Background Quantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models. Methods This paper uses a data mining methodology for classifying EEGs of 53 MDD patients and 43 HVs. This included: (a) pre-processing the data, including cleaning and normalization, applying Linear Discriminant Analysis (LDA) to map the features into a new feature space; and applying Genetic Algorithm (GA) to identify the most significant features; (b) building predictive models using the Decision Tree (DT) algorithm to discover rules and hidden patterns based on the reduced and mapped features; and (c) evaluating the models based on the accuracy and false positive values on the EEG data of MDD and HV participants. Two categories of experiments were performed. The first experiment analyzed each frequency band individually, while the second experiment analyzed the bands together. Results Application of LDA and GA markedly reduced the total number of utilized features by ≥ 50 % and, with all frequency bands analyzed together, the model showed average classification accuracy (MDD vs. HV) of 80 %. The best results from model testing with additional test EEG recordings from 9 MDD patients and 35 HV individuals demonstrated an accuracy of 80 % and showed an average sensitivity of 70 %, a specificity of 76 %, and a positive (PPV) and negative predictive value (NPV) of 74 and 75 %, respectively. Conclusions These initial findings suggest that the proposed automated EEG analytical approach could be a useful adjunctive diagnostic approach in clinical practice.

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

Given that the performance of public-sector organizations is tied to the efficient use of knowledge, this exploratory study examined factors that influence knowledge acquisition by work groups in knowledge-intensive public-sector organizations. Based upon a review of the absorptive capacity and knowledge management literature, we defined knowledge acquisition as the change in the collective knowledge of groups over time. The amount of individual prior knowledge, and common knowledge among group members, managerial practices, and perceptions of knowledge applicability were identified as independent variables. Data were collected from 179 individuals representing 28 work groups in 7 public-sector organizations. Using multi-level regression, we found that homogeneity of knowledge at the group level and perceptions of knowledge applicability influenced acquisition, but that, contrary to much of the literature in this domain, prior-related knowledge did not have such an influence. Middle-management practices moderated the impact of knowledge applicability, suggesting that middle managers provide contextual information that permits group members to better understand the relevance of external knowledge. Implications for practice include the importance of training staff in teams to build homogeneity of knowledge, and ensuring that middle managers understand the organization’s strategy and their roles in the knowledge-utilization process. Implications for theory include the notion that prior knowledge can either encourage or obstruct knowledge acquisition. Dado que o desempenho de organizações do setor público é relacionado ao uso eficiente do conhecimento, este estudo exploratório examina fatores que influenciam a aquisição de conhecimento por grupos de trabalho em organizações do setor público de conhecimento intensivo. Com base em uma revisão da literatura sobre capacidade de absorção e conhecimento gerencial, nós definimos a aquisição de conhecimento como a mudança no conhecimento coletivo de grupos no decorrer do tempo. A quantidade de conhecimento individual prévio e o conhecimento comum entre os membros do grupo, das práticas gerenciais e percepções da aplicabilidade de conhecimento são identificadas como variáveis independentes. Os dados foram coletados de 179 indivíduos representando 28 grupos de trabalho em sete organizações do setor público. Utilizando regressão multinível, nós descobrimos que, no nível de grupo, a homogeneidade do conhecimento e as percepções da aplicabilidade deste conhecimento influenciam a aquisição, mas que, ao contrario de vasta literatura neste campo, prévio conhecimento não tem tanta influência. Práticas gerenciais de nível tático moderaram o impacto da aplicabilidade do conhecimento, sugerindo que gerentes médios fornecem informações contextuais que permitem aos membros do grupo um melhor entendimento da relevância de conhecimento externo. Implicações práticas incluem a importância do treinamento dos servidores em times para construir a homogeneidade do conhecimento e assegurar que os gerentes médios compreendam a estratégia organizacional e seus papéis no uso do conhecimento. Implicações teóricas incluem a noção de que conhecimento prévio pode tanto encorajar quanto obstruir a aquisição de conhecimento. Translated by Ricardo Gomes, University of Brasília