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Background: The increased use of digital data in health research demands interdisciplinary collaborations to address its methodological complexities and challenges. This often entails merging the linear deductive approach of health research with the explorative iterative approach of data science. However, there is a lack of structured teaching courses and guidance on how to effectively and constructively bridge different disciplines and research approaches., Objective: This study aimed to provide a set of tools and recommendations designed to facilitate interdisciplinary education and collaboration. Target groups are lecturers who can use these tools to design interdisciplinary courses, supervisors who guide PhD and master's students in their interdisciplinary projects, and principal investigators who design and organize workshops to initiate and guide interdisciplinary projects., Methods: Our study was conducted in 3 steps: (1) developing a common terminology, (2) identifying established workflows for research question formulation, and (3) examining adaptations of existing study workflows combining methods from health research and data science. We also formulated recommendations for a pragmatic implementation of our findings. We conducted a literature search and organized 3 interdisciplinary expert workshops with researchers at the University of Zurich. For the workshops and the subsequent manuscript writing process, we adopted a consensus study methodology., Results: We developed a set of tools to facilitate interdisciplinary education and collaboration. These tools focused on 2 key dimensions- content and curriculum and methods and teaching style-and can be applied in various educational and research settings. We developed a glossary to establish a shared understanding of common terminologies and concepts. We delineated the established study workflow for research question formulation, emphasizing the "what" and the "how," while summarizing the necessary tools to facilitate the process. We propose 3 clusters of contextual and methodological adaptations to this workflow to better integrate data science practices: (1) acknowledging real-life constraints and limitations in research scope; (2) allowing more iterative, data-driven approaches to research question formulation; and (3) strengthening research quality through reproducibility principles and adherence to the findable, accessible, interoperable, and reusable (FAIR) data principles., Conclusions: Research question formulation remains a relevant and useful research step in projects using digital data. We recommend initiating new interdisciplinary collaborations by establishing terminologies as well as using the concepts of research tasks to foster a shared understanding. Our tools and recommendations can support academic educators in training health professionals and researchers for interdisciplinary digital health projects., (©Jana Sedlakova, Mina Stanikić, Felix Gille, Jürgen Bernard, Andrea B Horn, Markus Wolf, Christina Haag, Joel Floris, Gabriela Morgenshtern, Gerold Schneider, Aleksandra Zumbrunn Wojczyńska, Corine Mouton Dorey, Dominik Alois Ettlin, Daniel Gero, Thomas Friemel, Ziyuan Lu, Kimon Papadopoulos, Sonja Schläpfer, Ning Wang, Viktor von Wyl. Originally published in JMIR Medical Education (https://mededu.jmir.org), 23.01.2025.)

Objectives: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical exploratory sensor data analysis., Materials and Methods: Following a user-centered design approach, we learned that clinicians have 3 priorities for generating insights for the Barka-MS study data: exploration and search for, and contextualization of, sequences and patterns in patient sleep and activity. We compute meaningful sequences for patients using clustering and proximity search, displaying these with an interactive visual interface composed of coordinated views. Our evaluation posed both closed and open-ended tasks to participants, utilizing a scoring system to gauge the tool's usability, and effectiveness in supporting insight generation across 15 clinicians, data scientists, and non-experts., Results and Discussion: We present MS Pattern Explorer, a visual analytics system that helps clinicians better address complex data-centric challenges by facilitating the understanding of activity patterns. It enables innovative analysis that leads to rapid insight generation and contextualization of temporal activity data, both within and between patients of a cohort. Our evaluation results indicate consistent performance across participant groups and effective support for insight generation in MS patient fitness tracker data. Our implementation offers broad applicability in clinical research, allowing for potential expansion into cohort-wide comparisons or studies of other chronic conditions., Conclusion: MS Pattern Explorer successfully reduces the signal overload clinicians currently experience with activity data, introducing novel opportunities for data exploration, sense-making, and hypothesis generation., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice.

