Relevant bibliographies by topics / Anesthesiology Data processing

Academic literature on the topic 'Anesthesiology Data processing'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Anesthesiology Data processing"

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Burns, Michael L., Michael R. Mathis, John Vandervest, Xinyu Tan, Bo Lu, Douglas A. Colquhoun, Nirav Shah, Sachin Kheterpal, and Leif Saager. "Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning." Anesthesiology 132, no. 4 (April 1, 2020): 738–49. http://dx.doi.org/10.1097/aln.0000000000003150.

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Abstract Background Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. Methods Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. Results Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. Conclusions Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
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Riestiawan and Indah Ariyati. "Komparasi Pengolahan Data Keuangan Manual Dengan Pengolahan Data Keuangan Menggunakan Zahir Accounting Versi 5.1." Journal of Students‘ Research in Computer Science 3, no. 1 (May 30, 2022): 89–98. http://dx.doi.org/10.31599/jsrcs.v3i1.1478.

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The development of information technology has penetrated all areas of life in all parts of the world, which is triggered by the increasing complexity of business activities and the increasing need for financial information. Processing of financial data on CV. Akbar Motor is recorded manually using sheets of paper so that the recording still does not work optimally. In addition, manual data processing is quite time-consuming, labor-intensive, and large costs which causes late financial statements. Researchers process financial data manually then continue using Zahir Accouting Software Version 5.1 as a comparison. The research methods used in collecting data consist of observations, interviews and literature studies. Comparison between manual processing of financial data with Zahir Accounting version 5.1. aims to find out the financial condition of an enterprise in a certain period in the hope that it can be used in decision making quickly and accurately. Keywords: Comparative, Finance, Reports, Zahir Abstrak Perkembangan teknologi informasi telah merambah di segala bidang kehidupan di seluruh belahan penjuru dunia, yang dipicu dengan meningkatnya kompleksitas kegiatan usaha dan meningkatnya kebutuhan akan informasi keuangan. Pengolahan data keuangan pada CV. Akbar Motor dicatat secara manual menggunakan lembaran–lembaran kertas sehingga pencatatan masih belum bekerja secara maksimal. Selain itu pengolahan data secara manual cukup memakan waktu, tenaga, dan biaya besar yang menyebabkan terlambatnya laporan keuangan. Peneliti melakukan pengolahan data keuangan secara manual kemudian dilanjutkan dengan menggunakan Software Zahir Accouting Versi 5.1 sebagai komparasinya. Metode penelitian yang di gunakan dalam mengumpulkan data terdiri dari pengamatan, wawancara dan studi pustaka. Komparasi antara pengolahan data keuangan secara manual dengan Zahir Accounting versi 5.1. bertujuan untuk mengetahui kondisi keuangan suatu perusahaan ini dalam periode tertentu dengan harapan dapat di gunakan dalam pengambilan keputusan secara cepat tepat dan akurat Kata kunci: Keuangan, Komparasi, Laporan, Zahir
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Hasan, Firman Noor, and I. Ketut Sudaryana. "Penerapan Business Intelligence & Online Analytical Processing untuk Data-Data Penelitian dan Luarannya pada Perguruan Tinggi Menggunakan Pentaho." Infotech: Journal of Technology Information 8, no. 2 (November 30, 2022): 85–92. http://dx.doi.org/10.37365/jti.v8i2.143.

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Utilization of ICT in various sectors is needed, especially those related to business impact and strategy. Information technology is the backbone of the sustainability of businesses, companies, and organizations. Companies that are able to utilize ICT well, therefore indirectly adapt to the times and strive to excel from competitors. The method used refers to the steps recommended by Carlo Vercellis. The software and tools used are open source based, such as Pentaho Data Integration for processing extract, transform, load (ETL), Pentaho Community Edition for dashboards, Pentaho Report Designer for report generation, and Mondrian OLAP for displaying multidimensional data. The results of this study conclude application of business intelligence in universities is very easy and efficient, the use of a dashboard that is presented visually and interactively is very helpful for leaders in viewing existing research data. So it is very helpful for organizations, especially university leaders in making decisions.
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Flick, Moritz, Jasmin Matin Mehr, Luisa Briesenick, Phillip Hoppe, Karim Kouz, Christina Vokuhl, Doris Flotzinger, Katja Lerche, and Bernd Saugel. "Agreement between continuous noninvasive finger cuff-derived and invasive arterial blood pressure measurements: Effect of data sampling and data processing." European Journal of Anaesthesiology 38, no. 6 (March 1, 2021): 616–24. http://dx.doi.org/10.1097/eja.0000000000001469.

