Demystifying Big Data and Machine Learning for Healthcare

Natarajan, Prashant; Frenzel, John C.; Smaltz, Detlev H.

doi:10.1201/9781315389325

Cited by 38 publications

(25 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

Section: Model Estimation and Evaluation Of The Overall Modelcontrasting

confidence: 99%

“…Our results do not confirm the assumptions of Natarajan et al (2017) who expects that variety is the most significant factor of big data analytics. Natarajan et al (2017) have made these assertions in the context of medical information quality. It is conceivable, that for medical decisions a variety of information, which points to the same diagnosis, is more important than volume or accuracy of individual data points due to the issue of differential diagnoses.…”

Section: Model Estimation and Evaluation Of The Overall Modelcontrasting

confidence: 99%

See 1 more Smart Citation

Big data analytics in sustainability reports: an analysis based on the perceived credibility of corporate published information

Wanner

Janiesch

2019

Bus Res

View full text Add to dashboard Cite

The credibility of sustainability reports has been the subject of scientific research for several years. The problem is often referred to as the so-called credibility gap, which is based on information asymmetries. The situation is further complicated by the limited rationality of human action as improvements to reports do not necessarily translate into credibility gains. Research has proposed and extracted several methods to overcome the issue. Hitherto, most approaches to solve the problem focused on marketing-oriented approaches. This work takes a new approach and explores the extent to which information technology can increase credibility using the potential of big data analytics. We base our research on the relationship of the quality of information and on the perception of objective truth as postulated in the Habermas Theory of Communicative Action. We use the forecastoriented Partial Least Squares Methodology for the review of hypotheses extracted from literature and expert surveys. The result confirms potential of the criteria of volume and veracity while velocity and variety do not yield comparable potential concerning sustainability reporting.

show abstract

Section: Model Estimation and Evaluation Of The Overall Modelcontrasting

confidence: 99%

Section: Model Estimation and Evaluation Of The Overall Modelcontrasting

confidence: 99%

Big data analytics in sustainability reports: an analysis based on the perceived credibility of corporate published information

Wanner

Janiesch

2019

Bus Res

View full text Add to dashboard Cite

show abstract

“…This disrupts the progress of comprehensive diagnostics. Many healthcare institutions implement centralized repositories by pooling data from multiple systems into data warehouses or data lakes [9,10]. Sharing these data out of the organizations' boundaries is not a viable solution since the anonymization of data may not be possible for certain data types, such as genomic data, and also since linking data sets increases the re-identification risk [11,12].…”

Section: Infrastructure and Interoperabilitymentioning

confidence: 99%

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?

et al. 2020

View full text Add to dashboard Cite

Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and clinical chemistry, and its comprehensive analysis by artificial intelligence (AI)–based tools is expected to improve diagnostic precision and the therapeutic conduct. However, the complex medical environment poses a major obstacle to the translation of integrated diagnostics into clinical research and routine. There is a high need to address aspects like data privacy, data integration, interoperability standards, appropriate IT infrastructure, and education of staff. Besides this, a plethora of technical, political, and ethical challenges exists. This is complicated by the high diversity of approaches across Europe. Thus, we here provide insights into current international activities on the way to digital comprehensive diagnostics. This includes a technical view on challenges and solutions for comprehensive diagnostics in terms of data integration and analysis. Current data communications standards and common IT solutions that are in place in hospitals are reported. Furthermore, the international hospital digitalization scoring and the European funding situation were analyzed. In addition, the regional activities in radiomics and the related publication trends are discussed. Our findings show that prerequisites for comprehensive diagnostics have not yet been sufficiently established throughout Europe. The manifold activities are characterized by a heterogeneous digitization progress and they are driven by national efforts. This emphasizes the importance of clear governance, concerted investments, and cooperation at various levels in the health systems. Key Points • Europe is characterized by heterogeneity in its digitization progress with predominantly national efforts. Infrastructural prerequisites for comprehensive diagnostics are not given and not sufficiently funded throughout Europe, which is particularly true for data integration. • The clinical establishment of comprehensive diagnostics demands for a clear governance, significant investments, and cooperation at various levels in the healthcare systems. • While comprehensive diagnostics is on its way, concerted efforts should be taken in Europe to get consensus concerning interoperability and standards, security, and privacy as well as ethical and legal concerns.

show abstract

“…Eine Minute eines hochauflösenden chirurgischen Videos enthält zwischen 200 und 400 MB an Information, also ungefähr die gleiche Datenmenge, die ein gesamtes CT-Scanbild des Abdomens mit höherer Auflösung bieten könnte. Es ist eine Goldgrube für Daten, die nicht ignoriert werden sollte [4][5][6]. Zusätzlich zu Videoaufnahmen können die Körperbewegungen der Chirurgen und des Teams, inklusive Handführung, Instrumentennutzung und Interaktion mittels Kameras und anderen Sensoren im Operationssaal registriert werden [7].…”

Section: Ki Und Chirurgische Performanceunclassified

Machine Learning and Learning Machines: KI in der Aus- und Weiterbildung

et al. 2020

View full text Add to dashboard Cite

ZUSAMMENFASSUNGDie Künstliche Intelligenz (KI) geht Hand in Hand mit der Digitalisierung der chirurgischen Aus- und Weiterbildung und wird in der Zukunft immer mehr an Bedeutung gewinnen. Die Anforderungen an KI in der chirurgischen Aus- und Weiterbildung sind vielfältig – genauso vielfältig sind ihre Einsatzmöglichkeiten. KI wird zunehmend im chirurgischen Curriculum berücksichtigt, auch wenn sie manchmal nicht auf den ersten Blick erkennbar ist und durch ihre Algorithmen eher im Hintergrund agiert. Die modernen digitalen Lehrmethoden und Sensortechnik eröffnen neue Wege für die Einbindung der KI in die chirurgische Aus- und Weiterbildung. Die ersten Schritte sind bereits implementiert – KI unterstützt die Erstellung von digitalem Bildmaterial zu Lehrzwecken oder erfasst mithilfe von Sensortechnik die chirurgische Leistung, um diese zu analysieren. Diverse Virtual- und Augmented-Reality-Simulatoren werden nicht nur als effektive Trainingswerkzeuge genutzt, sondern stellen vielmehr wertvolle Quellen für chirurgische Daten dar. All dies birgt enormes Potenzial für neue Erkenntnisse im Bereich der Lehrforschung, was zur Entwicklung von hocheffektiven und evidenzbasierten Lehrkonzepten beiträgt.

show abstract

Demystifying Big Data and Machine Learning for Healthcare

Cited by 38 publications

References 0 publications

Big data analytics in sustainability reports: an analysis based on the perceived credibility of corporate published information

Big data analytics in sustainability reports: an analysis based on the perceived credibility of corporate published information

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?

Machine Learning and Learning Machines: KI in der Aus- und Weiterbildung

Contact Info

Product

Resources

About