Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer's Disease. A Bayesian Network Model based on the Analysis of Oral Definitions of Semantic Categories.

BACKGROUND: Early detection of Alzheimer's disease (AD) has become one of the principal focuses of research in medicine, particularly when the disease is incipient or even prodromic, because treatments are more effective in these stages. Lexical-semantic-conceptual deficit (LSCD) in the oral definitions of semantic categories for basic objects is an important early indicator in the evaluation of the cognitive state of patients. OBJECTIVES: The objective of this research is to define an economic procedure for cognitive impairment (CI) diagnosis, which may be associated with early stages of AD, by analysing cognitive alterations affecting declarative semantic memory. Because of its low cost, it could be used for routine clinical evaluations or screenings, leading to more expensive and selective tests that confirm or rule out the disease accurately. It should necessarily be an explanatory procedure, which would allow us to study the evolution of the disease in relation to CI, the irregularities in different semantic categories, and other neurodegenerative diseases. On the basis of these requirements, we hypothesise that Bayesian networks (BNs) are the most appropriate tool for this purpose. METHODS: We have developed a BN for CI diagnosis in mild and moderate AD patients by analysing the oral production of semantic features. The BN causal model represents LSCD in certain semantic categories, both of living things (dog, pine, and apple) and non-living things (chair, car, and trousers), as symptoms of CI. The model structure, the qualitative part of the model, uses domain knowledge obtained from psychology experts and epidemiological studies. Further, the model parameters, the quantitative part of the model, are learnt automatically from epidemiological studies and Peraita and Grasso's linguistic corpus of oral definitions. This corpus was prepared with an incidental sampling and included the analysis of the oral linguistic production of 81 participants (42 cognitively healthy elderly people and 39 mild and moderate AD patients) from Madrid region's hospitals. Experienced neurologists diagnosed these cases following the National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA)'s Alzheimer's criteria, performing, among other explorations and tests, a minimum neuropsychological exploration that included the Mini-Mental State Examination test. RESULTS: BN's classification performance is remarkable compared with other machine learning methods, achieving 91% accuracy and 94% precision in mild and moderate AD patients. Apart from this, the BN model facilitates the explanation of the reasoning process and the validation of the conclusions and allows the study of uncommon declarative semantic memory impairments. CONCLUSIONS: Our method is able to analyse LSCD in a wide set of semantic categories throughout the progression of CI, being a valuable first screening method in AD diagnosis in its early stages. Because of its low cost, it can be used for routine clinical evaluations or screenings to detect AD in its early stages. Besides, due to its knowledge-based structure, it can be easily extended to provide an explanation of the diagnosis and to the study of other neurodegenerative diseases. Further, this is a key advantage of BNs over other machine learning methods with similar performance: it is a recognisable and explanatory model that allows one to study irregularities in different semantic categories.

Empiric treatment in hospitalized community-acquired pneumonia. Impact on mortality, length of stay and re-admission.

OBJECTIVE: To evaluate adherence to guidelines when choosing an empirical treatment and its impact upon the prognosis of community-acquired pneumonia (CAP). METHODS: A prospective multicentre study was conducted in 425 CAP patients hospitalized on ward. Initial empirical treatment was classified as adhering or not to Spanish guidelines. Adherent treatment was defined as an initial antimicrobial regimen consisting of beta-lactams plus macrolides, beta-lactam monotherapy and quinolones. Non-adherent treatments included macrolide monotherapy and other regimens. Initial severity was graded according to pneumonia severity index (PSI). The end point variables were mortality, length of stay (LOS) and re-admission at 30 days. RESULTS: Overall 30-day mortality was 8.2%, the mean LOS was 8+/-5 days, and the global re-admission rate was 7.6%. Adherence to guidelines was 76.5%, and in most cases the empirical treatment consisted of beta-lactam and macrolide in combination (57.4%). Logistic regression analysis showed that other regimens were associated with higher mortality OR=3 (1.2-7.3), after adjusting for PSI and admitting hospital. Beta-lactam monotherapy was an independent risk factor for re-admission. LOS was independently associated with admitting hospital and not with antibiotics. CONCLUSIONS: A high adherence to CAP treatment guidelines was found, though with considerable variability in the empirical antibiotic treatment among hospitals. Non-adherent other regimens were associated with greater mortality. Beta-lactam monotherapy was associated with an increased re-admission rate.

A customisable framework for the assessment of therapies in the solution of therapy decision tasks.

In current medical research, a growing interest can be observed in the definition of a global therapy-evaluation framework which integrates considerations such as patients preferences and quality-of-life results. In this article, we propose the use of the research results in this domain as a source of knowledge in the design of support systems for therapy decision analysis, in particular with a view to application in oncology. We discuss the incorporation of these considerations in the definition of the therapy-assessment methods involved in the solution of a generic therapy decision task, described in the context of AI software development methodologies such as CommonKADS. The goal of the therapy decision task is to identify the ideal therapy, for a given patient, in accordance with a set of objectives of a diverse nature. The assessment methods applied are based either on data obtained from statistics or on the specific idiosyncrasies of each patient, as identified from their responses to a suite of psychological tests. In the analysis of the therapy decision task we emphasise the importance, from a methodological perspective, of using a rigorous approach to the modelling of domain ontologies and domain-specific data. To this aim we make extensive use of the semi-formal object oriented analysis notation UML to describe the domain level.