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Pasma, Wietze, Linda M. Peelen, Stef van Buuren, Wilton A. van Klei, and Jurgen C. de Graaff. "Artifact Processing Methods Influence on Intraoperative Hypotension Quantification and Outcome Effect Estimates." Anesthesiology 132, no. 4 (April 1, 2020): 723–37. http://dx.doi.org/10.1097/aln.0000000000003131.

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Abstract Background Physiologic data that is automatically collected during anesthesia is widely used for medical record keeping and clinical research. These data contain artifacts, which are not relevant in clinical care, but may influence research results. The aim of this study was to explore the effect of different methods of filtering and processing artifacts in anesthesiology data on study findings in order to demonstrate the importance of proper artifact filtering. Methods The authors performed a systematic literature search to identify artifact filtering methods. Subsequently, these methods were applied to the data of anesthesia procedures with invasive blood pressure monitoring. Different hypotension measures were calculated (i.e., presence, duration, maximum deviation below threshold, and area under threshold) across different definitions (i.e., thresholds for mean arterial pressure of 50, 60, 65, 70 mmHg). These were then used to estimate the association with postoperative myocardial injury. Results After screening 3,585 papers, the authors included 38 papers that reported artifact filtering methods. The authors applied eight of these methods to the data of 2,988 anesthesia procedures. The occurrence of hypotension (defined with a threshold of 50 mmHg) varied from 24% with a median filter of seven measurements to 55% without an artifact filtering method, and between 76 and 90% with a threshold of 65 mmHg. Standardized odds ratios for presence of hypotension ranged from 1.16 (95% CI, 1.07 to 1.26) to 1.24 (1.14 to 1.34) when hypotension was defined with a threshold of 50 mmHg. Similar variations in standardized odds ratios were found when applying methods to other hypotension measures and definitions. Conclusions The method of artifact filtering can have substantial effects on estimates of hypotension prevalence. The effect on the association between intraoperative hypotension and postoperative myocardial injury was relatively small. Nevertheless, the authors recommend that researchers carefully consider artifacts handling and report the methodology used. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
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Rusanov, Alexander, Riccardo Miotto, and Chunhua Weng. "Trends in anesthesiology research: a machine learning approach to theme discovery and summarization." JAMIA Open 1, no. 2 (September 4, 2018): 283–93. http://dx.doi.org/10.1093/jamiaopen/ooy009.

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Abstract Objectives Traditionally, summarization of research themes and trends within a given discipline was accomplished by manual review of scientific works in the field. However, with the ushering in of the age of “big data,” new methods for discovery of such information become necessary as traditional techniques become increasingly difficult to apply due to the exponential growth of document repositories. Our objectives are to develop a pipeline for unsupervised theme extraction and summarization of thematic trends in document repositories, and to test it by applying it to a specific domain. Methods To that end, we detail a pipeline, which utilizes machine learning and natural language processing for unsupervised theme extraction, and a novel method for summarization of thematic trends, and network mapping for visualization of thematic relations. We then apply this pipeline to a collection of anesthesiology abstracts. Results We demonstrate how this pipeline enables discovery of major themes and temporal trends in anesthesiology research and facilitates document classification and corpus exploration. Discussion The relation of prevalent topics and extracted trends to recent events in both anesthesiology, and healthcare in general, demonstrates the pipeline’s utility. Furthermore, the agreement between the unsupervised thematic grouping and human-assigned classification validates the pipeline’s accuracy and demonstrates another potential use. Conclusion The described pipeline enables summarization and exploration of large document repositories, facilitates classification, aids in trend identification. A more robust and user-friendly interface will facilitate the expansion of this methodology to other domains. This will be the focus of future work for our group.
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Xiao, Yan, Peter Hu, Hao Hu, Danny Ho, Franklin Dexter, Colin F. Mackenzie, F. Jacob Seagull, and Richard P. Dutton. "An Algorithm for Processing Vital Sign Monitoring Data to Remotely Identify Operating Room Occupancy in Real-Time." Anesthesia & Analgesia 101, no. 3 (September 2005): 823–29. http://dx.doi.org/10.1213/01.ane.0000167948.81735.5b.

