Cognitive Services for Collaborative mHealth: The OnParkinson Case Study

Macedo, Patrícia; Madeira, Rui Neves; Camarinha-Matos, Luís M.

doi:10.1007/978-3-319-99127-6_38

Cited by 4 publications

(2 citation statements)

References 14 publications

(17 reference statements)

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“…These findings contribute to discussions around the ways in which data are often used across collaborative networks. Healthcare sector is one of industries that are adopting data management and machine learning within collaborative networks [28]. Healthcare and social welfare data, for example, can be shared and used in these networks, but it must be anonymised.…”

Section: Discussionmentioning

confidence: 99%

Interactive Machine Learning: Managing Information Richness in Highly Anonymized Conversation Data

Alamäki

Aunimo

Ketamo

et al. 2019

Collaborative Networks and Digital Transformation

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This case study focuses on an experiment analysing textual conversation data using machine learning algorithms and shows that sharing data across organisational boundaries requires anonymisation that decreases that data's information richness. Additionally, sharing data between organisations, conducting data analytics and collaborating to create new business insight requires inter-organisational collaboration. This study shows that analysing highly anonymised and professional conversation data challenges the capabilities of artificial intelligence. Machine learning algorithms alone cannot learn the internal connections and meanings of information cues. This experiment is therefore in line with prior research in interactive machine learning where data scientists, specialists and computational agents interact. This study reveals that, alongside humans, computational agents will be important actors in collaborative networks. Thus, humans are needed in several phases of the machine learning process for facilitating and training. This calls for collaborative working in multidisciplinary teams of data scientists and substance experts interacting with computational agents.

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Section: Discussionmentioning

confidence: 99%

Interactive Machine Learning: Managing Information Richness in Highly Anonymized Conversation Data

Alamäki

Aunimo

Ketamo

et al. 2019

Collaborative Networks and Digital Transformation

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“…An additional level of integration of AI and machine learning elements in CN infrastructures and tools is emerging by the first attempts to embed cognitive engineering tools in such systems. One example is [71], which envisages cognitivebased collaborative networks to support mobile health services. In [72] the Cognitive Model of Creativity is explored to bring computational creativity to manufacturing networks.…”

Section: G) Towards Cognitive Networkmentioning

confidence: 99%

The Evolution Path to Collaborative Networks 4.0

Camarinha-Matos

2021

IFIP Advances in Information and Communication Technology

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The last two decades have witnessed considerable boost in emergence of a networked society, reflecting the increasing growth in hyperconnectivity among the organizations, people, smart machines, and intelligent systems. This trend was enabled by advances in ICT and more specifically in computer networking. In this context, new forms of coworking and collaboration in networks, composed of distributed, autonomous, and heterogeneous entities have emerged, which first led to the formation of Collaborative Networks (CN) as a new discipline, and then followed by series of milestones leading to its gradual evolution.Nowadays CNs play a key role in the ongoing process of digital transformation in industry and services. Although it is relatively young, a number of "generations" can be identified through the last decades for the CN discipline.We are now at the beginning of what can be identified as the Collaborative Networks 4.0, characterized by features such as: hybridization in CNs, collaboration between humans and intelligent autonomous systems, collaborative distributed cognitive systems, reflecting on collaborative accountability, handling ethics and coping with risks and disruptions faced in CNs, managing large amounts of collaborative data, monetization of collaboration, creating a collaboration culture, supporting collaboration creativity, handling mass collaboration, and supporting collaborative value creation through new business models, among others.The IFIP WG 5.5, through its annual conference the PRO-VE, which is now in its 21st edition, has played a determinant role, along these two decades. It has contributed to shaping, promoting, and extending the CN research and development community and its practices; thus, consolidating this area, and identifying and introducing new directions and preserving it as an active research agenda.

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