Background: Ventilation management may differ between COVID–19 ARDS (COVID–ARDS) patients and patients with pre–COVID ARDS (CLASSIC–ARDS); it is uncertain whether associations of ventilation management with outcomes for CLASSIC–ARDS also exist in COVID–ARDS. Methods: Individual patient data analysis of COVID–ARDS and CLASSIC–ARDS patients in six observational studies of ventilation, four in the COVID–19 pandemic and two pre–pandemic. Descriptive statistics were used to compare epidemiology and ventilation characteristics. The primary endpoint were key ventilation parameters; other outcomes included mortality and ventilator–free days and alive (VFD–60) at day 60. Results: This analysis included 6702 COVID–ARDS patients and 1415 CLASSIC–ARDS patients. COVID–ARDS patients received lower median VT (6.6 [6.0 to 7.4] vs 7.3 [6.4 to 8.5] ml/kg PBW; p < 0.001) and higher median PEEP (12.0 [10.0 to 14.0] vs 8.0 [6.0 to 10.0] cm H2O; p < 0.001), at lower median ΔP (13.0 [10.0 to 15.0] vs 16.0 [IQR 12.0 to 20.0] cm H2O; p < 0.001) and higher median Crs (33.5 [26.6 to 42.1] vs 28.1 [21.6 to 38.4] mL/cm H2O; p < 0.001). Following multivariable adjustment, higher ΔP had an independent association with higher 60–day mortality and less VFD–60 in both groups. Higher PEEP had an association with less VFD–60, but only in COVID–ARDS patients. Conclusions: Our findings show important differences in key ventilation parameters and associations thereof with outcomes between COVID–ARDS and CLASSIC–ARDS. Trial registration: Clinicaltrials.gov (identifier NCT05650957), December 14, 2022.
In my previous post on AI engineering I defined the concepts involved in this new discipline and explained that with the current state of the practice, AI engineers could also be named machine learning (ML) engineers. In this post I would like to 1) define our view on the profession of applied AI engineer and 2) present the toolbox of an AI engineer with tools, methods and techniques to defy the challenges AI engineers typically face. I end this post with a short overview of related work and future directions. Attached to it is an extensive list of references and additional reading material.
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Abstract for World Physiotherapy Congress 2021Title Ethical Considerations of Using Machine Learning for Decision Support in Occupational Physical Therapy: a narrative literature study and ethical deliberation. Authors Marianne W. M. C. Six Dijkstra1,4,7 · Egbert Siebrand2 · Steven Dorrestijn2 · Etto L. Salomons3 ·Michiel F. Reneman4 · Frits G. J. Oosterveld1 · Remko Soer1,5 · Douglas P. Gross6 · Hendrik J. Bieleman1 Presenter and contactName: Marianne W. M. C. Six DijkstraEmail: w.m.c.sixdijkstra@saxion.nlAdres: School of Health, Saxion University of Applied Sciences/AGZ, M.H. Tromplaan 28, 7500 KB, Enschede, The NetherlandsTel: +31(0)612379329 1 School of Health, Saxion University of AppliedSciences, Enschede, The Netherlands2 Research Group Ethics & Technology, Saxion Universityof Applied Sciences, Enschede, The Netherlands3 School of Ambient Intelligence, Saxion Universityof Applied Sciences, Enschede, The Netherlands4 Department of Rehabilitation Medicine, University MedicalCenter Groningen, University of Groningen, Groningen,The Netherlands5 University Medical Center Groningen, Pain Centre,University of Groningen, Groningen, The Netherlands6 Department of Physical Therapy, University of Alberta,Edmonton, Canada7 University of Groningen, Groningen, The Netherlands Funding This study was funded by Netherlands Organisation for Scientific Research (NWO) (023.011.076) and Saxion University of Applied Sciences in The Netherlands. The funding source had no involvementin study design, data collection, analysis or interpretation, in the writing of the report, or the decision to submit the article for publication.Ethical approvalThis study is part of a PhD project entitled “Development of a Decision Support System – Artificial Intelligence advices for Sustainable Employability”. The Ethics Board at the University Medical Center Groningen in The Netherlands decided that formal approval of the study was not necessary because all workers were subjected to care as usual only.AbstractBackground Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within physical therapy. This will change the interaction between therapists and their clients, with unknown consequences.Purpose The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs). We used an example in the context of occupational physical therapy.Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario in occupational physical therapy to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. The reflection was guided by the Product Impact Tool.
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Om tegemoet te komen aan de eisen die gesteld worden aan werknemers in de huidig snel veranderende samenleving heeft de NHL Stenden Hogeschool gekozen voor een nieuw onderwijsconcept, namelijk Design Based Education (DBE). DBE is gebaseerd op het gedachtegoed van Design Thinking en stimuleert iteratieve en creatieve denkprocessen. DBE is een student-georiënteerde leeromgeving, gebaseerd op praktijk-, dialoog-, en vraaggestuurde onderwijsprincipes en op zelfsturend, constructief, contextueel en samenwerkend leren. Studenten construeren gezamenlijk kennis en ontwikkelen een prototype voor een praktijkvraagstuk. Student-georiënteerde leeromgevingen vragen andere begeleidingsstrategieën van docenten dan zij gewend zijn. Van docenten wordt verwacht dat zij studenten activeren gezamenlijk kennis te construeren en dat zij nauw samenwerken met werkveldprofessionals. Eerder onderzoek toont aan dat docenten, zelfs in een student-georiënteerde leeromgeving, geneigd zijn terug te vallen op conventionele strategieën. De overstap naar een ander onderwijsconcept gaat dus blijkbaar niet vanzelf. Collectief leren stimuleert docenten de dialoog aan te gaan met andere docenten en werkveldprofessionals met als doel gezamenlijk te experimenteren en collectief te handelen. De centrale vraag van het postdoc-onderzoek is het ontwerpen en ontwikkelen van (karakteristieken van) interventies die collectief leren van docenten en werkveldprofessionals stimuleren. Het doel van het postdoconderzoek is om de overstap naar DBE zo probleemloos mogelijk te laten verlopen door docenten te ondersteunen DBE leeromgevingen te ontwikkelen in samenwerking met werkveldprofessionals en DBE te integreren in hun docentactiviteiten. De onderzoeksmethode is Educational Design Research en bestaat uit vier fasen: preliminair onderzoek, ontwikkelen van prototypes, evaluatie en bijdrage aan de praktijk. Het onderzoek is verbonden aan het lectoraat Sustainable Educational Concepts in Higher Education en wordt hiërarchisch en inhoudelijk aangestuurd door de lector. Docenten, experts, werkveldprofessionals en studenten worden betrokken bij het onderzoek. Dit onderzoek kan zowel binnen als buiten de hogeschool een bijdrage leveren omdat steeds meer hogescholen kiezen voor een ander onderwijsconcept.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
