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PURPOSE: Our primary goal was to study the use of outpatient attendances by lung cancer patients in Hospital Universitario Puerta de Hierro Majadahonda (HUPHM), Spain, by leveraging our Electronic Patient Record (EPR) and structured clinical registry of lung cancer cases as well as assessing current Data Science methods and tools. METHODS/PATIENTS: We applied the Cross-Industry Standard Process for Data Mining (CRISP-DM) to integrate and analyze activity data extracted from the EPR (9.3 million records) and clinical data of lung cancer patients from a previous registry that was curated into a new, structured database based on REDCap. We have described and quantified factors with an influence in outpatient care use from univariate and multivariate points of view (through Poisson and negative binomial regression). RESULTS: Three cycles of CRISP-DM were performed resulting in a curated database of 522 lung cancer patients with 133 variables which generated 43,197 outpatient visits and tests, 1538 ER visits and 753 inpatient admissions. Stage and ECOG-PS at diagnosis and Charlson Comorbidity Index were major contributors to healthcare use. We also found that the patients' pattern of healthcare use (even before diagnosis), the existence of a history of cancer in first-grade relatives, smoking habits, or even age at diagnosis, could play a relevant role. CONCLUSIONS: Integrating activity data from EPR and clinical structured data from lung cancer patients and applying CRISP-DM has allowed us to describe healthcare use in connection with clinical variables that could be used to plan resources and improve quality of care.

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The proper functioning of a hospital computer system is an arduous work for managers and staff. However, inconsistent policies are frequent and can produce enormous problems, such as stolen information, frequent failures, and loss of the entire or part of the hospital data. This paper presents a new method named EMRlog for computer security systems in hospitals. EMRlog is focused on two kinds of security policies: directive and implemented policies. Security policies are applied to computer systems that handle huge amounts of information such as databases, applications, and medical records. Firstly, a syntactic verification step is applied by using predicate logic. Then data mining techniques are used to detect which security policies have really been implemented by the computer systems staff. Subsequently, consistency is verified in both kinds of policies; in addition these subsets are contrasted and validated. This is performed by an automatic theorem prover. Thus, many kinds of vulnerabilities can be removed for achieving a safer computer system.

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This study aims to identify patterns in maternal and fetal characteristics in the prediction of infant mortality by incorporating innovative techniques like data mining, with proven relevance for public health. A database was developed with infant deaths from 2000 to 2004 analyzed by the Committees for the Prevention of Infant Mortality, based on integration of the Information System on Live Births (SINASC), Mortality Information System, and Investigation of Infant Mortality in the State of Paraná. The data mining software was WEKA (open source). The data mining conducts a database search and provides rules to be analyzed to transform the data into useful information. After mining, 4,230 rules were selected: teenage pregnancy plus birth weight < 2,500 g, or post-term birth plus teenage mother with a previous child or intercurrent conditions increase the risk of neonatal death. The results highlight the need for greater attention to teenage mothers, newborns with birth weight < 2,500 g, post-term neonates, and infants of mothers with intercurrent conditions, thus corroborating other studies.

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OBJECTIVE: To identify, with the assistance of computational techniques, rules concerning the conditions of the physical environment for the classification of risk micro-areas. METHODS: Exploratory research carried out in Curitiba, Southern Brazil, in 2007. It was divided into three phases: the identification of attributes to classify a micro-area; the construction of a database; and the process of discovering knowledge in a database through the use of data mining. The set of attributes included the conditions of infrastructure; hydrography; soil; recreation area; community characteristics; and existence of vectors. The database was constructed with data obtained in interviews by community health workers using questionnaires with closed-ended questions, developed with the essential attributes selected by specialists. RESULTS: There were 49 attributes identified, 41 of which were essential and eight irrelevant. There were 68 rules obtained in the data mining, which were analyzed through the perspectives of performance and quality and divided into two sets: the inconsistent rules and the rules that confirm the knowledge of experts. The comparison between the groups showed that the rules that confirm the knowledge, despite having lower computational performance, were considered more interesting. CONCLUSIONS: The data mining provided a set of useful and understandable rules capable of characterizing risk areas based on the characteristics of the physical environment. The use of the proposed rules allows a faster and less subjective area classification, maintaining a standard between the health teams and overcoming the influence of individual perception by each team member.

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O estudo busca identificar padrões de características materno-fetais na predição da mortalidade infantil, por meio da incorporação de técnicas inovadoras, como a Mineração de Dados, que se mostram relevantes em Saúde Pública. Foi elaborada uma base de dados, com óbitos infantis analisados pelos Comitês de Prevenção da Mortalidade Infantil de 2000 a 2004, a partir da integração dos Sistemas de Informações de Nascidos Vivos, da Mortalidade e da Investigação da Mortalidade Infantil no Estado do Paraná. O programa da mineração foi o WEKA, de uso livre. A mineração faz busca em banco de dados e fornece regras que devem ser analisadas para transformação em informação útil. Após a mineração, selecionaram-se 4.230 regras, por exemplo: mãe adolescente e peso ao nascer < 2.500g, ou parto pós-termo e mãe adolescente com outro filho, ou com afecções maternas, aumentam o risco para óbito neonatal. Vê-se a necessidade de estabelecer maior atenção às adolescentes, às crianças com peso ao nascer < 2.500g, pós-termo e filhas de mães com afecções maternas, confirmando resultados de outros estudos.

This study aims to identify patterns in maternal and fetal characteristics in the prediction of infant mortality by incorporating innovative techniques like data mining, with proven relevance for public health. A database was developed with infant deaths from 2000 to 2004 analyzed by the Committees for the Prevention of Infant Mortality, based on integration of the Information System on Live Births (SINASC), Mortality Information System, and Investigation of Infant Mortality in the State of Paraná. The data mining software was WEKA (open source). The data mining conducts a database search and provides rules to be analyzed to transform the data into useful information. After mining, 4,230 rules were selected: teenage pregnancy plus birth weight < 2,500g, or post-term birth plus teenage mother with a previous child or intercurrent conditions increase the risk of neonatal death. The results highlight the need for greater attention to teenage mothers, newborns with birth weight < 2,500g, post-term neonates, and infants of mothers with intercurrent conditions, thus corroborating other studies.

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