Haas Automation is the largest machine tool builder in the western world, manufacturing a complete line of CNC vertical machining centers, horizontal machining centers, CNC lathes, and rotary products.

Haas Automation Inc. - CNC Machine Tools

Haas Automation, Inc is an American machine tool builder headquartered in Oxnard, California. The company designs and manufactures lower cost machine tools and specialized accessory tooling, mostly computer numerically controlled (CNC) equipment, such as vertical machining centers and horizontal ...

Haas Automation - Wikipedia

Gene Haas founded Haas Automation, Inc., in 1983 to manufacture economical and reliable machine tools. The company entered the machine tool industry with the first-ever, fully automatic, programmable collet indexer – a device used to position parts for machining with very high accuracy.

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Haas Door

Haas Automation is the largest machine tool builder in the western world, manufacturing a complete line of CNC vertical machining centers, horizontal machining centers, CNC lathes and rotary products. The company also builds a variety of specialty machines, including 5-axis machining centers, ...

Haas Automation, Inc. | LinkedIn

Digital alerting from HAAS Alert’s Safety Cloud® notifies drivers of incoming emergency vehicles, and connects vehicles for better visibility and faster response times.

Safety Cloud - Digital Alerting for Connected Roads | HAAS Alert

Serving Southwest California, Haas Factory Outlet Torrance distributes, services and supports the Haas line of machine tools.

Haas Factory Outlet

Haas Automation UK supplies CNC Machine Tools, including Vertical Machining Centres, CNC Mills, Horizontals, Lathes and Rotary Tables.

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The company has heavily invested in the sourcing, transportation, storage, distribution, and marketing of diesel fuel, automotive gasoline, kerosene, jet fuel, Liquefied Petroleum Gas (LPG), and lubricants for industrial and automotive systems.

Hass Petroleum Group – Servicing New Frontiers

Haas &amp; Company AG - Logistic solutions for museums and galleries, collectors and private clients, auction houses and artists around the world.

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Rohm and Haas Company is a manufacturer of specialty chemicals for end use markets such as building and construction, electronic devices, packaging, household and personal care products. Headquartered in Philadelphia, the company is organized into three business groups of Specialty Materials, ...

Rohm and Haas - Wikipedia

Information on valuation, funding, acquisitions, investors, and executives for Haas Automation. Use the PitchBook Platform to explore the full profile.

Haas Automation Company Profile: Funding & Investors | PitchBook

Haas &amp; Company GmbH - Logistic solutions for museums and galleries, collectors and private clients, auction houses and artists around the world.

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See Instagram photos and videos from Haas Automation, Inc. (@haas_automation)

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Answer to Haas Company manufactures and sells one product. The

Haas Company manufactures and sells one product. The | Chegg.com

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Company profile page for Haas Automation Inc including stock price, company news, executives, board members, and contact information

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We bring together the world’s finest hops and technically advanced hop products to inspire great beer flavors and help you find new brewhouse efficiencies.

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Haas F1 Team is the only American team on the grid, but our workforce is truly global. The newest entry on the grid, making our competitive debut in 2016, we have over 200 employees split across three sites in the US, UK, and Italy, working tirelessly on creating a new era of racing for the team.

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High-tech manufacturing equipment from Haas Automation in Oxnard has been used by Russian military contractors during the war in Ukraine, despite U.S. sanctions against those companies, according to a report from a Ukrainian organization that monitors companies doing business in Russia.

Haas Automation responds to accusation its products fuel Russia's ...

Ghaas is a premium marketing team working with venture funded startups as well as Fortune 100 companies to successfully leverage digital channels to solve communication and outreach problems

