Artificial Intelligence for Hospital Health Care:Application Cases and Answers to Challenges in European Hospitals

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Investigating the Origins of Cancer in the Intestinal Crypt with a Gene Network Agent Based Hybrid Model

Colorectal cancer (CRC) is the second most common tumour in the world (Bray, 2018). It has been proposed that morbidity and mortality could be mitigated by screening methods that identify key genetic mutations in the DNA of a patient’s biosample (Traverso, 2002). However, for this to work, a theoretical understanding of the most likely mutations that initiate malignant transformation, and how they affect subsequent microevolution, is needed. Specifically, we hypothesise that there is a CRC-proliferative mutation that is more likely to be initially fixated in the crypt. To investigate this, we developed an agent-based model of cells in the colon crypt that shows emergent biological homeostasis at the tissue level from the cellular and molecular interactions. We equipped each of the cells with a molecular gene network which, in their wildtype state, regulates homeostasis in the crypt and recapitulates known behaviour. We identified and modelled key genes implicated in CRC which, when mutated, alter the rate of death and division of cells. We used this model to study the biological first principles of the fixation of mutations, offering key spatial and temporal understanding of this process. We discuss the impact and clinical relevance of proliferative genetic mutations in isolation, pointing to the KRAS gene as a likely mutation to be initially fixed in the crypt