Data Analytics in Healthcare : A Tertiary Study
Taipalus, T., Isomöttönen, V., Erkkilä, H., & Äyrämö, S. (2023). Data Analytics in Healthcare : A Tertiary Study. SN Computer Science, 4(1), Article 87. https://doi.org/10.1007/s42979-022-01507-0
Julkaistu sarjassa
SN Computer SciencePäivämäärä
2023Oppiaine
TutkintokoulutusLaskennallinen tiedeComputing, Information Technology and MathematicsComputing Education ResearchHuman and Machine based Intelligence in LearningDegree EducationComputational ScienceComputing, Information Technology and MathematicsComputing Education ResearchHuman and Machine based Intelligence in LearningTekijänoikeudet
© The Author(s) 2022
The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer’s disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25–100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.
...
Julkaisija
Springer Science and Business Media LLCISSN Hae Julkaisufoorumista
2662-995XAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/164489945
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
Open Access funding provided by University of Jyväskylä (JYU). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Towards a Great Design of Conceptual Modelling
Kiyoki, Yasushi; Thalheim, Bernhard; Duží, Marie; Jaakkola, Hannu; Chawakitchareon, Petchporn; Heimbürger, Anneli (IOS Press, 2020)Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. This is a big challenge for global information systems development and for their ... -
Data-analytiikka terveydenhuollossa : systemaattinen kirjallisuuskartoitus
Erkkilä, Hanna (2021)Massadata ja sen hyödyntäminen analytiikan keinoin on noussut viimeisten vuosikymmenten aikana yhdeksi keskeisimmäksi mielenkiinnon kohteeksi sekä tiedemaailmassa että liiketoiminnan eri osa-alueilla. Massadataa kertyy yhä ... -
Strategic cyber threat intelligence : Building the situational picture with emerging technologies
Voutilainen, Janne; Kari, Martti (Academic Conferences International, 2020)In 2019, e-criminals adopted new tactics to demand enormous ransoms from large organizations by using ransomware, a phenomenon known as “big game hunting.” Big game hunting is an excellent example of a sophisticated and ... -
On Attacking Future 5G Networks with Adversarial Examples : Survey
Zolotukhin, Mikhail; Zhang, Di; Hämäläinen, Timo; Miraghaei, Parsa (MDPI AG, 2023)The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to ... -
Practices and Infrastructures for Machine Learning Systems : An Interview Study in Finnish Organizations
Muiruri, Dennis; Lwakatare, Lucy Ellen; Nurminen, Jukka K.; Mikkonen, Tommi (Institute of Electrical and Electronics Engineers (IEEE), 2022)Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.