Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.

The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.

Efficient processing of complex XSD using Hive and Spark.

The eXtensible Markup Language (XML) files are widely used by the industry due to their flexibility in representing numerous kinds of data. Multiple applications such as financial records, social networks, and mobile networks use complex XML schemas with nested types, contents, and/or extension bases on existing complex elements or large real-world files. A great number of these files are generated each day and this has influenced the development of Big Data tools for their parsing and reporting, such as Apache Hive and Apache Spark. For these reasons, multiple studies have proposed new techniques and evaluated the processing of XML files with Big Data systems. However, a more usual approach in such works involves the simplest XML schemas, even though, real data sets are composed of complex schemas. Therefore, to shed light on complex XML schema processing for real-life applications with Big Data tools, we present an approach that combines three techniques. This comprises three main methods for parsing XML files: cataloging, deserialization, and positional explode. For cataloging, the elements of the XML schema are mapped into root, arrays, structures, values, and attributes. Based on these elements, the deserialization and positional explode are straightforwardly implemented. To demonstrate the validity of our proposal, we develop a case study by implementing a test environment to illustrate the methods using real data sets provided from performance management of two mobile network vendors. Our main results state the validity of the proposed method for different versions of Apache Hive and Apache Spark, obtain the query execution times for Apache Hive internal and external tables and Apache Spark data frames, and compare the query performance in Apache Hive with that of Apache Spark. Another contribution made is a case study in which a novel solution is proposed for data analysis in the performance management systems of mobile networks.

XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study.

BACKGROUND: Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. OBJECTIVE: This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician's perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. METHODS: We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. RESULTS: In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. CONCLUSIONS: Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

Toma de decisiones inteligente a partir de registros médicos almacenados en CDA-HL7 / Intelligent decision-making from medical records stored on the CDA-HL7

Debido al incremento exponencial de la información almacenada en las organizaciones, la Sociedad de la Información está siendo superada por la necesidad de nuevos métodos capaces de procesar la información y asegurar su uso productivo. Esto se hace lógicamente extensible a los centros hospitalarios, a partir del uso extendido de las Historias Clínicas en formato electrónico. Disponer de información sistematizada, gestionarla de forma eficiente y segura es esencial para garantizar mejores prácticas en salud. A esto se le añade la necesidad de soportar estándares que permitan el intercambio entre las instituciones de salud; específicamente HL7 se ha convertido en uno de los más utilizados debido a que proporciona el intercambio a partir del metalenguaje XML. En este trabajo se propone una metodología para el descubrimiento de conocimiento implícito en Historias Clínicas en formato semi-estructurado utilizando el contenido y la estructura de los mismos. Los principales resultados son: (1) La metodología para el agrupamiento de Historias Clínicas; (2) La interpretación de los resultados del agrupamiento para asistir la toma de decisiones diagnósticas; (3) La implementación del estándar HL7, para la manipulación de documentos médicos a partir de CDA(AU)

Due to the exponential increase of stored information the organizations, the information society is being overtaken by the need for new methods capable of processing information and ensuring its productive use. This is logically extended to the hospitals, from the widespread use of clinical histories in electronic format. To have systematized information, manage it efficiently and securely is essential to ensure better health practices. In addition, there is the need for standards to support the exchange among health institutions; specifically hl7 has become one of the most widely used because it provides the exchange from xml. In this paper is presented a methodology for the discovery of implicit knowledge in medical records with semi-structured format, using their content and structure. The main results are: (1) the methodology for the clustering of medical records; (2) the interpretation of the results of the clustering to assist diagnostic decision-making; (3) the implementation of the hl7 standard for handling medical records from CDA(AU)