As director of a training program in medical informatics, I have found that one of the most frequent inquiries from graduate students is, "Although I am happy with my research focus and the work I have done, how can I design and carry out a practical evaluation that proves the value of my contribution?" Informatics is a multifaceted, interdisciplinary field with research that ranges from theoretical developments to projects that are highly applied and intended for near-term use in clinical settings. The implications of "proving" a research claim accordingly vary greatly depending on the details of an individual student's goals and thesis state ment. Furthermore, the dissertation work leading up to an evaluation plan is often so time-consuming and arduous that attempting the "perfect" evaluation is fre quently seen as impractical or as diverting students from central programming or implementation issues that are their primary areas of interest. They often ask what compromises are possible so they can provide persuasive data in support of their claims without adding another two to three years to their graduate student life. Our students clearly needed help in dealing more effectively with such dilem mas, and it was therefore fortuitous when, in the autumn of 1991, we welcomed two superb visiting professors to our laboratories.

10.2 The Role and Contents of the URD in an Assessment Perspective -- 10.3 The Enterprise Model -- 10.4 The Normative Model -- 10.5 Assessment of the User Requirements Document -- 10.6 Discussion -- 11 Dynamic Aspects of the Assessment Methodology -- 11.1 Dynamic Aspects of IT-Development and Application -- 11.2 Adaptation of Frames of Reference for Assessment Activities -- 11.3 Feed-forward Loops -- 11.4 Support of Context Dependent Assessment -- 11.5 Conclusion -- 12 The Dynamic Assessment Methodology -- 12.1 Philosophy -- 12.2 Application Area -- 12.3 Operationalisation of the Methodology -- 12.4 Applicable Methods -- 12.5 Summary -- 13 Discussion -- 13.1 Discussion of Fulfilment of Objective for the 4th Goal -- 13.2 Conclusion of the Study -- References -- Appendix 1: Vocabulary -- Appendix 2: Abbreviations & Acronyms -- Appendix 3: KAVAS's & ISAR's Evaluation Methodology -- Appendix 4: Methodology for Assessment of Functionality -- Appendix 5: Experimental Observations: Functionality Assessment -- Appendix 6: Experimental Observations: LFA -- Appendix 7: Causal Analysis of Experimental Observations -- Appendix 8: Method for Elicitation of a Strategy -- Appendix 9: Selected References regarding Assessment Methods

Heavily updated and revised from the successful first edition Appeals to a wide range of informatics professionals, from students to on-site medical information system administrators Includes case studies and real world system evaluations References and self-tests for feedback and motivation after each chapter Great for teaching purposes, the book is recommended for courses offered at universities such as Columbia University Precise definition and use of terms

Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research. - Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications - Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. - Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.

Successful digital healthcare depends on the effective flow of a complete chain of information; from the sensor, via multiple steps of processing, to the actuator, which can be anything from a human healthcare professional to a robot. Along this pathway, methods for automating the processing of information, like signal processing, machine learning, predictive analytics and decision support, play an increasing role in providing actionable information and supporting personalized and preventive healthcare concepts in both biomedical and digital healthcare systems and applications. ICT systems in healthcare and biomedical systems and devices are very closely related, and in the future they will become increasingly intertwined. Indeed, it is already often difficult to delineate where the one ends and the other begins. This book presents the intended proceedings of the dHealth 2020 annual conference on the general topic of health Informatics and digital health, which was due to be held in Vienna, Austria, on 19 and 20 May 2020, but which was cancelled due to the COVID-19 pandemic. The decision was nevertheless taken to publish these proceedings, which include the 40 papers which would have been delivered at the conference. The special topic for the 2020 edition of the conference was Biomedical Informatics for Health and Care. The book provides an overview of current developments in health informatics and digital health, and will be of interest to researchers and healthcare practitioners alike.

Heavily updated and revised from the successful first edition Appeals to a wide range of informatics professionals, from students to on-site medical information system administrators Includes case studies and real world system evaluations References and self-tests for feedback and motivation after each chapter Great for teaching purposes, the book is recommended for courses offered at universities such as Columbia University Precise definition and use of terms

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. - Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring - Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making - Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis