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Portland International Conference on Management of engineering and Technology (PICMET)

作者： K.B. Eden D.F. Kocaoglu Division of Medical Informatics and Outcomes Research Oregon Health and Sciences University Portland OR USA Engineering Management Program Portland State University Portland OR USA

The objective of this research was to identify the relationships between the selection process and the perceived impacts of information technology on the physician's practice. A telephone survey was conducted with office representatives of 407 physician practices in Oregon. The descriptive results are presented in this summary paper. Relationships were identified between the selection process and the perceived impacts of the software on the practice. These relationships are not directly presented in this paper but are summarized and recommendations are made to physician practices for selecting software.

关键词： Information technology Medical services Telephony Costs Guidelines Software Hospitals Demography Biomedical informatics Research and development management

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Information technology selection process and perceived impacts in physician's practices

Information technology selection process and perceived impac...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： K. Beekman Eden D.F. Kocaoglu Division of Medical Informatics and Outcomes Research Oregon Health and Sciences University Portland OR USA Engineering Management Program Portland State University Portland OR USA

To date, few articles have documented the selection process and the resulting impact of the information technology on the health care organization. Information technology (IT) for this study is defined as: computer software used to store, transport and communicate information. This study was designed to gain understanding on how physicians and office staff select software and what the perceptions are of the software after implementation. The primary objective of this research was to identify relationships (if any) between the selection process and the perceived impacts of the software on the physician's practice.

关键词： Information technology Telephony Medical services Costs Software performance Business Packaging Biomedical informatics Physics computing Software packages

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A Trajectory for Validating Computational Emulation Models of Organizations

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Computational & Mathematical Organization Theory 1999年第4期5卷 385-401页

作者： Thomsen, Jan Levitt, Raymond E. Kunz, John C. Nass, Clifford I. Fridsma, Douglas B. Construction Engineering and Management Program Department of Civil and Environmental Engineering Stanford University Stanford Department of Communication Stanford University Stanford Palo Alto Veterans Administration Health Care System Palo Alto CA 94306 Stanford Medical Informatics Stanford University Stanford

Validation of complex simulation models is a challenging problem in computational organization theory research. In this paper, we describe a validation strategy suitable for emulation simulation systems, and show how a comprehensive validation consists of a sequence of steps that evaluate different aspects of a computational organizational simulation model. We demonstrate how this strategy can be applied to the evaluation of the Virtual Team Alliance (VTA), an emulation simulation system designed to guide managers charged with organizational change. VTA required a &201C;trajectory&201D; of successive validation experiments, before managers where willing to follow the recommendations of VTA. Ultimately, we believe this validation approach can be applied to a wide range of different simulation systems, and will make identification of the strengths and weaknesses of organizational simulations easier.

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Representing Medical Protocols for Organizational Simulation: An Information-Processing Approach

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Computational & Mathematical Organization Theory 1998年第1期4卷 71-95页

作者： Fridsma, Douglas B. Thomsen, Jan Department of Medicine Section on Medical Informatics Stanford University School of Medicine Stanford Department of Civil and Environmental Engineering The Construction Engineering and Management Program Stanford University School of Engineering Stanford

Organizational simulations have been used in business, manufacturing, and engineering design tasks to gain insight into organizational process bottlenecks, and to improve the quality and efficiency of processes within these industries. As market pressures demand increased efficiencies within the health care industry, organizational simulation techniques could provide similar insight into the design of better medical care processes, or protocols, in medical organizations. To simulate the process of medical care within a specific organization however, requires models that can represent (1) unpredictable patient responses to care, (2) the flexibility needed to adapt to different patients, and (3) different preferences of health care professionals and the implicit preferences contained within the protocol. Using previous work on simulation in the Virtual Design Team (VDT), and an example protocol drawn from an existing protocol in bone marrow transplantation, we describe extensions to the VDT information-processing representation that will allow us to simulate the performance characteristics of a medical protocol used within a medical organization. Our representational extensions capture the uncertainty of medical care for patients, the activity flexibility within the organization, and the preferences of health care professionals that will make information-processing organizational simulations in the medical domain possible. We believe our representation will provide a robust simulation “tool box” that can be used to investigate the performance of specific medical protocols within different hospital settings, and explore organizational theory within the health care industry.

