Paul Fodor

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: There is tremendous value in being able to predict post-operative complications before and during surgery. This may allow for prevention of morbidity or mortality by the healthcare team and ultimately improve patient outcomes. While there are multiple scoring systems available to aid the physician in determining the probability of post-operative complications, none of the currently available models focus on the individual patient’s likelihood of requiring lifesaving interventions such as continued ventilatory support, extracorporeal membrane oxygenation, or continuous renal replacement therapy in the post-operative setting. Objective: Our objective is to develop clinical models that can predict whether a patient will require the above-mentioned life-saving interventions in the postoperative setting through the use of machine learning. This allows for highly individualized risk prediction, allowing a care provider to prevent, prepare, and rapidly address life-threatening postoperative complications. Methods: Four artificial neural networks (ANN) and random forest models are utilized to predict the necessity of each of the four life-saving medical devices described above. Preoperative lab data, medication data, and surgery type served as the input to the model, along with intraoperative vital signs. Each network was trained using a dataset named INSPIRE (Lim et. al), which is composed of data from 130,000 patients undergoing surgery from various surgical specialties. Model analysis wass done through both accuracy and area-under-the-curve analysis. Results: Each model demonstrated very high efficacy: Extracorporeal membrane oxygenation prediction (Accuracy: 98.9% (ANN) & 98% (Random Forest classification), AUC: .992); Ventilatory support prediction (Accuracy: 97.69% (ANN) & 99% (Random Forest classification), AUC: 0.995); Intra-aortic balloon pump (Accuracy: 98.03% (ANN) & 98.2% (Random Forest classification), AUC: 0.9921); Continuous renal replacement therapy (Accuracy: 94.93% (ANN) & 96.45% (Random Forest Classification), AUC: 0.9833). Evaluation of the random forest decision tree indicates the need of individualized patient risk prediction, an aspect of risk prediction not seen in prior art. Conclusions: Utilization of this system can lead to highly accurate post-surgical risk prediction, and can allow for more robust post-operative patient management- thus potentially decreasing morbidity and mortality in patients undergoing both elective and non-elective surgery.

Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALMFL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.

This special issue of Theory and Practice of Logic Programming consists of extended versions of five selected papers from the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019). RuleML+RR 2019 was held in conjunction with the 5th Global Conference on Artificial Intelligence, GCAI 2019, as part of the Bolzano Rules and Artificial INtelligence Summit in Bolzano, Italy, from 17 to 19 of September 2019.

The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace.Knowledge and query authoringusing natural language, especiallycontrollednatural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced theKALMsystem (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introducesKALM-QA(KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, calledMetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.

Knowledge representation and reasoning (KRR) is key to the vision of the intelligent Web. Unfortunately, wide deployment of KRR is hindered by the difficulty in specifying the requisite knowledge, which requires skills that most domain experts lack. A way around this problem could be to acquire knowledge automatically from documents. The difficulty is that, KRR requires high-precision knowledge and is sensitive even to small amounts of errors. Although most automatic information extraction systems developed for general text understandings have achieved remarkable results, their accuracy is still woefully inadequate for logical reasoning. A promising alternative is to ask the domain experts to author knowledge in Controlled Natural Language (CNL). Nonetheless, the quality of knowledge construction even through CNL is still grossly inadequate, the main obstacle being the multiplicity of ways the same information can be described even in a controlled language. Our previous work addressed the problem of high accuracy knowledge authoring for KRR from CNL documents by introducing the Knowledge Authoring Logic Machine (KALM). This paper develops the query aspect of KALM with the aim of getting high precision answers to CNL questions against previously authored knowledge and is tolerant to linguistic variations in the queries. To make queries more expressive and easier to formulate, we propose a hybrid CNL, i.e., a CNL with elements borrowed from formal query languages. We show that KALM achieves superior accuracy in semantic parsing of such queries.

Word puzzles and the problem of their representations in logic languages have received considerable attention in the last decade (Ponnuruet al. 2004; Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is the problem of generating such representations directly from natural language (NL) or controlled natural language (CNL). An interesting variation of this problem, and to the best of our knowledge, scarcely explored variation in this context, is when the input information is inconsistent. In such situations, the existing encodings of word puzzles produce inconsistent representations and break down. In this paper, we bring the well-known type of paraconsistent logics, calledAnnotated Predicate Calculus(APC) (Kifer and Lozinskii 1992), to bear on the problem. We introduce a new kind of non-monotonic semantics for APC, calledconsistency preferred stable modelsand argue that it makes APC into a suitable platform for dealing with inconsistency in word puzzles and, more generally, in NL sentences. We also devise a number of general principles to help the user choose among the different representations of NL sentences, which might seem equivalent but, in fact, behave differently when inconsistent information is taken into account. These principles can be incorporated into existing CNL translators, such as Attempto Controlled English (ACE) (Fuchset al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC with the consistency preferred stable model semantics can be equivalently embedded in ASP with preferences over stable models, and we use this embedding to implement this version of APC in Clingo (Gebseret al. 2011) and its Asprin add-on (Brewkaet al. 2015).

Word puzzles and the problem of their representations in logic languages have received considerable attention in the last decade (Ponnuru et al. 2004; Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is the problem of generating such representations directly from natural language (NL) or controlled natural language (CNL). An interesting variation of this problem, and to the best of our knowledge, scarcely explored variation in this context, is when the input information is inconsistent. In such situations, the existing encodings of word puzzles produce inconsistent representations and break down. In this paper, we bring the well-known type of paraconsistent logics, called Annotated Predicate Calculus (APC) (Kifer and Lozinskii 1992), to bear on the problem. We introduce a new kind of non-monotonic semantics for APC, called consistency preferred stable models and argue that it makes APC into a suitable platform for dealing with inconsistency in word puzzles and, more generally, in NL sentences. We also devise a number of general principles to help the user choose among the different representations of NL sentences, which might seem equivalent but, in fact, behave differently when inconsistent information is taken into account. These principles can be incorporated into existing CNL translators, such as Attempto Controlled English (ACE) (Fuchs et al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC with the consistency preferred stable model semantics can be equivalently embedded in ASP with preferences over stable models, and we use this embedding to implement this version of APC in Clingo (Gebser et al. 2011) and its Asprin add-on (Brewka et al. 2015).

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. It has evolved from an annual series of international workshops since 2002, international conferences in 2005 and 2006, and international symposia since 2007. It is the flagship event of the Rule Markup and Modeling Initiative (RuleML, http://ruleml.org), a nonprofit umbrella organization of several technical groups from academia, industry, and government working on rule technology and its applications. RuleML is the leading conference to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards (e.g., RuleML, SWRL, RIF, PRR, SBVR, DMN, CL, Prolog); rule-based event processing languages and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.