84 research outputs found
Decision Support Systems
Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference
An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer
Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts
the health and productivity of individuals and society as a whole. Reasons for medication
non-adherence are multi-faced, with no clear-cut solution. Adherence to medication
remains a difficult area to study, due to inconsistencies in representing medicationadherence
behavior data that poses a challenge to humans and today’s computer
technology related to interpreting and synthesizing such complex information.
Developing a consistent conceptual framework to medication adherence is needed to
facilitate domain understanding, sharing, and communicating, as well as enabling
researchers to formally compare the findings of studies in systematic reviews.
The goal of this research is to create a common language that bridges human and
computer technology by developing a controlled structured vocabulary of medication
adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology)
using breast cancer as a case study to inform and evaluate the proposed ontology and
demonstrating its application to real-world situation. The intention is for MAB-Ontology
to be developed against the background of a philosophical analysis of terms, such as
belief, and desire to be human, computer-understandable, and interoperable with other
systems that support scientific research.
The design process for MAB-Ontology carried out using the METHONTOLOGY
method incorporated with the Basic Formal Ontology (BFO) principles of best practice.
This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including
adherence assessment, adherence determinants, adherence theories, adherence
taxonomies, and tacit knowledge source types. These sources were analyzed using a
systematic approach that involved some questions applied to all source types to guide
data extraction and inform domain conceptualization. A set of intermediate
representations involving tables and graphs was used to allow for domain evaluation
before implementation. The resulting ontology included 629 classes, 529 individuals, 51
object property, and 2 data property.
The intermediate representation was formalized into OWL using Protégé. The
MAB-Ontology was evaluated through competency questions, use-case scenario, face
validity and was found to satisfy the requirement specification. This study provides a
unified method for developing a computerized-based adherence model that can be
applied among various disease groups and different drug categories
Efficient Decision Support Systems
This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers
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Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias
A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators.
This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator.
We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service
Program and Proceedings: The Nebraska Academy of Sciences 1880-2013
PROGRAM
FRIDAY, APRIL 19, 2013
REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall
Aeronautics and Space Science, Session A, Olin 249
Aeronautics and Space Science, Session B, Olin 224
Collegiate Academy, Biology Session A, Olin B
Biological and Medical Sciences, Session A, Olin 112
Biological and Medical Sciences, Session B, Smith Callen Conference Center
NE Chapter, Nat\u27l Council For Geographic Education, Olin 325
Junior Academy, Judges Check-In, Olin 219
Junior Academy, Senior High REGISTRATION, Olin Hall Lobby
Chemistry and Physics, Section A, Chemistry, Olin A
Chemistry and Physics, Section B, Physics, Planetarium
Collegiate Academy, Chemistry and Physics, Session A, Olin 324
Junior Academy, Senior High Competition, Olin 124, Olin 131
Aeronautics and Space Science, Poster Session, Olin 249
Anthropology, Olin 111
NWU Health and Sciences Graduate School Fair, Olin and Smith Curtiss Halls
Aeronautics and Space Science, Poster Session, Olin 249
MAIBEN MEMORIAL LECTURE, OLIN B
Bob Feurer, North Bend High School, Making People Smarter Using Habits of Mind
LUNCH, PATIO ROOM, STORY STUDENT CENTER
(pay and carry tray through cafeteria line, or pay at NAS registration desk)
Aeronautics Group, Sunflower Room
Biological and Medical Sciences, Session C, Olin 112
Biological and Medical Sciences, Session D, Smith Callen Conference Center
Chemistry and Physics, Section A, Chemistry, Olin A
Collegiate Academy, Biology Session A, Olin B
Collegiate Academy, Biology Session B, Olin 249
Collegiate Academy, Chemistry and Physics, Session B, Olin 324
Junior Academy, Judges Check-In, Olin 219
Junior Academy, Junior High REGISTRATION, Olin Hall Lobby
Junior Academy, Senior High Competition, (Final), Olin 110
Anthropology, Olin 111
Teaching of Science and Math, Olin 224
Applied Science and Technology, Olin 325
Junior Academy, Junior High Competition, Olin 124, Olin 131
NJAS Board/Teacher Meeting, Olin 219
BUSINESS MEETING, OLIN B
AWARDS RECEPTION for NJAS, Scholarships, Members, Spouses, and Guests
First United Methodist Church, 2723 N 50th Street, Lincoln, N
Large space structures and systems in the space station era: A bibliography with indexes (supplement 05)
Bibliographies and abstracts are listed for 1363 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and July 31, 1992. Topics covered include technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion and solar power satellite systems
Computational Tools for the Untargeted Assignment of FT-MS Metabolomics Datasets
Metabolomics is the study of metabolomes, the sets of metabolites observed in living systems. Metabolism interconverts these metabolites to provide the molecules and energy necessary for life processes. Many disease processes, including cancer, have a significant metabolic component that manifests as differences in what metabolites are present and in what quantities they are produced and utilized. Thus, using metabolomics, differences between metabolomes in disease and non-disease states can be detected and these differences improve our understanding of disease processes at the molecular level. Despite the potential benefits of metabolomics, the comprehensive investigation of metabolomes remains difficult.
