https://reunir.unir.net/handle/123456789/11902 Tue, 05 Nov 2024 17:33:22 GMT 2024-11-05T17:33:22Z https://reunir.unir.net/handle/123456789/11911 IJIMAI Editor's Note - Vol. 4 Issue 7 Mochón, Francisco; Elvira, Carlos The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on AI tools or tools that use AI with interactive multimedia techniques. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T12:11:58Z No. of bitstreams: 1 ijimai_4_7_0_pdf_71516.pdf: 142423 bytes, checksum: 1dec9e26c34da0454ce5922653438c52 (MD5); Made available in DSpace on 2021-09-27T12:11:58Z (GMT). No. of bitstreams: 1 ijimai_4_7_0_pdf_71516.pdf: 142423 bytes, checksum: 1dec9e26c34da0454ce5922653438c52 (MD5) https://reunir.unir.net/handle/123456789/11911 https://reunir.unir.net/handle/123456789/11910 Big Data and Public Health Systems: Issues and Opportunities Rojas, David; Carnicero, Javier Over the last years, the need for changing the current model of European public health systems has been repeatedly addressed, in order to ensure their sustainability. Following this line, IT has always been referred to as one of the key instruments for enhancing the information management processes of healthcare organizations, thus contributing to the improvement and evolution of health systems. On the IT field, Big Data solutions are expected to play a main role, since they are designed for handling huge amounts of information in a fast and efficient way, allowing users to make important decisions quickly. This article reviews the main features of the European public health system model and the corresponding healthcare and management-related information systems, the challenges that these health systems are currently facing, and the possible contributions of Big Data solutions to this field. To that end, the authors share their professional experience on the Spanish public health system, and review the existing literature related to this topic. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T11:56:12Z No. of bitstreams: 1 ijimai_4_7_8_pdf_16011.pdf: 354882 bytes, checksum: c8b0581f35bddbebaf3c8623c0061f25 (MD5); Made available in DSpace on 2021-09-27T11:56:12Z (GMT). No. of bitstreams: 1 ijimai_4_7_8_pdf_16011.pdf: 354882 bytes, checksum: c8b0581f35bddbebaf3c8623c0061f25 (MD5) https://reunir.unir.net/handle/123456789/11910 https://reunir.unir.net/handle/123456789/11909 Big Data and Health Economics: Opportunities, Challenges and Risks Bodas-Sagi, Diego; Labeaga, José Big Data offers opportunities in many fields. Healthcare is not an exception. In this paper we summarize the possibilities of Big Data and Big Data technologies to offer useful information to policy makers. In a world with tight public budgets and ageing populations we feel necessary to save costs in any production process. The use of outcomes from Big Data could be in the future a way to improve decisions at a lower cost than today. In addition to list the advantages of properly using data and technologies from Big Data, we also show some challenges and risks that analysts could face. We also present an hypothetical example of the use of administrative records with health information both for diagnoses and patients. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T11:43:48Z No. of bitstreams: 1 ijimai_4_7_7_pdf_86496.pdf: 519132 bytes, checksum: 61dfe095796c20f114246608ebd6ba8d (MD5); Made available in DSpace on 2021-09-27T11:43:48Z (GMT). No. of bitstreams: 1 ijimai_4_7_7_pdf_86496.pdf: 519132 bytes, checksum: 61dfe095796c20f114246608ebd6ba8d (MD5) https://reunir.unir.net/handle/123456789/11909 https://reunir.unir.net/handle/123456789/11908 Generating Big Data Sets from Knowledge-based Decision Support Systems to Pursue Value-based Healthcare González-Ferrer, Arturo; Seara, Germán; Cháfer, Joan; Mayol, Julio Talking about Big Data in healthcare we usually refer to how to use data collected from current electronic medical records, either structured or unstructured, to answer clinically relevant questions. This operation is typically carried out by means of analytics tools (e.g. machine learning) or by extracting relevant data from patient summaries through natural language processing techniques. From other perspective of research in medical informatics, powerful initiatives have emerged to help physicians taking decisions, in both diagnostics and therapeutics, built from the existing medical evidence (i.e. knowledge-based decision support systems). Much of the problems these tools have shown, when used in real clinical settings, are related to their implementation and deployment, more than failing in its support, but, technology is slowly overcoming interoperability and integration issues. Beyond the point-of-care decision support these tools can provide, the data generated when using them, even in controlled trials, could be used to further analyze facts that are traditionally ignored in the current clinical practice. In this paper, we reflect on the technologies available to make the leap and how they could help driving healthcare organizations shifting to a value-based healthcare philosophy. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T11:21:49Z No. of bitstreams: 1 ijimai_4_7_6_pdf_12585.pdf: 652666 bytes, checksum: 59b3a20d850628e9bffcea67d4417319 (MD5); Made available in DSpace on 2021-09-27T11:21:49Z (GMT). No. of bitstreams: 1 ijimai_4_7_6_pdf_12585.pdf: 652666 bytes, checksum: 59b3a20d850628e9bffcea67d4417319 (MD5) https://reunir.unir.net/handle/123456789/11908 https://reunir.unir.net/handle/123456789/11907 Development of Injuries Prevention Policies in Mexico: A Big Data Approach Cantón Croda, Rosa María; Gibaja Romero, Damián Emilio Considering that Mexican injuries prevention strategies have been focused on injuries caused by car accidents and gender violence, a whole analysis of the injuries registered are performed in this paper to have a wider overview of those agents that can cause injuries around the country. Taking into account the amount of information from both public and private sources, obtained from dynamic cubes reported by the Minister of Health, Big Data strategies are used with the objective of finding an appropriate extraction such as to identify the real correlations between the different variables registered by the Health Sector. The results of the analysis show areas of opportunity to improve the public policies on the subject, particularly in diminishing wounds at living place, public road (pedestrians) and work. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T10:59:26Z No. of bitstreams: 1 ijimai_4_7_5_pdf_14121.pdf: 509507 bytes, checksum: 776e0ad36e2a0376639079d8b58e9bba (MD5); Made available in DSpace on 2021-09-27T10:59:26Z (GMT). No. of bitstreams: 1 ijimai_4_7_5_pdf_14121.pdf: 509507 bytes, checksum: 776e0ad36e2a0376639079d8b58e9bba (MD5) https://reunir.unir.net/handle/123456789/11907 https://reunir.unir.net/handle/123456789/11906 Machine-Learning-Based No Show Prediction in Outpatient Visits Mochón, Francisco; Elvira, Carlos; Ochoa, Alberto; Gonzalvez, Juan Carlos A recurring problem in healthcare is the high percentage of patients who miss their appointment, be it a consultation or a hospital test. The present study seeks patient’s behavioural patterns that allow predicting the probability of no- shows. We explore the convenience of using Big Data Machine Learning models to accomplish this task. To begin with, a predictive model based only on variables associated with the target appointment is built. Then the model is improved by considering the patient’s history of appointments. In both cases, the Gradient Boosting algorithm was the predictor of choice. Our numerical results are considered promising given the small amount of information available. However, there seems to be plenty of room to improve the model if we manage to collect additional data for both patients and appointments. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T10:46:55Z No. of bitstreams: 1 ijimai_4_7_4_pdf_11885.pdf: 326853 bytes, checksum: 7660a249f6c0e17f88867c5837d3b718 (MD5); Made available in DSpace on 2021-09-27T10:46:55Z (GMT). No. of bitstreams: 1 ijimai_4_7_4_pdf_11885.pdf: 326853 bytes, checksum: 7660a249f6c0e17f88867c5837d3b718 (MD5) https://reunir.unir.net/handle/123456789/11906 https://reunir.unir.net/handle/123456789/11905 Development of a Predictive Model for Induction Success of Labour Pruenza, Cristina; Teurón, María; Lechuga, Luis; Díaz, Julia; González, Ana Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements) or less frequently, social (elective inductions for convenience). The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn), costs (medication, hospitalization, qualified staff) and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the information recovery system and the predictions and explanations of the classification show the effectiveness and strength of this tool. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T10:20:59Z No. of bitstreams: 1 ijimai_4_7_3_pdf_17377.pdf: 692348 bytes, checksum: 1994c566195b8d3e37ef9673c6a1852a (MD5); Made available in DSpace on 2021-09-27T10:20:59Z (GMT). No. of bitstreams: 1 ijimai_4_7_3_pdf_17377.pdf: 692348 bytes, checksum: 1994c566195b8d3e37ef9673c6a1852a (MD5) https://reunir.unir.net/handle/123456789/11905 https://reunir.unir.net/handle/123456789/11904 DataCare: Big Data Analytics Solution for Intelligent Healthcare Management Saez, Yago; Baldominos Gómez, Alejandro; Rada, Fernando This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster-computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at fast speed from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T09:37:40Z No. of bitstreams: 1 ijimai_4_7_2_pdf_16566.pdf: 784802 bytes, checksum: 14d2bb079d5b214a21c2acf687d97687 (MD5); Made available in DSpace on 2021-09-27T09:37:40Z (GMT). No. of bitstreams: 1 ijimai_4_7_2_pdf_16566.pdf: 784802 bytes, checksum: 14d2bb079d5b214a21c2acf687d97687 (MD5) https://reunir.unir.net/handle/123456789/11904 https://reunir.unir.net/handle/123456789/11903 Savana: Re-using Electronic Health Records with Artificial Intelligence Hernández Medrano, Ignacio; Tello Guijarro, Jorge; Belda, Cristóbal; Ureña, Alberto; Salcedo, Ignacio; Espinosa-Anke, Luis; Saggion, Horacio Health information grows exponentially (doubling every 5 years), thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing “mindlines” against “guidelines”, these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP) and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2021-09-27T09:09:25Z No. of bitstreams: 1 ijimai_4_7_1_pdf_22755.pdf: 935126 bytes, checksum: 8b6c3a77baefcab321a6826593d81f2e (MD5); Made available in DSpace on 2021-09-27T09:09:25Z (GMT). No. of bitstreams: 1 ijimai_4_7_1_pdf_22755.pdf: 935126 bytes, checksum: 8b6c3a77baefcab321a6826593d81f2e (MD5) https://reunir.unir.net/handle/123456789/11903