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Contributory presentations/posters

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Journal of Biosciences 1999年第1期24卷 33-198页

作者： N. Manoj V. R. Srinivas A. Surolia M. Vijayan K. Suguna R. Ravishankar R. Schwarzenbacher K. Zeth Diederichs G. M. Kostner A. Gries P. Laggner R. Prassl Madhusudan Pearl Akamine Nguyen-huu Xuong Susan S. Taylor M. Bidva Sagar K. Saikrishnan S. Roy K. Purnapatre P. Handa U. Varshney B. K. Biswal N. Sukumar J. K. Mohana Rao A. Johnson Vasantha Pattabhi S. Sri Krishna Mira Sastri H. S. Savithri M. R. N. Murthy Bindu Pillai Kannan M. V. Hosur Mukesh Kumar Swati Patwardhan K. K. Kannan B. Padmanabhaa S. Sasaki-Sugio M. Nukaga T. Matsuzaki S. Karthikevan S. Sharma A. K. Sharma M. Paramasivam P. Kumar J. A. Khan S. Yadav A. Srinivasan T. P. Singh S. Gourinath Neelima Alam A. Srintvasan Vikas Chandra Punit Kaur Ch. Betzel S. Ghosh A. K. Bera S. Bhattacharya S. Chakraborty A. K. Pal B. P. Mukhopadhyay I. Dey U. Haldar Asok Baneriee Jozef Sevcik Adriana Solovicova K. Sekar M. Sundaralingam N. Genov Dong-cai Liang Tao Jiang Ji-ping Zhang Wen-rui Chang Wolfgang Jahnke Marcel Blommers S. C. Panchal R. V. Hosur Bindu Pillay Puniti Mathur S. Srivatsun Ratan Mani Joshi N. R. Jaganathan V. S. Chauhan H. S. Atreya S. C. Sahu K. V. R. Chary Girjesh Govil Elisabeth Adjadj Éric Quinjou Nadia Izadi-Pruneyre Yves Blouquit Joël Mispelter Bernadette Heyd Guilhem Lerat Philippe Milnard Michel Desmadreil Y. Lin B. D. Nageswara Rao Vidva Raghunathan Mei H. Chau Prashant Pesais Sudha Srivastava Evans Coutinho Anil Saran Leizl F. Sapico Jayson Gesme Herbert Lijima Raymond Paxton Thamarapu Srikrishnan C. R. Grace G. Nagenagowda A. M. Lynn Sudha M. Cowsik Sarata C. Sahu S. Chauhan A. Bhattacharya G. Govil Anil Kumar Maurizio Pellecchia Erik R. P. Zuiderweg Keiichi Kawano Tomoyasu Aizawa Naoki Fujitani Yoichi Hayakawa Atsushi Ohnishi Tadayasu Ohkubo Yasuhiro Kumaki Kunio Hikichi Katsutoshi Nitta V. Rani Parvathy R. M. Kini Takumi Koshiba Yoshihiro Kobashigawa Min Yao Makoto Demura Astushi Nakagawa Isao Tanaka Kunihiro Kuwajima Jens Linge Seán O. Donoghue Michael Nilges G. Chakshusmathi Girish S. Ratnaparkhi P. K. Madhu R. Varadarajan C. Tetreau Molecular Biophysics Unit Indian Institute of Science Bangalore India Austrian Academy of Sciences Institute of Biophysics and X-ray Structure Research Graz Austria Faculty of Biology University of Konstanz Germany Institute of Medical Biochemistry University of Graz Austria Howard Huges Medical Institute Department of Chemistry & Biochemistry University of California La Jolla USA Department of Chemistry & Biochemistry University of California San Diego La Jolla USA Molecular Biophysics Unit India Department of Microbiology &Cell Biology Indian Institute of Science Bangalore India Macromolecular Structure Laboratory NCI - Frederick Cancer Research and Development Center ABL - Basic Research Program Frederick USA Department of Crystallography and Biophysics University of Madras Chennai Biochemistry Department Indian Institute of Science Bangalore India Condensed Matter Physics Division B.A.R.C. Trombay Mumbai Condensed Matter Physics Division Bhabha Atomic Research Centre Trombay Mumbai IMCB The University of Tokyo Tokyo Japan Mitsubishi Chemical Corporation Yokohama Research Center Yokohama Japan Faculty of Pharmaceutical Sciences Chiba University Chiba Japan Department of Biophysics All India Institute of Medical Sciences New Delhi India Department of Biophysics All India Institute of Medical sciences New Delhi India Institüt Physiologische Chemie Hamburg Germany Biophysics Department Bose Institute Calcutta India Slovak Academy of Sciences Institute of Molecular Biology Bratislava Slovakia Indian Institute of Science Bioinformatics Centre Bangalore Departments of Chemistry and Biochemistry Biological Macromolecular Structure Center USA Bulgarian Academy of Sciences Institute of Organic Chemistry Sofia Bulgaria Chinese Academy of Sciences Institute of Biophysics Beijing China Novartis Pharma AG Preclinical Research Basel Switzerland Department of Chemical Sciences Tata Institute of Fundamental Research Mumbai India Solid State Physics Div

