Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients
| Autor: | Spruit, M., Lytras, M., Sub Organization and Information, Organization and Information |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Applied data science
Computer Networks and Communications Process (engineering) Computer science Big data Knowledge discovery process 02 engineering and technology Big data analytics Field (computer science) Domain (software engineering) Personalization Knowledge extraction 0502 economics and business 0202 electrical engineering electronic engineering information engineering Patient-centric healthcare Meta-algorithmic modelling Electrical and Electronic Engineering business.industry Natural language processing 05 social sciences Adaptive analytic system Data science 050211 marketing 020201 artificial intelligence & image processing Data pre-processing Design science research business |
| Zdroj: | Telematics and Informatics, 35(4). Elsevier |
| ISSN: | 0736-5853 |
| DOI: | 10.1016/j.tele.2018.04.002 |
| Popis: | We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency. |
| Databáze: | OpenAIRE |
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