Miguel Ángel Sánchez Razo

Reading, England, United Kingdom Contact Info
378 followers 342 connections

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High-level IT professional and lecturer with a solid academic background in Data Science…

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Publications

  • Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease

    Frontiers in Neuroscience

    Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain…

    Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain age model as biomarker is the prediction of Alzheimer's disease (AD) from structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied to this specific predictive task, some of which have achieved high accuracy at the expense of the descriptiveness of the model. This work investigates an appropriate combination of data science techniques and linear models to provide, at the same time, high accuracy and good descriptiveness. The proposed method is based on a data workflow that include typical data science methods, such as outliers detection, feature selection, linear regression, and logistic regression. In particular, a novel inductive bias is introduced in the regression model, which is aimed at improving the accuracy and the specificity of the classification task. The method is compared to other machine learning approaches for AD classification based on morphological brain data with and without the use of the brain age, including Support Vector Machines and Deep Neural Networks. This study adopts brain MRI scans of 1, 901 subjects which have been acquired from three repositories (ADNI, AIBL, and IXI). A predictive model based only on the proposed apparent brain age and the chronological age has an accuracy of 88% and 92%, respectively, for male and female subjects, in a repeated cross-validation analysis, thus achieving a comparable or superior performance than state of the art machine learning methods.

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Projects

  • Eli Lilly - Mexico Sales Data Warehouse Development

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    Activities: Business Intelligence, Data Integration, Data Governance, DWH Design (Data Vault), Master Data Manage-ment and Data Profiling. Team members: 10

    Outcome: Implementation of the DWH which covers Institution’s information needs of reporting and data integration for Sales Productivity Process and Market Research areas; design and set up of a Data Governance Structure

    Tools: SQL Server 2012, Master Data Management, Integration Services

  • FOVISSSTE - Data Governance and Business Intelligence Platform Implementation

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    Activities: Business Intelligence, Data Governance, and Data Profiling, BI Competency Center. Team members: 22.

    Outcome: Implementation of the DWH and Dashboards that covers Institution’s information needs of reporting and data integration; design and set up of a structure of Data Governance and BI Competency Center.

    Tools: Oracle 11g, ODI and Artus (Reporting).

  • SAT - Information Lifecycle Management

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    Activities: Definition and implementation of Information Lifecycle Management Strategy. Team members: 10.

    Outcome: Improvement in the storage infrastructures administration by implementing Institution’s information life cycle defining storage, backup and recovery policies.

    Tools: Oracle 11g, SQL and Excel.

  • SAT - Information Lifecycle Management

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    Activities: Definition and implementation of Information Lifecycle Management Strategy. Team members: 10.

    Outcome: Improvement in the storage infrastructures administration by implementing Institution’s information life cycle defining storage, backup and recovery policies.

    Tools: Oracle 11g, SQL and Excel.

  • IMSS - Information Lifecycle Management

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    Activities: Information and Compliance analysis

    Outcomes: Improve information management by implementing an Information Lifecycle Management (ILM) Strategy for different organization’s data sets

    Tools: Microsoft Office

  • PEMEX - P3

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    Activities: Information and Compliance analysis

    Outcome: Creating a framework to standardize future projects of Enterprise Content Management

    Tools: Microsoft Office

  • IMSS - BDU

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    Activities: Data analysis and Database Design

    Outcome: Institute’s Master Data management focused on creation of the Governance Framework which will guide the unique database structure within this Institute

    Tools: Microsoft Office

Languages

  • Spanish

    Native or bilingual proficiency

  • English

    Professional working proficiency

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