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  1. Article ; Online: Improving engineering students' understanding of classical physics through visuo-haptic simulations.

    González-Mena, Guillermo / Lozada-Flores, Octavio / Murrieta Caballero, Dione / Noguez, Julieta / Escobar-Castillejos, David

    Frontiers in robotics and AI

    2024  Volume 11, Page(s) 1305615

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2024-03-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2781824-X
    ISSN 2296-9144 ; 2296-9144
    ISSN (online) 2296-9144
    ISSN 2296-9144
    DOI 10.3389/frobt.2024.1305615
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.

    Fregoso-Aparicio, Luis / Noguez, Julieta / Montesinos, Luis / García-García, José A

    Diabetology & metabolic syndrome

    2021  Volume 13, Issue 1, Page(s) 148

    Abstract: Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and ... ...

    Abstract Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.
    Language English
    Publishing date 2021-12-20
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2518786-7
    ISSN 1758-5996
    ISSN 1758-5996
    DOI 10.1186/s13098-021-00767-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A feature selection strategy for gene expression time series experiments with hidden Markov models.

    Cárdenas-Ovando, Roberto A / Fernández-Figueroa, Edith A / Rueda-Zárate, Héctor A / Noguez, Julieta / Rangel-Escareño, Claudia

    PloS one

    2019  Volume 14, Issue 10, Page(s) e0223183

    Abstract: Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of ... ...

    Abstract Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.
    MeSH term(s) Algorithms ; Case-Control Studies ; Gene Expression/genetics ; Markov Chains ; Models, Statistical
    Language English
    Publishing date 2019-10-10
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0223183
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A Review of Simulators with Haptic Devices for Medical Training.

    Escobar-Castillejos, David / Noguez, Julieta / Neri, Luis / Magana, Alejandra / Benes, Bedrich

    Journal of medical systems

    2016  Volume 40, Issue 4, Page(s) 104

    Abstract: Medical procedures often involve the use of the tactile sense to manipulate organs or tissues by using special tools. Doctors require extensive preparation in order to perform them successfully; for example, research shows that a minimum of 750 ... ...

    Abstract Medical procedures often involve the use of the tactile sense to manipulate organs or tissues by using special tools. Doctors require extensive preparation in order to perform them successfully; for example, research shows that a minimum of 750 operations are needed to acquire sufficient experience to perform medical procedures correctly. Haptic devices have become an important training alternative and they have been considered to improve medical training because they let users interact with virtual environments by adding the sense of touch to the simulation. Previous articles in the field state that haptic devices enhance the learning of surgeons compared to current training environments used in medical schools (corpses, animals, or synthetic skin and organs). Consequently, virtual environments use haptic devices to improve realism. The goal of this paper is to provide a state of the art review of recent medical simulators that use haptic devices. In particular we focus on stitching, palpation, dental procedures, endoscopy, laparoscopy, and orthopaedics. These simulators are reviewed and compared from the viewpoint of used technology, the number of degrees of freedom, degrees of force feedback, perceived realism, immersion, and feedback provided to the user. In the conclusion, several observations per area and suggestions for future work are provided.
    MeSH term(s) Dentistry, Operative/education ; Endoscopy/education ; Formative Feedback ; Humans ; Orthopedic Procedures/education ; Palpation/methods ; Simulation Training/methods ; Suture Techniques/education ; User-Computer Interface
    Keywords covid19
    Language English
    Publishing date 2016-04
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-016-0459-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: The hackathon model to spur innovation around global mHealth.

    Angelidis, Pantelis / Berman, Leslie / Casas-Perez, Maria de la Luz / Celi, Leo Anthony / Dafoulas, George E / Dagan, Alon / Escobar, Braiam / Lopez, Diego M / Noguez, Julieta / Osorio-Valencia, Juan Sebastian / Otine, Charles / Paik, Kenneth / Rojas-Potosi, Luis / Symeonidis, Andreas L / Winkler, Eric

    Journal of medical engineering & technology

    2016  Volume 40, Issue 7-8, Page(s) 392–399

    Abstract: The challenge of providing quality healthcare to underserved populations in low- and middle-income countries (LMICs) has attracted increasing attention from information and communication technology (ICT) professionals interested in providing societal ... ...

    Abstract The challenge of providing quality healthcare to underserved populations in low- and middle-income countries (LMICs) has attracted increasing attention from information and communication technology (ICT) professionals interested in providing societal impact through their work. Sana is an organisation hosted at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology that was established out of this interest. Over the past several years, Sana has developed a model of organising mobile health bootcamp and hackathon events in LMICs with the goal of encouraging increased collaboration between ICT and medical professionals and leveraging the growing prevalence of cellphones to provide health solutions in resource limited settings. Most recently, these events have been based in Colombia, Uganda, Greece and Mexico. The lessons learned from these events can provide a framework for others working to create sustainable health solutions in the developing world.
    Language English
    Publishing date 2016-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 243092-7
    ISSN 1464-522X ; 0309-1902
    ISSN (online) 1464-522X
    ISSN 0309-1902
    DOI 10.1080/03091902.2016.1213903
    Database MEDical Literature Analysis and Retrieval System OnLINE

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