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Santoso, Hari, Sugesti Sugesti, and Notatema Anugrah Gea. "RANCANG BANGUN SISTEM INFORMASI REKAM MEDIS BERBASIS WEB." Infotech: Journal of Technology Information 7, no. 1 (June 30, 2021): 1–6. http://dx.doi.org/10.37365/jti.v7i1.100.

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Medical record is a file that contains records about the patient's identity, examination, treatment, actions and other health services to patients. The speed of obtaining data and processing of data is very much needed in the current technological era. With the development of technology makes people think to be able to work more effectively and efficiently. One of them is making a conventional system into a computerized system. By utilizing website facilities that are connected to the internet, medical records can be more effective and efficient in searching and recording medical history. In this research a web-based information system is designed using the PHP programming language and MySQL database using the waterfall method as its research method. With this system, it is expected to be able to overcome the various needs of users to search for patient data and perform data processing as well as facilitate users in making reports. From the results of research and design that has been implemented to produce medical record applications that facilitate the processing of patient data.
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Hofbauer, Robert K., Pierre Fiset, Gilles Plourde, Steven B. Backman, and M. Catherine Bushnell. "Dose-dependent Effects of Propofol on the Central Processing of Thermal Pain." Anesthesiology 100, no. 2 (February 1, 2004): 386–94. http://dx.doi.org/10.1097/00000542-200402000-00031.

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Background Anatomic and physiologic data show that multiple regions of the forebrain are activated by pain. However, the effect of anesthetic level on nociceptive input to these regions is not well understood. Methods The authors used positron emission tomography to measure the effect of various concentrations of propofol on pain-evoked changes in regional cerebral blood flow. Fifteen volunteers were scanned while warm and painful heat stimuli were presented to the volar forearm using a contact thermode during administration of target propofol concentrations of 0.0 microg/ml (alert control), 0.5 microg/ml (mild sedation), 1.5 microg/ml (moderate sedation), and 3.5 microg/ml (unconsciousness). Results During the 0.5-microg/ml target propofol concentration (mild sedation), the subjects' pain ratings increased relative to the alert control condition; correspondingly, pain-evoked regional cerebral blood flow increased in the thalamus and the anterior cingulate cortex. In contrast, when subjects lost consciousness (3.5 microg/ml), pain-evoked responses in the thalamus and the anterior cingulate cortex were no longer observed, whereas significant pain-evoked activation remained in the insular cortex. Conclusion These data show that propofol has a dose-dependent effect on thalamocortical transfer of nociceptive information but that some pain-evoked cortical activity remains after loss of consciousness.
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Lindvall, Charlotta, Alexander Forsyth, Regina Barzilay, and James Tulsky. "Natural Language Processing: An Opportunity to Make Chart Data Come Alive in Palliative Care Research (FR481A)." Journal of Pain and Symptom Management 53, no. 2 (February 2017): 385. http://dx.doi.org/10.1016/j.jpainsymman.2016.12.164.

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Dissertations / Theses on the topic "Anesthesiology Data processing"

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Hawkins, Kevin Michael. "Development of an automated anesthesia system for the stabilization of physiological parameters in rodents." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0424103-105500/.

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Books on the topic "Anesthesiology Data processing"

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Osswald, P. M. Computers in Critical Care and Pulmonary Medicine. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985.

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Information Technology Applied to Anesthesiology. Elsevier - Health Sciences Division, 2011.

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Oyama, Tsutomu, Kazuo Sato, Kazuyuki Ikeda, Matsuyuki Doi, and Tomiei Kazama. Computing and Monitoring in Anesthesia and Intensive Care: Recent Technological Advances. Springer, 2012.

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Oyama, Tsutomu, Kazuo Sato, Kazuyuki Ikeda, Matsuyuki Doi, and Tomiei Kazama. Computing and Monitoring in Anesthesia and Intensive Care: Recent Technological Advances. Springer London, Limited, 2012.

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1932-, Ikeda Kazuyuki, ed. Computing and monitoring in anesthesia and intensive care: Recent technological advances. Tokyo: Springer-Verlag, 1992.

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Anesthesia Informatics Health Informatics. Springer, 2008.

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S, Graenstein J., ed. The Automated anesthesia record and alarm systems. Boston: Butterworths, 1987.

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Ikeda, Kazuyuki, ed. State of the Art Technology in Anesthesia and Intensive Care. Elsevier Science Publishing Company, 1998.

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Simulation In Anesthesia. Saunders, 2006.

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Hartung, Hans-Joachim. Anwendungsgebiete der Computertechnologie in Anaesthesie und Intensivmedizin. 1985.

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