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Market Research Group

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Joseph Arnold
Joseph Arnold

Trends And Applications In Knowledge Discovery ...

Many healthcare leaders find themselves overwhelmed with data, but lack the information they need to make informed decisions. Knowledge discovery in databases (KDD) can help organizations turn their data into information. KDD is the process of finding complex patterns and relationships in data. The tools and techniques of KDD have achieved impressive results in other industries, and healthcare needs to take advantage of advances in this exciting field. Recent advances in the KDD field have brought it from the realm of research institutions and large corporations to many smaller companies. Software and hardware advances enable small organizations to tap the power of KDD using desktop PCs. KDD has been used extensively for fraud detection and focused marketing. There is a wealth of data available within the healthcare industry that would benefit from the application of KDD tools and techniques. Providers and payers have a vast quantity of data (such as, charges and claims), but not effective way to analyze the data to accurately determine relationships and trends. Organizations that take advantage of KDD techniques will find that they offer valuable assistance in the quest to lower healthcare costs while improving healthcare quality.

Trends and Applications in Knowledge Discovery ...

Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.

The scientific field of geospatial artificial intelligence (geoAI) was recently formed from combining innovations in spatial science with the rapid growth of methods in artificial intelligence (AI), particularly machine learning (e.g., deep learning), data mining, and high-performance computing to glean meaningful information from spatial big data. geoAI is highly interdisciplinary, bridging many scientific fields including computer science, engineering, statistics, and spatial science. The innovation of geoAI partly lies in its applications to address real-world problems. In particular, geoAI applications were showcased at the inaugural 2017 Association of Computing Machinery (ACM) Special Interest Group on Spatial Information (SIGSPATIAL) International Workshop on GeoAI: AI and Deep Learning for Geographic Knowledge Discovery (the steering committee was led by the U.S. Department of Energy Oak Ridge National Laboratory Urban Dynamics Institute), which included advances in remote sensing image classification and predictive modeling for traffic. Further, the application of AI technologies for knowledge discovery from spatial data reflects a recent trend as demonstrated in other scientific communities including the International Symposium on Spatial and Temporal Databases. These novel geoAI methods can be used to address human health-related problems, for example, in environmental epidemiology [3]. In particular, geoAI technologies are beginning to be used in the field of environmental exposure modeling, which is commonly used to conduct exposure assessment in these studies [4]. Ultimately, one of the overarching goals for integrating geoAI with environmental epidemiology is to conduct more accurate and highly resolved modeling of environmental exposures (compared to conventional approaches), which in turn would lead to more accurate assessment of the environmental factors to which we are exposed, and thus improved understanding of the potential associations between environmental exposures and disease in epidemiologic studies. Further, geoAI provides methods to measure new exposures that have been previously difficult to capture.

Geospatial artificial intelligence (geoAI) is an emerging science that utilizes advances in high-performance computing to apply technologies in AI, particularly machine learning (e.g., deep learning) and data mining to extract meaningful information from spatial big data. geoAI is both a specialized field within spatial science because particular spatial technologies, including GIS, must be used to process and analyze spatial data, and an applied type of spatial data science, as it is specifically focused on applying AI technologies to analyze spatial big data. The first-ever International Workshop on geoAI organized as part of the 2017 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems brought together scientists across diverse disciplines, including geoscientists, computer scientists, engineers, and entrepreneurs to discuss the latest trends in deep learning for geographical data mining and knowledge discovery. Featured geoAI applications included deep learning architectures and algorithms for feature recognition in historical maps [25]; multi-sensor remote sensing image resolution enhancement [26]; and identification of the semantic similarity in VGI attributes for OpenStreetMap [27]. The geoAI Workshop is one example of the recent trend in the application of AI to spatial data. For example, AI research has been presented at the International Symposium on Spatial and Temporal Databases, which features research in spatial, temporal, and spatiotemporal data management and related technologies.

geoAI is an emerging interdisciplinary scientific field that harnesses the innovations of spatial science, artificial intelligence (particularly machine learning and deep learning), data mining, and high-performance computing for knowledge discovery from spatial big data. geoAI traces part of its roots from spatial data science, which is an evolving field that aims to help organize how we think about and approach processing and analyzing spatial big data. Recent research demonstrates movement towards practical applications of geoAI to address real-world problems from feature recognition to image enhancement. geoAI offers several advantages for environmental epidemiology, particularly for exposure modeling as part of exposure assessment, including the capability to incorporate large amounts of spatial big data of high spatial and/or temporal resolution; computational efficiency regarding time and resources; flexibility in accommodating important features of spatial (environmental) processes such as spatial nonstationarity; and scalability to model different environmental exposures in different geographic areas. Potential future geoAI applications for environmental epidemiology should utilize cross-disciplinary approaches to developing and establishing rigorous and best practices for exposure modeling that includes careful consideration of data quality and domain-specific expertise.

IDC's Knowledge Discovery program analyzes the technological capabilities, market trends, and buyer needs surrounding the evolution of search into knowledge discovery. Knowledge discovery systems use a combination of AI/ML, NLP, ontologies/taxonomies, and techniques such as semantic knowledge graphs and vector search to analyze various structured and unstructured forms of data from different repositories and proactively surface contextualized insights, products, and other recommendations.

Knowledge discovery software (KDS) refers to software product/services that are used to develop solutions that find and provide answers, entities (people, places, things), and/or information and knowledge. This program will cover the following topics:

Knowledge discovery is defined as ``the non-trivial extraction of implicit, unknown, and potentially useful information from data'' [6]. In [5], a clear distinction between data mining and knowledge discovery is drawn. Under their conventions, the knowledge discovery process takes the raw results from data mining (the process of extracting trends or patterns from data) and carefully and accurately transforms them into useful and understandable information. This information is not typically retrievable by standard techniques but is uncovered through the use of AI techniques.

KDD is a growing field: There are many knowledge discovery methodologies in use and under development. Some of these techniques are generic, while others are domain-specific. The purpose of this paper is to present the results of a literature survey outlining the state-of-the-art in KDD techniques and tools. The paper is not intended to provide an in-depth introduction to each approach; rather, we intend it to acquaint the reader with some KDD approaches and potential uses.

Although there are many approaches to KDD, six common and essential elements qualify each as a knowledge discovery technique. The following are basic features that all KDD techniques share (adapted from [5] and [6]):

Large amounts of data are required to provide sufficient information to derive additional knowledge. Since large amounts of data are required, processing efficiency is essential. Accuracy is required to assure that discovered knowledge is valid. The results should be presented in a manner that is understandable by humans. One of the major premises of KDD is that the knowledge is discovered using intelligent learning techniques that sift through the data in an automated process. For this technique to be considered useful in terms of knowledge discovery, the discovered knowledge must be interesting; that is, it must have potential value to the user. 041b061a72


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