A popular analytical technique for metabolomics is mass spectrometry. Advances in Fourier transform mass spectrometry (FT-MS) instrumentation have yielded simultaneous improvements in mass resolution, mass accuracy, and detection sensitivity. In the metabolomics field, these advantages permit more complicated, but more informative experimental designs such as the use of multiple isotope-labeled precursors in stable isotope-resolved metabolomics (SIRM) experiments.
However, despite these potential applications, several outstanding problems hamper the use of FT-MS for metabolomics studies. First, artifacts and data quality problems in FT-MS spectra can confound downstream data analyses, confuse machine learning models, and complicate the robust detection and assignment of metabolite features. Second, the assignment of observed spectral features to metabolites remains difficult. Existing targeted approaches for assignment often employ databases of known metabolites; however, metabolite databases are incomplete, thus limiting or biasing assignment results. Additionally, FT-MS provides limited structural information for observed metabolites, which complicates the determination of metabolite class (e.g. lipid, sugar, etc. ) for observed metabolite spectral features, a necessary step for many metabolomics experiments.
To address these problems, a set of tools were developed. The first tool identifies artifacts with high peak density observed in many FT-MS spectra and removes them safely. Using this tool, two previously unreported types of high peak density artifact were identified in FT-MS spectra: fuzzy sites and partial ringing. Fuzzy sites were particularly problematic as they confused and reduced the accuracy of machine learning models trained on datasets containing these artifacts. Second, a tool called SMIRFE was developed to assign isotope-resolved molecular formulas to observed spectral features in an untargeted manner without a database of expected metabolites. This new untargeted method was validated on a gold-standard dataset containing both unlabeled and 15N-labeled compounds and was able to identify 18 of 18 expected spectral features. Third, a collection of machine learning models was constructed to predict if a molecular formula corresponds to one or more lipid categories. These models accurately predict the correct one of eight lipid categories on our training dataset of known lipid and non-lipid molecular formulas with precisions and accuracies over 90% for most categories.
These models were used to predict lipid categories for untargeted SMIRFE-derived assignments in a non-small cell lung cancer dataset. Subsequent differential abundance analysis revealed a sub-population of non-small cell lung cancer samples with a significantly increased abundance in sterol lipids. This finding implies a possible therapeutic role of statins in the treatment and/or prevention of non-small cell lung cancer. Collectively these tools represent a pipeline for FT-MS metabolomics datasets that is compatible with isotope labeling experiments. With these tools, more robust and untargeted metabolic analyses of disease will be possible
pHealth 2021. Proc. of the 18th Internat. Conf. on Wearable Micro and Nano Technologies for Personalised Health, 8-10 November 2021, Genoa, Italy
Smart mobile systems – microsystems, smart textiles, smart implants, sensor-controlled medical devices – together with related body, local and wide-area networks up to cloud services, have become important enablers for telemedicine and the next generation of healthcare services. The multilateral benefits of pHealth technologies offer enormous potential for all stakeholder communities, not only in terms of improvements in medical quality and industrial competitiveness, but also for the management of healthcare costs and, last but not least, the improvement of patient experience.
This book presents the proceedings of pHealth 2021, the 18th in a series of conferences on wearable micro and nano technologies for personalized health with personal health management systems, hosted by the University of Genoa, Italy, and held as an online event from 8 – 10 November 2021. The conference focused on digital health ecosystems in the transformation of healthcare towards personalized, participative, preventive, predictive precision medicine (5P medicine). The book contains 46 peer-reviewed papers (1 keynote, 5 invited papers, 33 full papers, and 7 poster papers). Subjects covered include the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, the Health Internet of Things (HIoT), as well as potential risks for security and privacy, and the motivation and empowerment of patients in care processes.
Providing an overview of current advances in personalized health and health management, the book will be of interest to all those working in the field of healthcare today
Achaiki Iatriki : official publication of the medical society of western Greece and Peloponnesus
In the current issue, the editorial by Cauchi et al.
argues for eco-friendly measures in endoscopy and
emphasies the role of healthcare providers in reducing waste. The editorial adeptly employs the three Rs
(Reduce, Reuse, Recycle) framework to tackle waste
management, offering practical solutions. The editorial by Milionis et al. focuses on the reverse cascade
screening for paediatric familial hypercholesterolaemia
(FH), which is an upcoming tool for public health. Advantages, practices, and challenges regarding FH are
thoroughly discussed. Lastly, the editorial by Fousekis
et al. presents the main aspects of a chronic immune-mediated cutaneous disease, dermatitis herpetiformis
(DH), which constitutes an extraintestinal manifestation
of celiac disease, including its diagnosis, pathogenesis,
and management.
Moreover, this issue includes three review articles.
The review article by Krontira et al. discusses the evolving data on the epidemiology, diagnostic approach and
appropriate management of foreign body and caustic
substance ingestion, based on updated guidelines
published by gastroenterological and endoscopic societies. The review by Halliasos et al. provides data on the
clinical presentation, diagnosis, and management of
metastatic acute spinal cord compression, focusing on
the importance of a multidisciplinary team approach,
including spine surgeons, radiation oncologists, medical
oncologists, palliative care clinicians, physiotherapists,
and psychologists. Lastly, the review by Schinas et al.
outlines the potential of immune modulation in the
treatment of infections and the need for individualised approaches in the modern world of personalised
medicine by examining some of the key strategies and
immune-based therapies being developed to combat
infectious diseases.peer-reviewe
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