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Explainable AI in Medical Informatics and Decision Support

Call for papers

Based on a successful workshop on explainable AI during the Cross Domain for Machine Learning and Knowledge Extraction (CD-MAKE) 2018 conference, we launch this call for a special issue at BMC Medical Informatics and Decision Making, with the possibility to present the papers at the next session on explainable AI during the CD-MAKE 2020 conference in Dublin (Ireland) at the end of August 2020.

New Content Item

We want to inspire cross-domain experts interested in artificial intelligence/machine learning to stimulate research, engineering and evaluation in, around and for explainable AI - towards making machine decisions transparent, re-enactive, comprehensible, interpretable, thus explainable, re-traceable and reproducible; the latter is the cornerstone of scientific research per se!

We foster cross-disciplinary and interdisciplinary work including but not limited to:

  • Novel methods, algorithms, tools for supporting explainable AI
  • Proof-of-concepts and demonstrators of how to integrate explainable AI into workflows
  • Frameworks, architectures, algorithms and tools to support post-hoc and ante-hoc explainability and causality machine learning
  • Theoretical approaches of explainability ("What is a good explanation?")
  • Towards argumentation theories of explanation and issues of cognition
  • Comparison Human intelligence vs. Artificial Intelligence (HCI -- KDD)
  • Interactive machine learning with human(s)-in-the-loop (crowd intelligence)
  • Explanation User Interfaces and Human-Computer Interaction (HCI) for explainable AI
  • Novel Intelligent User Interfaces and affective computing approaches
  • Fairness, accountability and trust
  • Ethical aspects, law and social responsibility
  • Business aspects of explainable AI
  • Self-explanatory agents and decision support systems
  • Explanation agents and recommender systems
  • Combination of statistical learning approaches with large knowledge repositories (ontologies)

The grand goal of future explainable AI is to make results understandable and transparent  and to answer questions of how and why a result was achieved. In fact: “Can we explain how and why a specific result was achieved by an algorithm?”

Submission for this special issue is open until 30th December 2020. The special issue is overseen by Section Editor Andreas Holzinger.

Authors who aspire to present their paper at the CD-MAKE 2020 conference should submit their manuscript before 30th April 2020. The call for papers will remain open throughout 2020 to allow high quality manuscripts from the CD-MAKE conference in August 2020 to be included and bring together a unique and high quality collection of work on explainable AI.

For further information, please click here. For an introduction to the topic, please see the online content provided by Prof. Holzinger. 

For pre-submission enquiries, please contact the in-house Editor (Alison Cuff) at bmcmedinformdecismak@biomedcentral.com.

  1. Health Information System is the key to making evidence-based decisions. Ethiopia has been implementing the Health Management Information System (HMIS) since 2008 to collect routine health data and revised it ...

    Authors: Moges Asressie Chanyalew, Mezgebu Yitayal, Asmamaw Atnafu and Binyam Tilahun

    Citation: BMC Medical Informatics and Decision Making 2021 21:28

    Content type: Research article

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  2. Early alarm of hypoglycemia, detection of asymptomatic hypoglycemia, and effective control of blood glucose fluctuation make a great contribution to diabetic treatment. In this study, we designed a multi-level...

    Authors: Xia Yu, Ning Ma, Tao Yang, Yawen Zhang, Qing Miao, Junjun Tao, Hongru Li, Yiming Li and Yehong Yang

    Citation: BMC Medical Informatics and Decision Making 2021 21:22

    Content type: Research article

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  3. Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in ce...

    Authors: Julia Amann, Alessandro Blasimme, Effy Vayena, Dietmar Frey and Vince I. Madai

    Citation: BMC Medical Informatics and Decision Making 2020 20:310

    Content type: Research article

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  4. Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devo...

    Authors: Huaxin Pang, Shikui Wei, Yufeng Zhao, Liyun He, Jian Wang, Baoyan Liu and Yao Zhao

    Citation: BMC Medical Informatics and Decision Making 2020 20:264

    Content type: Research article

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  5. Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descr...

