavonoids vary in terms of classes, and while structurally unique, they share a basic structure formed by three rings, known as the flavan nucleus. Structural differences can be found in the pattern of subs

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Bychko, Dmytro, Vira Shendryk, and Yuliia Parfenenko. "The method of primary processing of poorly structured medical data." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 8 (December 5, 2020): 1–10. http://dx.doi.org/10.23939/sisn2020.08.001.

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The article deals with the approach to the primary processing of poorly structured medical protocol textual data stored and disseminated as pdf files. The relevance of this work is due to the lack of a universal structure for the presentation of medical protocols and methods of their processing. In the course of the work, the problem of primary processing of clinical protocol data was solved by the example of a unified clinical protocol of primary, secondary (specialized) and tertiary (highly specialized) medical care. The method of primary data processing was developed to create a clear struc

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Pisanelli, D. M., F. L. Ricci, F. Consorti, A. Piermattei, and F. Ferri. "Toward a General Model for the Description of Multimedia Clinical Data." Methods of Information in Medicine 37, no. 03 (1998): 278–84. http://dx.doi.org/10.1055/s-0038-1634537.

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AbstractThe patient folder integrates information originating from heterogeneous sources. For this reason computerized tools for patient data management should exploit the advantages of multimediality and offer an integrated environment for data presentation, and image and biosignal visualization. Object-oriented modeling is the best approach for designing systems for multimedia patient folder management.We propose an object-oriented model, able to define the entities constituting the patient folder and their logical organization. This model has sufficient flexibility to adapt to the most vari

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Djaković Sekulić, Tatjana, Gyongyi Vastag, Kristina Tot, Jadranka Tot, and Anita Lazić. "Quantitative structure—retention relationships modeling and multivariate data analysis of lipophilicity data of new spirohydantoin derivatives." JPC - Journal of Planar Chromatography - Modern TLC 29, no. 4 (2016): 281–86. http://dx.doi.org/10.1556/1006.2016.29.4.6.

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Kühl, J., J. Grocholl, A. Seekamp, T. Klüter, and S. Fuchs. "PATIENT-SPECIFIC TIBIAL IMPLANTS: ALGORITHMIC DESIGN PROCESSES FOR CLINICAL CASES." Orthopaedic Proceedings 106-B, SUPP_18 (2024): 82. http://dx.doi.org/10.1302/1358-992x.2024.18.082.

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IntroductionThe surgical treatment of critical-sized bone defects with complex three-dimensional (3D) geometries is a challenge for the treating surgeon. Additive manufacturing such as 3D printing enables the production of highly individualized bone implants meeting the shape of the patient's bone defect and including a tunable internal structure. In this study, we showcase the design process for patient-specific implants with critical-sized tibia defects.MethodsTwo clinical cases of patients with critical tibia defects (size 63×20×21 mm and 50×24×17 mm) were chosen. Brainlab software was used

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Lawson, Daniel John, Garrett Hellenthal, Simon Myers, and Daniel Falush. "Inference of Population Structure using Dense Haplotype Data." PLoS Genetics 8, no. 1 (2012): e1002453. http://dx.doi.org/10.1371/journal.pgen.1002453.

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Lukas, Arno, and Bernd Mayer. "Data annotation and relations modeling for integrated omics in clinical research." IIOAB Journal 1, no. 2 (2010): 15–23.

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Omics has massively permeated translational clinical research with numerous diseases being covered by Omics studies from the genome to the metabolome level. Integrating these disease specific Omics tracks appears a logical next step for building the fundament of Systems Biology and Systems Medicine. Here, coherence of individual Omics tracks regarding clinical hypothesis, samples and clinical descriptors, and finally data handling and integration become pivotal. We present a data integration, annotation and relations modeling concept for heterogeneous Omics data and workflows. With molecular f

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Rakhmiddin, Ruzaliev, and KangYoon Lee. "Federated Learning for Clinical Event Classification Using Vital Signs Data." Multimodal Technologies and Interaction 7, no. 7 (2023): 67. http://dx.doi.org/10.3390/mti7070067.