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The industry has polluted the Earth’s waterways and has been left unchecked for a disconcertingly long time, an issue that we are passionate to address. fashion Sadly, this problem is even more prevalent in countries where environmental and safety laws are less strict, and because of this, once beautiful and flowing exotic rivers have become polluted, stagnant, toxic, and oddly enough, black as ink. The Bane of Bangladesh: Black-Ink Rivers Even the idea of a black-ink river makes us uneasy. For some residents in Bangladesh, black ink rivers are a part of their day-to-day lives. Aside from China, Bangladesh is the largest garment manufacturer in the world, pumping out over $34 billion dollars worth of clothing materials in 2019.  This demand for garments has had a devastating effect on the environment. Rivers with wastewater from the dye industry can contain many carcinogenic chemicals, dyes, salts, and heavy metals, and much of this black, ruined water reaches the homes of the unfortunate surrounding residents. polluted Running Families from their Homes Black ink rivers, and other similarly eerie phenomena are the unfortunate end result. However, their dreadful appearance is hardly their worst quality. In an interview with CNN, an unnamed resident of Savar comments on the quality of the water he and his family have had to endure, saying that they had exhausted their options and would have to leave soon. Savar also explained to CNN how the area had changed since he was younger. Garment factories were non-existent, the land was free of waste, and the rivers were clean and full of life. Now, it has become a toxic wasteland. His children will never experience the home that he cherished. Toxic Dye Chemicals: An Environmental Concern The World Bank specifically identified 72 toxic chemicals that are released into the waterways of Bangladesh on a daily basis. Even though many of the chemicals used are non-toxic, they still accumulate in the water. Of course, the rivers aren’t the only issue. The factories themselves are poorly managed, and the safety standards are lackluster, to say the least. Many of the factory workers are maskless, gloveless, and sock-less, wearing sandals, shorts, and stuffed into congested areas with many dangerous chemicals. If you are exposed to these chemicals (and you eventually will be) you are almost guaranteed to develop open sores, rashes, and other skin irritations. The Government’s Response Thankfully, the Bangladesh government has been addressing the issue. Minister Shahab Uddin, leader of the Ministry, stated in an email to CNN that a wide variety of measures were being taken to address the carbon footprint of the Bangladeshi garment industry. This includes updating conservation and environmental laws, monitoring water quality, imposing fines on polluters, setting up centralized treatment plants, and collaborating with international development partners to optimize wastewater treatment. Savar also explained to CNN how the area had changed since he was younger. Garment factories were non-existent, the land was free of waste, and the rivers were clean and full of life. Now, it has become a toxic wasteland. His children will never experience the home that he cherished. The Saltwater Alternative Researchers in Dhaka, Bangladesh are also considering of dye application to combat textile pollution. Normal textile industrial processes require access to freshwater sources. The method they’ve developed would only require a saltwater source. Killing two birds with one stone, the researchers also negated the need for salt, a main ingredient in the dyeing process, by using salt water instead of freshwater. new methods You may think that such a process would inevitably provide a lower-quality product, but these researchers would disagree. After accomplishing an extensive amount of sample comparison  (shade, colorfastness, color strength, etc.) between seawater and freshwater, they came to the conclusion that the seawater process resulted in an end product that did appear lighter, but not to a degree that would be considered significant.' Finding Balance Though the dyeing industry will certainly take time to reach a level of balance that would be suitable for the environment, the fashion industry as a whole is in a constant state of innovation, and new advances are arriving every day. We at  ÀLA.HAUSSE are excited and hopeful for the future of , however big the step, so long as it’s in the right direction. sustainability

