Guest EditorialIntelligent healthcare informatics in big data era
Section snippets
Data analytics and predictive modeling
In “Removing confounding factors via constraint-based clustering: An application to finding homogeneous groups of multiple sclerosis patients,” Jingjing Liu, Carla Brodley, Brian Healy, and Tanuja Chitnis presented a constraint-based clustering method to remove the impact of confounding factors in identifying homogeneous groups of multiple sclerosis patients without assuming the form of influence of these confounding factors on the other features [1]. By using such method, the authors were able
Telemedicine and home monitoring systems
José M. Juarez, Jose M. Ochotorena, Manuel Campos, and Carlo Combi, in their paper “Spatiotemporal data visualization for homecare monitoring of elderly people,” proposed the multiple temporal axes model to visualize the behavior of elderly people who were living alone so that the health-risk scenarios and repetitive patterns can be identified to prevent home accidents [3]. The experiments confirmed that the proposed model were useful in representing fall and fatigue scenarios.
Epidemiology
In “Synthesis of a high resolution social contact network for Delhi with application to pandemic planning,” Huadong Xia, Kalyani Nagaraj, Jiangzhuo Chen, and Madhav V. Marathe presented a methodology to create an individual-based social contact network for Delhi using a variety of open source and commercial data [4]. Using such social contact network, the authors studied how an influenza-like illness would spread by a high performance agent-based modeling environment EPIFAST and then analyzed
Semantic and data integration
Ioannis Korkontzelos, Dimitrios Piliouras, Andrew W. Dowsey, and Sophia Ananiadou, in their paper “Boosting drug named entity recognition using an aggregate classifier,” utilized a voting system to combine a number of heterogeneous models to enhance the drug entity name recognition without relying exclusively on manual annotations of training data [6]. The authors also employed genetic programming to evolve regular-expressions that capture common drug suffixes as extra means of recognition. The
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Removing confounding factors via constraint-based clustering: an application to finding homogeneous groups of multiple sclerosis patients
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Predicting readmission risk with institution-specific prediction models
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Spatiotemporal data visualization for homecare monitoring of elderly people
Artif Intell Med
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Cited by (35)
Grouping attributes zero-shot learning for tongue constitution recognition
2020, Artificial Intelligence in MedicineCitation Excerpt :Traditional Chinese Medicine (TCM) plays an much important role in health-care for several thousand years due to its minor side effect as well as non-invasive diagnosis and treatment. It is widely used in China and some Asian countries [1–4]. This is why lots of research on TCM are investigated to perform auxiliary diagnosis and treatment using advanced techniques [5], such as image processing methods [6,7] and deep learning methods [8,9].
Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks
2019, Artificial Intelligence in MedicineCitation Excerpt :The rapid development of computing technologies already had a major impact on healthcare, especially in intensive care units (ICUs). This technological growth has been accompanied by an increase in the complexity of monitoring equipment, generating large amounts of data that need rapid and accurate interpretation by medical staff [1]. Databases have become an essential part of ICUs for storing, integrating and sharing patient data.
Semantic term weighting for clinical texts
2018, Expert Systems with ApplicationsCitation Excerpt :In recent years, the prevalence of clinical texts such as electronic medical records (EMRs) and electronic health records (EHRs) opens new chances for developing methods to solve many significant problems in medical research regarding semantic and data integration and phenotyping (Richesson, Sun, Pathak, Kho, & Denny, 2016; Yang & Veltri, 2015).
Diffusion Model in Semi-Supervised Scleral Segmentation
2023, 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023Storage Method for Medical and Health Big Data Based on Distributed Sensor Network
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