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Non-rigid image alignment for layered cell representation and motion tracking

Non-rigid image alignment for layered cell representation an...

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Annual Northeast Bioengineering Conference

作者： S. Shemlon S. Hawkins Graduate Program in Biomedical Informatics SHRP Newark NJ USA Department of Biomedical Engineering Rutgers University Piscataway NJ USA

Mathematical modeling of the dynamic behavior of physical systems, gives computers that mimic the capabilities to intelligently monitor and predict their evolutionary characteristics. In this paper, we present a system for tracking the time-varying features of non-rigid objects in images of evolving scenes, using the elastic string model of planar contours, which permits the inference and prediction of the quantitative parameters that characterize evolutionary behavior. The goal of our work is to dynamically track non-rigid objects in video sequences, using object alignment techniques based on the properties of the elastic string. We present experimental results of growth cone and neurite tracking in cell growth and motion studies.

关键词： Tracking Deformable models Predictive models Mathematical model Biomedical computing Layout Image motion analysis Motion analysis Equations Neurons

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Deforming post-mortem radiograph images to reconstruct a bullet path through stock CT data

Deforming post-mortem radiograph images to reconstruct a bul...

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Visualization in Biomedical Computing 1994

作者： Boxwala, Aziz A. Oliver, William R. Program in Medical Informatics Department of Biomedical Engineering University of North Carolina Chapel HillNC27599-7575 United States Digital Image Processing Laboratory Department of Cellular Pathology Armed Forces Institute of Pathology WashingtonDC20306-6000 United States

An earlier study investigated a technique to reconstruct in three-dimensions the path of a bullet through a skull, using the post-mortem X-rays of the victim and stock computed tomography (CT) data. This paper describes the addition of image deformation methods in order to improve the accuracy of the reconstruction technique. The skull X-ray images of the victim were warped to match the shape of the skull in the CT. The warping was done by a thin-plate spline deformation algorithm. The bullet path was again reconstructed using the warped X-ray images as the source of information for the bullet entry and exit wound. The difference in the angle of the bullet paths in the two reconstructions was 6.9 degrees. © 1994 SPIE. All rights reserved.

关键词： Deformation

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Graph Artificial Intelligence in Medicine

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Biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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Ernst Denert Award for Software engineering 2020 1

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1000年

作者： Michael Felderer Wilhelm Hasselbring Heiko Koziolek Florian Matthes Lutz Prechelt Ralf Reussner Bernhard Rumpe Ina Schaefer

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Ernst Denert Award for Software engineering 2019 1

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1000年

作者： Michael Felderer Wilhelm Hasselbring Heiko Koziolek Florian Matthes Lutz Prechelt Ralf Reussner Bernhard Rumpe Ina Schaefer

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