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[null,"1.","Record Nr.",null,"UNINA9910460646403321"]
[null,null,"Titolo",null,"The computer-based patient record : an essential technology for health care / / Institute of Medicine. Committee on Improving the Patient Record, Institute of Medicine. Division of Health Care Services, Institute of Medicine ; Richard S. Dick and Elaine B. Steen, editors"]
[null,null,"Pubbl/distr/stampa",null,"Washington, District of Columbia : , : National Academy Press, , 1991"]
[{"$nulls":4},"©1991"]
[null,null,"ISBN",null,"0-309-29149-6"]
[null,null,"Descrizione fisica",null,"1 online resource (207 p.)"]
[null,null,"Disciplina",null,"651.5/04261/0285"]
[null,null,"Soggetti",null,"Medical records - Data processing"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,"2.","Record Nr.",null,"UNINA9910482023703321"]
[null,null,"Autore",null,"Meteren Emmanuel van <1535-1612.>"]
[null,null,"Titolo",null,"Meteranus novus, das ist: warhafftige Beschreibung dess Niederländischen Krieges: so wol was sich Denckwurtiges in dem gantzen Römischen Reich, auch in Franckreich, Hispanien, Engelland und anderen Königreichen, dessgleichen in Ost und West-indien ... zugetragen Em. von Meteren Nun aber in das Hochteutsch ubergesetzt, in 4 Thl. unterscheiden ... und biss auff das Jahr 1638 continuirt [volume 4]"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam, : Jan Jansson, 1588-1664, 1640"]
[null,null,"Descrizione fisica",null,"Online resource (4 dl, 2°)"]
[null,null,"Lingua di pubblicazione",null,"Tedesco"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Reproduction of original in Koninklijke Bibliotheek, Nationale bibliotheek van Nederland."]
[null,"3.","Record Nr.",null,"UNINA9910872189603321"]
[null,null,"Autore",null,"Longo Luca"]
[null,null,"Titolo",null,"Explainable Artificial Intelligence : Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part IV / / edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-63803-4"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (480 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 2156"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Natural language processing (Computer science)"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Natural Language Processing (NLP)"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer Communication Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"-- Explainable AI in healthcare and computational neuroscience.  -- SRFAMap: a method for mapping integrated gradients of a CNN trained with statistical radiomic features to medical image saliency maps.  -- Transparently Predicting Therapy Compliance of Young Adults Following Ischemic Stroke.  -- Precision medicine in student health: Insights from Tsetlin Machines into chronic pain and psychological distress.  -- Evaluating Local Explainable AI Techniques for the Classification of Chest X-ray Images.  -- Feature importance to explain multimodal prediction models. A clinical use case.  -- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.  -- Increasing Explainability in Time Series Classification by Functional Decomposition.  -- Towards Evaluation of Explainable Artificial Intelligence in Streaming Data.  -- Quantitative Evaluation of xAI Methods for Multivariate Time Series - A Case Study for a CNN-based MI Detection Model.  -- Explainable AI for improved human-computer interaction and Software Engineering for explainability.  -- Influenciae: A library for tracing the influence back to the data-points.  "]
[{"$nulls":4},"-- Explainability Engineering Challenges: Connecting Explainability Levels to Run-time Explainability.  -- On the Explainability of Financial Robo-advice Systems.  -- Can I trust my anomaly detection system? A case study based on explainable AI..  -- Explanations considered harmful: The Impact of misleading Explanations on Accuracy in hybrid human-AI decision making.  -- Human emotions in AI explanations.  -- Study on the Helpfulness of Explainable Artificial Intelligence.  -- Applications of explainable artificial intelligence.  -- Pricing Risk: An XAI Analysis of Irish Car Insurance Premiums.  -- Exploring the Role of Explainable AI in the Development and Qualification of Aircraft Quality Assurance Processes: A Case Study.  -- Explainable Artificial Intelligence applied to Predictive Maintenance: Comparison of Post-hoc Explainability Techniques.  -- A comparative analysis of SHAP, LIME, ANCHORS, and DICE for interpreting a dense neural network in Credit Card Fraud Detection.  -- Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data.  -- Ensuring Safe Social Navigation via Explainable Probabilistic and Conformal Safety Regions.  -- Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments.  -- AcME-AD: Accelerated Model Explanations for Anomaly Detection."]
[null,null,null,"Sommario/riassunto","This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on: Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI. Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI. Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI. Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence."]