Black Ink Rivers: A Ghastly Consequence of the Dye Industry

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 6 Conclusion Understanding how recommender systems shape our behaviour is essential to avoid manipulation. In this paper, we investigated the Amazon recommender system concerning whether it has made it harder to change preferences. By analysing the Barrier-to-Exit (Rakova & Chowdhury, 2019) of more than 50,000 users, we found a highly significant growth in Barrier-to-Exit over time, which indicates that it has indeed become harder to change preferences for the analysed users. However, sampling bias induced by the calculation of Barrier-to-Exit makes it difficult to draw conclusions about the general population of Amazon customers. This highlights the dilemma of portability in measuring socio-technical systems (Selbst et al., 2019): accurately evaluating a concept like ”changing preferences” requires adapting to the context of the system, which makes it more difficult to generalise (and compare) to other systems. Comparing recommender systems is necessary for ensuring that these respect human autonomy (Varshney, 2020) and live up to new regulations such as the EU AI Act (Kop, 2021). Further work, should aim to create auditing procedures and metrics that allow third parties to measure potential preference manipulation in a way that fits within the context of the industry and allows for comparisons between different systems. This will help assess the pressures of Surveillance Capitalism (Zuboff, 2019) on human autonomy. References Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59 (4), 390–412. (Publisher: Elsevier) Barto´n, K. (2022, September). MuMIn: Multi-Model Inference. Retrieved 2023-01-08, from https://CRAN.R-project .org/package=MuMIn Bates, D., M¨achler, M., Bolker, B., & Walker, S. (2014, June). Fitting Linear Mixed-Effects Models using lme4. arXiv. Retrieved 2023-01-12, from http://arxiv.org/abs/1406.5823 (arXiv:1406.5823 [stat]) Bennett, J., & Lanning, S. (2007). The netflix prize. In Proceedings of KDD cup and workshop (Vol. 2007, p. 35). New York. urrell, J. (2016, June). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3 (1), 2053951715622512. Retrieved 2022-10-11, from https://ezproxy-prd.bodleian.ox.ac.uk:2246/doi/full/10.1177/2053951715622512 (Publisher: SAGE Publications Ltd) doi: 10.1177/ 2053951715622512 Calvo, R. A., Peters, D., Vold, K., & Ryan, R. M. (2020). Supporting Human Autonomy in AI Systems: A Framework for Ethical Enquiry. In C. Burr & L. Floridi (Eds.), Ethics of Digital Well-Being: A Multidisciplinary Approach (pp. 31–54). Cham: Springer International Publishing. Retrieved 2022-08-16, from https://doi.org/10.1007/ 978-3-030-50585-1 2 doi: 10.1007/978-3-030-50585-1 2 Carroll, M. D., Dragan, A., Russell, S., & Hadfield-Menell, D. (2022). Estimating and penalizing induced preference shifts in recommender systems. In International Conference on Machine Learning (pp. 2686–2708). PMLR. Feng, C., Wang, H., Lu, N., Chen, T., He, H., Lu, Y., & Tu, X. M. (2014, April). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 26 (2), 105–109. Retrieved 2023-01-10, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120293/ doi: 10.3969/j.issn.1002-0829.2014.02.009 Floridi, L., Holweg, M., Taddeo, M., Amaya Silva, J., M¨okander, J., & Wen, Y. (2022, March). capAI - A Procedure for Conducting Conformity Assessment of AI Systems in Line with the EU Artificial Intelligence Act (SSRN Scholarly Paper No. ID 4064091). Rochester, NY: Social Science Research Network. Retrieved 2022-04-01, from https://papers.ssrn.com/abstract=4064091 doi: 10.2139/ssrn.4064091 Fox, J. (2003). Effect Displays in R for Generalised Linear Models. Journal of Statistical Software, 8 (15). Retrieved 2023-01-10, from http://www.jstatsoft.org/v08/i15/ doi: 10.18637/jss.v008.i15 Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications. Franklin, M., Ashton, H., Gorman, R., & Armstrong, S. (2022, May). Missing Mechanisms of Manipulation in the EU AI Act. The International FLAIRS Conference Proceedings, 35 . Retrieved 2022-12-31, from https:// journals.flvc.org/FLAIRS/article/view/130723 doi: 10.32473/flairs.v35i.130723 Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daum´e III, H., & Crawford, K. (2021, December). Datasheets for Datasets. arXiv. Retrieved 2022-10-20, from http://arxiv.org/abs/1803.09010 (arXiv:1803.09010 [cs]) Ghasemi, A., & Zahediasl, S. (2012, December). Normality Tests for Statistical Analysis: A Guide for NonStatisticians. International Journal of Endocrinology and Metabolism, 10 (2), 486–489. Retrieved 2023-01-11, from https://brief.land/ijem/articles/71904.html doi: 10.5812/ijem.3505 Gorelick, M., & Ozsvald, I. (2020). High performance Python: practical performant programming for humans (Second edition ed.). Beijing [China] ; Boston [MA]: O’Reilly. Harper, F. M., & Konstan, J. A. (2016, January). The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 5 (4), 1–19. Retrieved 2022-12-29, from https://dl.acm.org/doi/10.1145/ 2827872 doi: 10.1145/2827872 Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., . . . Oliphant, T. E. (2020, September). Array programming with NumPy. Nature, 585 (7825), 357–362. Retrieved 2021-12-27, from https://www.nature.com/articles/s41586-020-2649-2 doi: 10.1038/s41586-020-2649-2 Jannach, D., & Adomavicius, G. (2016, September). Recommendations with a Purpose. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 7–10). Boston Massachusetts USA: ACM. Retrieved 2022-09-01, from https://dl.acm.org/doi/10.1145/2959100.2959186 doi: 10.1145/2959100.2959186 Jiang, R., Chiappa, S., Lattimore, T., Gy¨orgy, A., & Kohli, P. (2019, January). Degenerate Feedback Loops in Recommender Systems. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 383–390). Honolulu HI USA: ACM. Retrieved 2022-09-07, from https://dl.acm.org/doi/10.1145/3306618 .3314288 doi: 10.1145/3306618.3314288 Kipf, T. N., & Welling, M. (2017, February). Semi-Supervised Classification with Graph Convolutional Networks. arXiv. Retrieved 2022-12-14, from http://arxiv.org/abs/1609.02907 (arXiv:1609.02907 [cs, stat]) Knijnenburg, B. P., Reijmer, N. J., & Willemsen, M. C. (2011, October). Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 141–148). New York, NY, USA: Association for Computing Machinery. Retrieved 2023-01-11, from https://doi.org/10.1145/2043932.2043960 doi: 10.1145/2043932.2043960 Kop, M. (2021, November). EU Artificial Intelligence Act: The European Approach to AI. , 11. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: tests in linear mixed effects models. Journal of statistical software, 82 (13). (Publisher: The Foundation for Open Access Statistics) Lam, S. K., Pitrou, A., & Seibert, S. (2015). Numba: A llvm-based python jit compiler. In Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC (pp. 1–6). Ledwich, M., & Zaitsev, A. (2019). Algorithmic extremism: Examining YouTube’s rabbit hole of radicalization. arXiv preprint arXiv:1912.11211 . Leino, J., & R¨aih¨a, K.-J. (2007, October). Case amazon: ratings and reviews as part of recommendations. In Proceedings of the 2007 ACM conference on Recommender systems (pp. 137–140). New York, NY, USA: Association for Computing Machinery. Retrieved 2023-01-08, from https://doi.org/10.1145/1297231.1297255 doi: 10.1145/1297231.1297255 Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7 (1), 76–80. (Publisher: Ieee) Maddala, G. S. (1971, March). The Use of Variance Components Models in Pooling Cross Section and Time Series Data. Econometrica, 39 (2), 341. Retrieved 2023-01-11, from https://www.jstor.org/stable/1913349 ?origin=crossref doi: 10.2307/1913349 McAuley, J., Targett, C., Shi, Q., & Hengel, A. v. d. (2015, June). Image-based Recommendations on Styles and Substitutes. arXiv. Retrieved 2022-12-29, from http://arxiv.org/abs/1506.04757 (arXiv:1506.04757 [cs]) McKinney, W. (2011). pandas: a foundational Python library for data analysis and statistics. Python for high performance and scientific computing, 14 (9), 1–9. (Publisher: Seattle) Millecamp, M., Htun, N. N., Jin, Y., & Verbert, K. (2018). Controlling spotify recommendations: effects of personal characteristics on music recommender user interfaces. In Proceedings of the 26th Conference on user modeling, adaptation and personalization (pp. 101–109). Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14 (134), 20170213. (Publisher: The Royal Society) Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., & Konstan, J. A. (2014). Exploring the filter bubble: the effect of using recommender systems on content diversity. In Proceedings of the 23rd international conference on World wide web - WWW ’14 (pp. 677–686). Seoul, Korea: ACM Press. Retrieved 2022-12-31, from http:// dl.acm.org/citation.cfm?doid=2566486.2568012 doi: 10.1145/2566486.2568012 Ni, J., Li, J., & McAuley, J. (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 188–197). Nonnecke, B., & Preece, J. (2000). Lurker demographics: Counting the silent. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 73–80). O’Hara, R., & Kotze, J. (2010, January). Do not log-transform count data. Nature Precedings. Retrieved 2023-01-11, from https://www.nature.com/articles/npre.2010.4136.1 doi: 10.1038/npre.2010.4136.1 Pandita, R. (2017). Internet: A change agent an overview of internet penetration & growth across the world. International Journal of Information Dissemination and Technology, 7 (2), 83–91. Retrieved 2023-01-11, from https://www.proquest.com/docview/1920419032/abstract/14B253C0DD6A4277PQ/1 (Num Pages: 83-91 Place: Ambala, India Publisher: Maharishi Markandehwar University) Papakyriakopoulos, O., Serrano, J. C. M., & Hegelich, S. (2020). Political communication on social media: A tale of hyperactive users and bias in recommender systems. Online Social Networks and Media, 15 , 100058. (Publisher: Elsevier) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12 , 2825–2830. (Publisher: JMLR. org) Poole, M. A., & O’Farrell, P. N. (1971). The assumptions of the linear regression model. Transactions of the Institute of British Geographers, 145–158. (Publisher: JSTOR) Popper, K. R. (1970). Normal science and its dangers. Cambridge University Press Cambridge. Rakova, B., & Chowdhury, R. (2019, September). Human self-determination within algorithmic sociotechnical systems. arXiv. Retrieved 2022-09-07, from http://arxiv.org/abs/1909.06713 (arXiv:1909.06713 [cs]) Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11 (4), 193. (Publisher: Multidisciplinary Digital Publishing Institute) Roski, J., Maier, E. J., Vigilante, K., Kane, E. A., & Matheny, M. E. (2021). Enhancing trust in AI through industry self-governance. Journal of the American Medical Informatics Association, 28 (7), 1582–1590. (Publisher: Oxford University Press) Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry, 22 , 4349–4357. Satterthwaite, F. E. (1946, December). An Approximate Distribution of Estimates of Variance Components. Biometrics Bulletin, 2 (6), 110. Retrieved 2023-01-11, from https://www.jstor.org/stable/10.2307/3002019 ?origin=crossref doi: 10.2307/3002019 Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., . . . Araya-Ajoy, Y. G. (2020, September). Robustness of linear mixed-effects models to violations of distributional assumptions. Methods in Ecology and Evolution, 11 (9), 1141–1152. Retrieved 2022-12-27, from https://onlinelibrary.wiley.com/ doi/10.1111/2041-210X.13434 doi: 10.1111/2041-210X.13434 Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). Atlanta GA USA: ACM. 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Retrieved 2023-01-12, from https://onlinelibrary.wiley.com/doi/10.1111/1467-8551.12431 doi: 10.1111/1467-8551.12431 Wells, J. R., Danskin, G., & Ellsworth, G. (2018). Amazon. com, 2018. Harvard Business School Case Study(716-402). Wood, S. N., Pya, N., & S¨afken, B. (2016, October). Smoothing Parameter and Model Selection for General Smooth Models. Journal of the American Statistical Association, 111 (516), 1548–1563. Retrieved 2023- 01-12, from https://doi.org/10.1080/01621459.2016.1180986 (Publisher: Taylor & Francis eprint: https://doi.org/10.1080/01621459.2016.1180986) doi: 10.1080/01621459.2016.1180986 Zhu, F., & Liu, Q. (2018). Competing with complementors: An empirical look at Amazon.com. Strategic Management Journal, 39 (10), 2618–2642. Retrieved 2023-01-11, from https://onlinelibrary.wiley.com/doi/ abs/10.1002/smj.2932 ( eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.2932) doi: 10.1002/ smj.2932 Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power: Barack Obama’s Books of 2019. Profile Books. This paper is under CC 4.0 license. available on arxiv

