A most general refinement operator for reduced sentences

Many learning systems use the space of logic formulas as the search space of hypotheses. To build efficient systems, the set of first order logic formulas can be reduced in many ways. Most systems restrict themselves to (subsets of) Horn clauses. In this paper we investigate the space of reduced first order sentences, which has the same expressive power as an arbritrary first order logic. Shapiro [1981] has used the subset of reduced first order sentences to define a most general refinement operator. His operator is claimed to be complete, i.e., all reduced sentences are derivable from the empty sentence. In this article we will show that his operator is not complete and propose a new, complete refinement operator for reduced first order sentences

Simple improvements of a simple solution for inverting resolution

In this paper we address some simple improvements of the algorithm of Rouveirol and Puget [1989] for inverting resolution. Their approach is based on automatic change of representation called flattening and unflattening of clauses in a logic program. This enables a simple implementation of operators, such as Absorption, presented in Muggleton and Buntine [1988]. Unfortunately both the algorithms of MB and RP are incomplete. We analyze the reasons of the incompleteness of the RP algorithm and present an improved Absorption operator. It appears that flat tree epresentations of clauses and predicate calculus with equality provide an appropriate context for these matters

Constructing refinement operators by decomposing logical implication

Inductive learning models [Plotkin 1971; Shapiro 1981] often use a search space of clauses, ordered by a generalization hierarchy. To find solutions in the model, search algorithms use different generalization and specialization operators. In this article we will decompose the quasi-ordering induced by logical implication into six increasingly weak orderings. The difference between two successive orderings will be small, and can therefore be understood easily. Using this decomposition, we will describe upward and downward refinement operators for all orderings, including $theta$-subsumption and logical implication

Factors associated with successful rehabilitation in older adults: A systematic review and best evidence synthesis

Purpose; Returning to community living is an indicator for successful rehabilitation in older adults admittedto geriatric rehabilitation. Predicting successful rehabilitation could contribute to the deployment of earlydischarge planning, and leads to a more custom-made rehabilitation trajectory. This review aims to presentan overview of factors associated with successful rehabilitation following inpatient geriatric rehabilitation.Method; A systematic literature review was conducted in PubMed, CINAHL and Embase. Extracted factorswere analysed via Bakker’s five levels of evidence.Results; Nine studies with methodological quality of good to moderate were included. For 13 of the 18 extractedfactors, limited (n=3),moderate (n=5) and conflicting (n=5) evidence found a significant association.Conclusions; Caregiver, comorbidities, motor-function, nutritional status, time from onset are significantlyrelated to successful rehabilitation. These factors could support healthcare professionals to indicate successfulrehabilitation at admission and contributes to deployment of early discharge planning and developmentof more custom-made rehabilitation trajectories.Public Health and primary careGeriatrics in primary car

Improving the efficiency of ILP systems

Inductive Logic Programming (ILP) is a promising technol-ogy for knowledge extraction applications. ILP has produced intelligiblesolutions for a wide variety of domains where it has been applied. TheILP lack of eciency is, however, a major impediment for its scalabilityto applications requiring large amounts of data. In this paper we pro-pose a set of techniques that improve ILP systems eciency and makethen more likely to scale up to applications of knowledge extraction fromlarge datasets. We propose and evaluate the lazy evaluation of examples,to improve the eciency of ILP systems. Lazy evaluation is essentiallya way to avoid or postpone the evaluation of the generated hypotheses(coverage tests).The techniques were evaluated using the IndLog system on ILP datasetsreferenced in the literature. The proposals lead to substantial eficiencyimprovements and are generally applicable to any ILP system

Repairing Socially Aggregated Ontologies Using Axiom Weakening

Ontologies represent principled, formalised descriptions of agents’ conceptualisations of a domain. For a community of agents, these descriptions may differ among agents. We propose an aggregative view of the integration of ontologies based on Judgement Aggregation (JA). Agents may vote on statements of the ontologies, and we aim at constructing a collective, integrated ontology, that reflects the individual conceptualisations as much as possible. As several results in JA show, many attractive and widely used aggregation procedures are prone to return inconsistent collective ontologies. We propose to solve the possible inconsistencies in the collective ontology by applying suitable weakenings of axioms that cause inconsistencies

Monitoring Community Pharmacist's Quality of Care: A feasibility study of using pharmacy claims data to assess performance

Contains fulltext : 98109.pdf (publisher's version ) (Open Access)BACKGROUND: Public pressure has increasingly emphasized the need to ensure the continuing quality of care provided by health professionals over their careers. Health profession's regulatory authorities, mandated to be publicly accountable for safe and effective care, are revising their quality assurance programs to focus on regular evaluations of practitioner performance. New methods for routine screening of performance are required and the use of administrative data for measuring performance on quality of care indicators has been suggested as one attractive option. Preliminary studies have shown that community pharmacy claims databases contain the information required to operationalize quality of care indicators. The purpose of this project was to determine the feasibility of routine use of information from these databases by regulatory authorities to screen the quality of care provided at community pharmacies. METHODS: Information from the Canadian province of Quebec's medication insurance program provided data on prescriptions dispensed in 2002 by more than 5000 pharmacists in 1799 community pharmacies. Pharmacy-specific performance rates were calculated on four quality of care indicators: two safety indicators (dispensing of contra-indicated benzodiazepines to seniors and dispensing of nonselective beta-blockers to patients with respiratory disease) and two effectiveness indicators (dispensing asthma or hypertension medications to non-compliant patients). Descriptive statistics were used to summarize performance. RESULTS: Reliable estimates of performance could be obtained for more than 90% of pharmacies. The average rate of dispensing was 4.3% (range 0 - 42.5%) for contra-indicated benzodiazepines, 15.2% (range 0 - 100%) for nonselective beta-blockers to respiratory patients, 10.7% (range 0 - 70%) for hypertension medications to noncompliant patients, and 43.3% (0 - 91.6%) for short-acting beta-agonists in over-use situations. There were modest correlations in performance across the four indicators. Nine pharmacies (0.5%) performed in the lowest quartile in all four of the indicators, and 5.3% (n = 95) performed in the lowest quartile on three of four indicators. CONCLUSIONS: Routinely collected pharmacy claims data can be used to monitor indicators of the quality of care provided in community pharmacies, and may be useful in future to identify underperforming pharmacists, measure the impact of policy changes and determine predictors of best practices

Using ontologies to enhance human understandability of global post-hoc explanations of black-box models

The interest in explainable artificial intelligence has grown strongly in recent years because of the need to convey safety and trust in the ‘how’ and ‘why’ of automated decision-making to users. While a plethora of approaches has been developed, only a few focus on how to use domain knowledge and how this influences the understanding of explanations by users. In this paper, we show that by using ontologies we can improve the human understandability of global post-hoc explanations, presented in the form of decision trees. In particular, we introduce Trepan Reloaded, which builds on Trepan, an algorithm that extracts surrogate decision trees from black-box models. Trepan Reloaded includes ontologies, that model domain knowledge, in the process of extracting explanations to improve their understandability. We tested the understandability of the extracted explanations by humans in a user study with four different tasks. We evaluate the results in terms of response times and correctness, subjective ease of understanding and confidence, and similarity of free text responses. The results show that decision trees generated with Trepan Reloaded, taking into account domain knowledge, are significantly more understandable throughout than those generated by standard Trepan. The enhanced understandability of post-hoc explanations is achieved with little compromise on the accuracy with which the surrogate decision trees replicate the behaviour of the original neural network models