Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Introduction <s> In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other hand, due to neurule-based efficient inference mechanism, conclusions can be reached more efficiently. In addition, neurule-based inferences can be performed even if some of the inputs are unknown, in contrast to symbolic rule-based inferences. Furthermore, an existing symbolic rule-base with indexed exception cases can be converted into a neurule-base with corresponding indexed exception cases. Finally, empirical data can be used as a knowledge source, which facilitates knowledge acquisition. We also present a new high-level categorization of the approaches integrating rule-based and case-based reasoning. <s> BIB001 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Introduction <s> Objectives: Clinical decision-support systems (CDSSs) are being recognized as important tools for improving quality of care. In this paper, we review the literature to find trends in CDSSs that were developed over the last few decades and give some indication of future directions in developing successful, usable clinical decisionsupport systems. Methods: We searched PubMed for papers that were published during the past five years with the words Decision Support Systems appearing in the title and used our own knowledge of the field for earlier work. Results: The goals of developers of modern CDSSs are to develop systems that deliver needed information and could be integrated with the healthcare’s organizational dynamics. Such CDSSs form part of knowledge-management activities that healthcare organizations employ in order to excel. During the past few decades, we have witnessed a gradual maturation of knowledge representation formalisms and the needed infrastructure for developing integrated CDSSs, including electronic health record systems (EHR), standard terminologies, and messaging standards for exchange of clinical data. The demand for CDSSs that are effective and that will evolve as circumstances change gave rise to methodologies that guide developers on the construction and evaluation of CDSSs. Conclusion: Although there exist many approaches for representing, managing and delivering clinical knowledge, the design and implementation of good and useful systems that will last and evolve are still active areas of research. The gradual maturation of EHR and infrastructure standards should make it possible for CDSSs implementers to make major contributions to the delivery of healthcare. Haux R, Kulikowski C, editors. IMIA Yearbook of Medical Informatics 2006. Methods Inf Med 2006; 45 Suppl 1: S72-80. <s> BIB002 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Introduction <s> Display Omitted Combined operation of MAS, CBR and ontologies can aide clinical decision-making.MAS and its agent types define rules and interfaces for knowledge representation.MAS ensures the cooperation between heterogeneous medical knowledge bases.CBR restores experience to solve similar issues and to improve knowledge retrieval.Ontology interprets user requests for question answering between MAS and user. This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts. <s> BIB003 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Introduction <s> Abstract Objective Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the " decay rate " of clinical data source relevance. Materials and methods We trained a clinical order recommender system, analogous to Netflix or Amazon's " Customers who bought A also bought B... " product recommenders, based on a tertiary academic hospital's structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Results Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P −10 ). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009–2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a " half-life " of data relevance about 4 months. Discussion Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Conclusions and relevancm Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. <s> BIB004
Clinical decision-support systems (DSSs) aim to enhance healthcare decision-making, with the final objective to improve the quality of care provided by healthcare organizations  BIB002 . Since earlier years, clinical DSSs have been often conceived as applications of Artificial Intelligence (AI) methodologies to medical domains  . Examples range from the so-called "expert systems" based on rules  , to approaches relying on models of deeper and more principled human reasoning, like, e.g., causal reasoning  , to more recent proposals exploiting fuzzy logic, Bayesian networks, case-based reasoning (CBR), and other techniques (see, e.g., BIB001 BIB003 ). AI-based DSSs have been used as aids across a wide range of medical tasks among various medical specialties. According to the Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe  , AI methodologies can be divided into two fundamentally different categories: knowledge-based AI and data-driven AI. Knowledge-based AI consists in "an attempt to model human knowledge in computational terms, starting in a top-down fashion from human self-reporting of what concepts and knowledge individuals use to solve problems or answer queries in a domain of expertise, including common sense knowledge", and "formalizes and operationalizes this knowledge in terms of software. It rests primarily on highly sophisticated but now quite standard symbolic computing technologies and has already had a huge impact"  . Data-driven AI, on the other hand, is characterized by "starting in a bottom-up fashion from large amounts of data of human activity, which are processed with statistical machine learning methods […] in order to abstract patterns that can then be used to make predictions, complete partial data, or emulate human behaviour in similar conditions in the past. Data-driven AI requires big data and very substantial computing power to reach adequate performance levels"  . Knowledge-based methodologies are well established, but less able to exploit large volumes of data, and to automatically build a knowledge model by generalizing from data themselves. In fact, knowledge models often remain human-developed. Knowledge acquisition and formalization is thus a "bottleneck", which consumes development time and requires a significant initial effort. On the other hand, data-driven methodologies are currently receiving a lot of attention, thanks to the large amount of data available in electronic form, to the availability of powerful computing architectures, and to the significant advancement of machine learning techniques that are able to extract characteristic features and to identify patterns from data with a high level of accuracy. Interestingly enough, Chen et al. BIB004 have recently analysed this AI category distinction, referring to the field of clinical DSSs. They have observed that the knowledge-based approach to clinical decision support is limited in scale, due to the lack of evidence in some domains as well as to the cost of human knowledge authoring processes. On the contrary, taking advantage of the accumulated clinical data, stored, e.g., in electronic patient records, powerful datadriven clinical decision support systems can be implemented, possibly leading to more effective outcomes in practice. Data-driven approaches, however, might lack in transparency and explainability, while, as highlighted by Shortliffe et al.  , a clinical DSS should enable users to "understand the basis for any advice or recommendations that are offered". In this survey, we analyse the latest literature contributions to clinical DSSs (years 2017-2018), focusing on approaches that adopt AI techniques. Our main goal is to understand if "classical" knowledge-based DSSs are still being proposed, or if, as in other domains, earlier AI principles and methods are less represented, in favour of big data analytics and machine learning approaches. The challenges of the surveyed methodological choices are analysed, with a particular focus on the transparency and explainability issues, nowadays recognized as central themes to be addressed by AI and medical informatics research, as mentioned above. Promises deriving from synergies between the two AI categories are also discussed.