Ensuring Ethical AI: Lessons from Amazon's Barrier-to-Exit Analysis

Assessing Preference Manipulation in Amazon's Recommender System

Understanding Barrier-to-Exit in Amazon's Recommender System

অ্যামাজনের সুপারিশকারী সিস্টেমে বাধা-থেকে-প্রস্থান বোঝা

Verständnis der Ausstiegsbarrieren im Empfehlungssystem von Amazon

Comprender la barrera de salida en el sistema de recomendación de Amazon

Comprendre les barrières à la sortie dans le système de recommandation d'Amazon

अमेज़न की अनुशंसा प्रणाली में बैरियर-टू-एग्जिट को समझना

Amazon のレコメンデーション システムにおける退出障壁を理解する

Amazon 추천 시스템의 종료 장벽 이해

Compreendendo a barreira de saída no sistema de recomendação da Amazon

Понимание барьера выхода в рекомендательной системе Amazon

Amazon'un Tavsiye Sisteminde Çıkış Engelini Anlamak

Tìm hiểu về Rào cản thoát trong Hệ thống đề xuất của Amazon

了解亚马逊推荐系统的退出壁垒

Authors:
(1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions;
(2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine;
(3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions;
(4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions;
(5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Table of Links Introduction Methods Results and Discussions Conclusion & References Introduction The integration and exchange of health data across diverse platforms and systems remain challenging due to the absence of standardized formats and a shared semantic understanding. This challenge becomes more significant when critical health information is embedded in unstructured data rather than well-organized structured formats. Standardizing unstructured health data, such as clinical notes, into FHIR resources can alleviate ambiguity across different health providers and, therefore, improve interoperability. However, it is by no means an easy task. Previous studies 1,2 have attempted to transform clinical notes into FHIR resources using a combination of natural language processing and machine learning tools through multi-step processes involving clinical named entity recognition, terminology coding, mathematical calculations, structural formatting, and human calibrations. However, these approaches require additional human effort to consolidate the results from multiple tools and have achieved only moderate performances, with F1 scores ranging from 0.7 to 0.9 in different elements. To this end, we intend to harness Large Language Models (LLMs) to directly generate FHIR-formatted resources from free-text input. The utilization of LLMs is expected to simplify the previously multi-step processes, enhance the efficiency and accuracy of automatic FHIR resource generation, and ultimately improve health data interoperability. Methods Data Annotation To the best of our knowledge, there is no largely publicly available dataset in the FHIR standard that is generated from contextual data. Therefore, we have chosen to annotate a dataset containing both free-text input and structured output in FHIR formats. The free-text input was derived from the discharge summaries of the MIMICIII datase. 3 Thanks to the 2018 n2c2 medication extraction challenge 4 , which essentially involves named entity recognition tasks, elements in medication statements have been identified. Our annotations built upon these n2c2 annotations and standardized the free text into multiple clinical terminology coding systems, such as NDC, RxNorm, and SNOMED. We organized the contexts and codes into FHIR medicationStatement resources. The converted FHIR resources underwent validation by the official FHIR validator (https://validator.fhir.org/) to ensure compliance with FHIR standards, including structure, datatype, code sets, display names, and more. These validated results were considered the gold standard transformation results and could be used to test against the LLMs. No ethical concerns exist regarding data usage, as both the MIMIC and n2c2 datasets are publicly available to authorized users. Large Language Model We used OpenAI's GPT-4 model as the LLM for FHIR format transformation. We used five separate prompts to instruct the LLM to transform input free text into medication (including medicationCode, strength, and form), route, schedule, dosage, and, reason, respectively. All prompts adhered to a template with the following strucuture: task instructions, expected output FHIR templates in .JSON format, 4-5 conversion examples, a comprehensive list of codes from which the model can make selections, and then the input text. As there was no finetuning or domain-specific adaptation in our experiments, we initially had the LLM generate a small subset (N=100). Then, we manually reviewed the discrepancies between the LLM-generated FHIR output and our human annotations. Common mistakes were identified and used to refine the prompts. It's important to note that we did not have the access to the whole lists of NDC, RxNorm, and SNOMED Medication codes for drug names, as well as SNOMED Finding codes for reasons. Additionally, even if we had such comprehensive lists, they would have exceeded the token limits for LLMs. Thus, we did not task LLMs with coding these entities; instead, we instructed them to identify the contexts mentioned in the input text. For other elements, e.g. drug routes and forms, numbering in the hundreds, we allowed LLMs to directly code them. When evaluating the LLM-generated output, our primary criterion was the exact match rate, which necessitates precise alignment with human annotations in all aspects, including codes, structures, and more. Additionally, we reported precision, recall, and F1 scores for specific element occurrences. We accessed the GPT-4 APIs through the Azure OpenAI service, aligning with responsible use guidelines for MIMIC data. The specific model we used was 'gpt-4-32k' in its '2023-05-15' version. Each text input was individually transformed into a MedicationStatement resource. To optimize efficiency, we made multiple asynchronous API calls. Results and Discussions The results of annotation and FHIR generation are presented in Table 1. In summary, we annotated 3,671 medication resources, covering over 625 distinct medications and associated with 354 reasons. The Large Language Model (LLM) achieved an impressive accuracy rate of over 90% and an F1 score exceeding 0.96 across all elements. In prior studies, F1 scores reached 0.750 in timing.repeat, 0.878 in timing.route, and 0.899 in timing dosage. 1 The LLM improved these F1 scores by at least 8%. It's worth noting that the previous studies used a smaller private dataset, did not employ the strictest evaluation metrics like exact match rate, skipped terminology coding, and required extensive training. On further investigation, we were also impressed by the high accuracy in terminology coding (which essentially involves a classification task with more than 100 classes), mathematical conversion (e.g., inferring a duration of 10 days when the input mentions 'TID, dispense 30 tablets'), format conformity (with less than a 0.3% chance that the results cannot be interpreted in .JSON format), and cardinality (the LLM can handle both 1:N and 1:1 relationships). The accuracy of the output is highly dependent on the instruction prompts used. Based on our extensive trials and errors, we have the following recommendations: i) provide diverse conversion examples that encompass a wide range of heterogeneous edge cases; ii) use strong language, such as “MUST”, to ensure that the output adheres to the expected formats and rults; iii) continuously update and refine the prompts by reviewing results from a small subset, which can help identify common mistakes and enhance overall accuracy; iv) be cautious about out-of-vocabulary codings. LLMs may attempt to cater users by inventing codes that do not exist when they cannot find a close match. Conclusion In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models. Reference 1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74. 