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Accurate and timely diagnosis is a pillar of effective healthcare. However, the challenge lies in gathering extensive training data while maintaining patient privacy. This study introduces a novel approach using federated learning (FL) and a cross-device multimodal model for clinical event classification based on vital signs data. Our architecture employs FL to train several machine learning models including random forest, AdaBoost, and SGD ensemble models on vital signs data. The data were sourced from a diverse clientele at a Boston hospital (MIMIC-IV dataset). The FL structure trains direct

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De Bin, Riccardo, Anne-Laure Boulesteix, Axel Benner, Natalia Becker, and Willi Sauerbrei. "Combining clinical and molecular data in regression prediction models: insights from a simulation study." Briefings in Bioinformatics 21, no. 6 (2019): 1904–19. http://dx.doi.org/10.1093/bib/bbz136.

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Abstract Data integration, i.e. the use of different sources of information for data analysis, is becoming one of the most important topics in modern statistics. Especially in, but not limited to, biomedical applications, a relevant issue is the combination of low-dimensional (e.g. clinical data) and high-dimensional (e.g. molecular data such as gene expressions) data sources in a prediction model. Not only the different characteristics of the data, but also the complex correlation structure within and between the two data sources, pose challenging issues. In this paper, we investigate these i

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Pflesser, B., A. Pommert, M. Riemer, et al. "A Realistic Model of Human Structure from the Visible Human Data." Methods of Information in Medicine 40, no. 02 (2001): 83–89. http://dx.doi.org/10.1055/s-0038-1634481.

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AbstractThe computer-based 3D models of the human body reported to date suffer from poor spatial resolution. The Visible Human project has delivered high resolution cross-sectional images that are suited for generation of high-quality models. Yet none of the 3D models described to date reflect the quality of the original images. We present a method of segmentation and visualization which provides a new quality of realism and detail. Using the example of a 3D model of the inner organs, we demonstrate that such models, especially when combined with a knowledge base, open new possibilities for sc

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Pecoraro, Fabrizio, Daniela Luzi, and Fabrizio L. Ricci. "Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes." ACI Open 03, no. 01 (2019): e44-e62. http://dx.doi.org/10.1055/s-0039-1688936.

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Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiauto

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Göksel Duru, Dilek, and Mehmed Özkan. "Determination of Neural Fiber Connections Based on Data Structure Algorithm." Computational Intelligence and Neuroscience 2010 (2010): 1–6. http://dx.doi.org/10.1155/2010/251928.

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The brain activity during perception or cognition is mostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single

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Bi, Yuhe, Xinju Liu, Wei Li, JiaCheng Xu, Jie Xi, and Shengchao Wei. "Clinical Data and Biocalculation Methods of GABRD Determine the Clinical Characteristics and Immune Relevance of Colorectal Cancer." Evidence-Based Complementary and Alternative Medicine 2022 (June 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/6198448.

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Background. The aim of this study was to clarify the expression of gamma-aminobutyric acid type A receptor delta subunit (GABRD) gene in pan-cancer and its correlation with patient prognosis, and to investigate the function and possible mechanism of GABRD in colorectal cancer (CRC). Methods. The Cancer Genome Atlas (TCGA) data were used to analyze the expression differences of GABRD in pan-cancer, and the correlation between GABRD and clinical prognosis of various tumors was analyzed by Cox regression method. According to the expression level of GABRD, Gene Function Annotation (GO) and Kyoto E

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Hu, Dianlin, Yikun Zhang, Wangyao Li, et al. "SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data." IEEE Transactions on Instrumentation and Measurement 72 (2023): 1–13. http://dx.doi.org/10.1109/tim.2023.3318712.

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Hu, D., Y. Zhang, W. Li, et al. "SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data." International Journal of Radiation Oncology*Biology*Physics 117, no. 2 (2023): S178—S179. http://dx.doi.org/10.1016/j.ijrobp.2023.06.2523.

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