    Authors: Enrico Mensa, Davide Colla, Marco Dalmasso, Marco Giustini, Carlo Mamo, Alessio Pitidis and Daniele P. Radicioni

    Citation: BMC Medical Informatics and Decision Making 2020 20:263

    Content type: Research article

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  6. A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated...

    Authors: Jasminka Hasic Telalovic and Azra Music

    Citation: BMC Medical Informatics and Decision Making 2020 20:262

    Content type: Research article

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  7. There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, approaches...

    Authors: Amie J. Barda, Christopher M. Horvat and Harry Hochheiser

    Citation: BMC Medical Informatics and Decision Making 2020 20:257

    Content type: Research article

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  8. We focus on the importance of interpreting the quality of the labeling used as the input of predictive models to understand the reliability of their output in support of human decision-making, especially in cr...

    Authors: Federico Cabitza, Andrea Campagner and Luca Maria Sconfienza

    Citation: BMC Medical Informatics and Decision Making 2020 20:219

    Content type: Research article

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  9. One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a signi...

    Authors: Peng-Nien Yin, Kishan KC, Shishi Wei, Qi Yu, Rui Li, Anne R. Haake, Hiroshi Miyamoto and Feng Cui

    Citation: BMC Medical Informatics and Decision Making 2020 20:162

    Content type: Research article

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  10. Infectious diseases that can cause epidemics, such as COVID-19, SARS-CoV, and MERS-CoV, constitute a major social issue, with healthcare providers fearing secondary, tertiary, and even quaternary infections. T...

    Authors: Dong-won Kim, Jin-young Choi and Keun-hee Han

    Citation: BMC Medical Informatics and Decision Making 2020 20:106

    Content type: Research article

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  11. In this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for...

    Authors: Tingting Zhang, Yaqiang Wang, Xiaofeng Wang, Yafei Yang and Ying Ye

    Citation: BMC Medical Informatics and Decision Making 2020 20:64

    Content type: Research article

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  12. A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precis...

    Authors: Priscilla Machado do Nascimento, Inácio Gomes Medeiros, Raul Maia Falcão, Beatriz Stransky and Jorge Estefano Santana de Souza

    Citation: BMC Medical Informatics and Decision Making 2020 20:52

    Content type: Technical advance

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  13. The penetration level of mobile technology has grown exponentially and is part of our lifestyle, at all levels. The use of the smartphone has opened up a new horizon of possibilities in the treatment of health...

    Authors: A. Hernández-Reyes, G. Molina-Recio, R. Molina-Luque, M. Romero-Saldaña, F. Cámara-Martos and R. Moreno-Rojas

    Citation: BMC Medical Informatics and Decision Making 2020 20:40

    Content type: Study protocol

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  14. Cloud storage facilities (CSF) has become popular among the internet users. There is limited data on CSF usage among university students in low middle-income countries including Sri Lanka. In this study we pre...

    Authors: Samankumara Hettige, Eshani Dasanayaka and Dileepa Senajith Ediriweera

    Citation: BMC Medical Informatics and Decision Making 2020 20:10

    Content type: Research article

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  15. In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false...

    Authors: André M. Carrington, Paul W. Fieguth, Hammad Qazi, Andreas Holzinger, Helen H. Chen, Franz Mayr and Douglas G. Manuel

    Citation: BMC Medical Informatics and Decision Making 2020 20:4

    Content type: Research article

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  16. Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guideli...

    Authors: Zhidong Zhao, Yanjun Deng, Yang Zhang, Yefei Zhang, Xiaohong Zhang and Lihuan Shao

    Citation: BMC Medical Informatics and Decision Making 2019 19:286

    Content type: Research article

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  17. Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models...

    Authors: Rawan AlSaad, Qutaibah Malluhi, Ibrahim Janahi and Sabri Boughorbel

    Citation: BMC Medical Informatics and Decision Making 2019 19:214

    Content type: Research Article

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