2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9. 3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9. 4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions; (2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine; (3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions; (4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions; (5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Authors: Authors: (1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions; (2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine; (3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions; (4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions; (5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Table of Links Introduction Introduction Methods Methods Results and Discussions Results and Discussions Conclusion & References Conclusion & References Introduction The integration and exchange of health data across diverse platforms and systems remain challenging due to the absence of standardized formats and a shared semantic understanding. This challenge becomes more significant when critical health information is embedded in unstructured data rather than well-organized structured formats. Standardizing unstructured health data, such as clinical notes, into FHIR resources can alleviate ambiguity across different health providers and, therefore, improve interoperability. However, it is by no means an easy task. Previous studies 1,2 have attempted to transform clinical notes into FHIR resources using a combination of natural language processing and machine learning tools through multi-step processes involving clinical named entity recognition, terminology coding, mathematical calculations, structural formatting, and human calibrations. However, these approaches require additional human effort to consolidate the results from multiple tools and have achieved only moderate performances, with F1 scores ranging from 0.7 to 0.9 in different elements. To this end, we intend to harness Large Language Models (LLMs) to directly generate FHIR-formatted resources from free-text input. The utilization of LLMs is expected to simplify the previously multi-step processes, enhance the efficiency and accuracy of automatic FHIR resource generation, and ultimately improve health data interoperability. Methods Data Annotation To the best of our knowledge, there is no largely publicly available dataset in the FHIR standard that is generated from contextual data. Therefore, we have chosen to annotate a dataset containing both free-text input and structured output in FHIR formats. The free-text input was derived from the discharge summaries of the MIMICIII datase. 3 Thanks to the 2018 n2c2 medication extraction challenge 4 , which essentially involves named entity recognition tasks, elements in medication statements have been identified. Our annotations built upon these n2c2 annotations and standardized the free text into multiple clinical terminology coding systems, such as NDC, RxNorm, and SNOMED. We organized the contexts and codes into FHIR medicationStatement resources. The converted FHIR resources underwent validation by the official FHIR validator (https://validator.fhir.org/) to ensure compliance with FHIR standards, including structure, datatype, code sets, display names, and more. These validated results were considered the gold standard transformation results and could be used to test against the LLMs. No ethical concerns exist regarding data usage, as both the MIMIC and n2c2 datasets are publicly available to authorized users. Data Annotation Large Language Model We used OpenAI's GPT-4 model as the LLM for FHIR format transformation. We used five separate prompts to instruct the LLM to transform input free text into medication (including medicationCode, strength, and form), route, schedule, dosage, and, reason, respectively. All prompts adhered to a template with the following strucuture: task instructions, expected output FHIR templates in .JSON format, 4-5 conversion examples, a comprehensive list of codes from which the model can make selections, and then the input text. As there was no finetuning or domain-specific adaptation in our experiments, we initially had the LLM generate a small subset (N=100). Then, we manually reviewed the discrepancies between the LLM-generated FHIR output and our human annotations. Common mistakes were identified and used to refine the prompts. It's important to note that we did not have the access to the whole lists of NDC, RxNorm, and SNOMED Medication codes for drug names, as well as SNOMED Finding codes for reasons. Additionally, even if we had such comprehensive lists, they would have exceeded the token limits for LLMs. Thus, we did not task LLMs with coding these entities; instead, we instructed them to identify the contexts mentioned in the input text. For other elements, e.g. drug routes and forms, numbering in the hundreds, we allowed LLMs to directly code them. When evaluating the LLM-generated output, our primary criterion was the exact match rate, which necessitates precise alignment with human annotations in all aspects, including codes, structures, and more. Additionally, we reported precision, recall, and F1 scores for specific element occurrences. We accessed the GPT-4 APIs through the Azure OpenAI service, aligning with responsible use guidelines for MIMIC data. The specific model we used was 'gpt-4-32k' in its '2023-05-15' version. Each text input was individually transformed into a MedicationStatement resource. To optimize efficiency, we made multiple asynchronous API calls. Large Language Model Results and Discussions The results of annotation and FHIR generation are presented in Table 1. In summary, we annotated 3,671 medication resources, covering over 625 distinct medications and associated with 354 reasons. The Large Language Model (LLM) achieved an impressive accuracy rate of over 90% and an F1 score exceeding 0.96 across all elements. In prior studies, F1 scores reached 0.750 in timing.repeat, 0.878 in timing.route, and 0.899 in timing dosage. 1 The LLM improved these F1 scores by at least 8%. It's worth noting that the previous studies used a smaller private dataset, did not employ the strictest evaluation metrics like exact match rate, skipped terminology coding, and required extensive training. On further investigation, we were also impressed by the high accuracy in terminology coding (which essentially involves a classification task with more than 100 classes), mathematical conversion (e.g., inferring a duration of 10 days when the input mentions 'TID, dispense 30 tablets'), format conformity (with less than a 0.3% chance that the results cannot be interpreted in .JSON format), and cardinality (the LLM can handle both 1:N and 1:1 relationships). The accuracy of the output is highly dependent on the instruction prompts used. Based on our extensive trials and errors, we have the following recommendations: i) provide diverse conversion examples that encompass a wide range of heterogeneous edge cases; ii) use strong language, such as “MUST”, to ensure that the output adheres to the expected formats and rults; iii) continuously update and refine the prompts by reviewing results from a small subset, which can help identify common mistakes and enhance overall accuracy; iv) be cautious about out-of-vocabulary codings. LLMs may attempt to cater users by inventing codes that do not exist when they cannot find a close match. Conclusion In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models. Reference 1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74. 2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9. 3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9. 4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license.