Background & Aims: Adult-to-adult living donor liver transplantation (LDLT) offers an opportunity to decrease the liver transplant waitlist and reduce waitlist mortality. We sought to compare donor and recipient characteristics and post-transplant outcomes after LDLT in the US, the UK, and Canada., Methods: This is a retrospective multicenter cohort-study of adults (≥18-years) who underwent primary LDLT between Jan-2008 and Dec-2018 from three national liver transplantation registries: United Network for Organ Sharing (US), National Health Service Blood and Transplantation (UK), and the Canadian Organ Replacement Registry (Canada). Patients undergoing retransplantation or multi-organ transplantation were excluded. Post-transplant survival was evaluated using the Kaplan-Meier method, and multivariable adjustments were performed using Cox proportional-hazards models with mixed-effect modeling., Results: A total of 2,954 living donor liver transplants were performed (US: n = 2,328; Canada: n = 529; UK: n = 97). Canada has maintained the highest proportion of LDLT utilization over time (proportion of LDLT in 2008 - US: 3.3%; Canada: 19.5%; UK: 1.7%; p <0.001 - in 2018 - US: 5.0%; Canada: 13.6%; UK: 0.4%; p <0.001). The 1-, 5-, and 10-year patient survival was 92.6%, 82.8%, and 70.0% in the US vs. 96.1%, 89.9%, and 82.2% in Canada vs. 91.4%, 85.4%, and 66.7% in the UK. After adjustment for characteristics of donors, recipients, transplant year, and treating transplant center as a random effect, all countries had a non-statistically significantly different mortality hazard post-LDLT (Ref US: Canada hazard ratio 0.53, 95% CI 0.28-1.01, p = 0.05; UK hazard ratio 1.09, 95% CI 0.59-2.02, p = 0.78)., Conclusions: The use of LDLT has remained low in the US, the UK and Canada. Despite this, long-term survival is excellent. Continued efforts to increase LDLT utilization in these countries may be warranted due to the growing waitlist and differences in allocation that may disadvantage patients currently awaiting liver transplantation., Lay Summary: This multicenter international comparative analysis of living donor liver transplantation in the United States, the United Kingdom, and Canada demonstrates that despite low use of the procedure, the long-term outcomes are excellent. In addition, the mortality risk is not statistically significantly different between the evaluated countries. However, the incidence and risk of retransplantation differs between the countries, being the highest in the United Kingdom and lowest in the United States., Competing Interests: Conflict of interest Gonzalo Sapisochin discloses consultancy for Astra-Zeneca, Roche, Novartis, and Integra. Gonzalo Sapisochin has received financial compensation for talks for Roche, Astra-Zeneca, Chiesi, and Integra. Gonzalo Sapisochin has received a grant from Roche. None of the other authors have any conflicts of interest to declare. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2022 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Background and Aims: Following liver resection (LR) for HCC, the likelihood of survival is dynamic, in that multiple recurrences and/or metastases are possible, each having variable impacts on outcomes. We sought to evaluate the natural progression, pattern, and timing of various disease states after LR for HCC using multistate modeling and to create a practical calculator to provide prognostic information for patients and clinicians., Approach and Results: Adult patients undergoing LR for HCC between January 2000 and December 2018 were retrospectively identified at a single center. Multistate analysis modeled post-LR tumor progression by describing transitions between distinct disease states. In this model, the states included surgery, intrahepatic recurrence (first, second, third, fourth, fifth), distant metastasis with or without intrahepatic recurrence, and death. Of the 486 patients included, 169 (34.8%) remained recurrence-free, 205 (42.2%) developed intrahepatic recurrence, 80 (16.5%) developed distant metastasis, and 32 (7%) died. For an average patient having undergone LR, there was a 33.1% chance of remaining disease-free, a 31.0% chance of at least one intrahepatic recurrence, a 16.3% chance of distant metastasis, and a 19.8% chance of death within the first 60 months post-LR. The transition probability from surgery to first intrahepatic recurrence, without a subsequent state transition, increased from 3% (3 months) to 17.4% (30 months) and 17.2% (60 months). Factors that could modify these probabilities included tumor size, satellite lesions, and microvascular invasion. The online multistate model calculator can be found on https://multistatehcc.shinyapps.io/home/., Conclusions: In contrast to standard single time-to-event estimates, multistate modeling provides more realistic prognostication of outcomes after LR for HCC by taking into account many postoperative disease states and transitions between them. Our multistate modeling calculator can provide meaningful data to guide the management of patients undergoing postoperative surveillance and therapy., (© 2022 American Association for the Study of Liver Diseases.)