Enhancing Health Data Interoperability with Large Language Models: A FHIR Study

বৃহৎ ভাষার মডেলগুলির সাথে স্বাস্থ্য ডেটা আন্তঃক্রিয়াশীলতা বৃদ্ধি করা: একটি এফএইচআইআর স্টাডি

Verbesserung der Interoperabilität von Gesundheitsdaten mit großen Sprachmodellen: Eine FHIR-Studie

Mejora de la interoperabilidad de los datos sanitarios con modelos de lenguaje grandes: un estudio del FHIR

Améliorer l'interopérabilité des données de santé avec de grands modèles linguistiques : une étude FHIR

बड़े भाषा मॉडल के साथ स्वास्थ्य डेटा इंटरऑपरेबिलिटी को बढ़ाना: एक FHIR अध्ययन

大規模言語モデルによる医療データの相互運用性の強化: FHIR 調査

대규모 언어 모델을 통한 건강 데이터 상호 운용성 향상: FHIR 연구

Melhorando a interoperabilidade de dados de saúde com grandes modelos de linguagem: um estudo FHIR

Повышение совместимости медицинских данных с помощью больших языковых моделей: исследование FHIR

Büyük Dil Modelleriyle Sağlık Verilerinin Birlikte Çalışabilirliğini Artırma: Bir FHIR Çalışması

Nâng cao khả năng tương tác dữ liệu sức khỏe với các mô hình ngôn ngữ lớn: Nghiên cứu của FHIR

利用大型语言模型增强健康数据互操作性：FHIR 研究

**Accra, Ghana, March 12th, 2024/Chainwire/--**Gluwa’s CEO, Tae Oh, paid a courtesy visit to the Vice President of Ghana, Mahamudu Bawumia, to discuss technological opportunities within the country for improving financial inclusivity. As part of Gluwa’s continued mission to increase financial access in underserved or unbanked areas across Africa, CEO Tae Oh is honored to have had the opportunity to discuss the ongoing development of Ghana’s Central Bank Digital Currency (CBDC) with the Ghanaian Vice President, Mahamudu Bawumia, in a recent visit. In addition to providing support for the development and adoption of its CBDC, Gluwa also introduced its latest technological solution: a satellite-based Internet Service Provider (ISP) designed to expand digital access across Africa. The complementary integration of CBDCs with a satellite-based ISP aims to increase financial access within Ghana and provide a robust foundation for expanding both digital and financial economic opportunities within the country. Gluwa is a leading financial technology company responsible for major blockchain solutions including the Credal API, which has helped emerging market fintech lenders connect to millions of new users and build immutable credit reputations on-chain. The meeting follows other similar developments in Nigeria, where Gluwa recently began working with the , the eNaira. Central Bank of Nigeria as an official Partner Agent on its CBDC About Gluwa is a pioneering L1 blockchain platform designed for the documentation and matching of credit transactions. It establishes a public ledger that records credit histories and loan performances, setting the stage for a new era of interoperable cross-chain credit markets. Gluwa By collaborating with technology partners and fintech lenders, such as Aella, and engaging with financial institutions in global emerging markets, Gluwa’s Creditcoin framework is facilitating capital financing, building credit histories, and fostering trust among millions of financially underserved individuals and businesses, guided by the principles of Real World Assets (RWA). Contact Director of Marketing Alan Kong alan.kong@gluwa.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

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