The traditional approach to health care is being revolutionized by the rapid adoption of patient‐centered healthcare models. The successful transformation of patients from passive recipients to ...active participants is largely attributed to increased access to healthcare information. Online support groups present a platform to seek and exchange information in an inclusive environment. As the volume of text on online support groups continues to grow exponentially, it is imperative to improve the quality of retrieved information in terms of relevance, reliability, and usefulness. We present a text‐mining approach that generates a knowledge extraction layer to address this void in personalized information retrieval from online support groups. The knowledge extraction layer encapsulates an ensemble of text‐mining techniques with a domain ontology to interpose an investigable and extensible structure on hitherto unstructured text. This structure is not limited to personalized information retrieval for patients, as it also imparts aggregates for crowdsourcing analytics by healthcare researchers. The proposed approach was successfully trialed on an active online support group consisting of 800,000 posts by 72,066 participants. Demonstrations for both patient and researcher use cases accentuate the value of the proposed approach to unlock a broad spectrum of personalized and aggregate knowledge concealed within crowdsourced content.
Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, ...his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.
This paper presents the “CDAC AI life cycle,” a comprehensive life cycle for the design, development, and deployment of artificial intelligence (AI) systems and solutions. It addresses the void of a ...practical and inclusive approach that spans beyond the technical constructs to also focus on the challenges of risk analysis of AI adoption, transferability of prebuilt models, increasing importance of ethics and governance, and the composition, skills, and knowledge of an AI team required for successful completion. The life cycle is presented as the progression of an AI solution through its distinct phases—design, develop, and deploy—and 19 constituent stages from conception to production as applicable to any AI initiative. This life cycle addresses several critical gaps in the literature where related work on approaches and methodologies are adapted and not designed specifically for AI. A technical and organizational taxonomy that synthesizes the functional value of AI is a further contribution of this article.
•The CDAC AI life cycle is a comprehensive approach from conception to production of AI•It begins with risk assessment, followed by design, develop, and deploy phases•The technical details of 19 constituent stages of the life cycle are articulated•A taxonomy of AI capabilities and the organizational context of AI are presented
Artificial intelligence is enabling new opportunities for value creation in organizations, industries, communities, and overall society. However, exponential commercial investments in the private sector should not overlook the public interest and social value of this emerging technology powerhouse. The world’s superpowers are also competing for leadership in AI and the contentious pursuit of artificial general intelligence. Collectively for commercial, social, and national interests, as well as internal and external stakeholders, it is vital to know, observe, understand, apply, adapt, and even critique an inclusive, evidence-based approach to conceptualize, assess risks, design, develop, and deploy an AI solution. This article presents the CDAC AI life cycle, a comprehensive approach that addresses and accounts for all challenges from conception to production of AI.
Artificial intelligence (AI) is embedded in our way of life, transforming how we live and work. AI is enabling new opportunities for value creation in organizations, industries, and society. Collectively for commercial, social, and national interests, as well as all corresponding stakeholders, it is vital to know, apply, adapt, and even critique an inclusive, evidence-based approach to conceptualize, assess risks, design, develop, and deploy an AI solution. This article presents the CDAC AI life cycle, from conception to production of AI.
A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, ...yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines.
We have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed.
Findings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.
Work-related stress is an uncompromising burden with compounding effects on individuals, communities, organizations, and the economy. Highly automated digital environments, such as smart factories ...and cyber-physical ecosystems, are significantly impacted by these negative effects due to the inherent constraints on human engagement and social interaction. Verbal communication between human operators is a critical resource in such environments, as it can be effectively utilized to address this challenge. A mix of statistical and artificial intelligence (AI) techniques have been reported in recent literature for the detection of stress-related indicators from audio recordings. However, none of these studies has focused on the challenges of detecting work-related stress in cyber-physical environments, such as smart factories, where verbal communication is not only constrained but also impaired by background noise and other disturbances common to factory settings. In this paper, we address these challenges by proposing a novel deep learning approach, based on the workings of the Convolutional Neural Network (CNN) and Growing Self-Organizing Map (GSOM) algorithms. In addition to this novel AI approach, sampling strategy and speaker diarization techniques are utilised for noise reduction. Pitch and speech augmentation techniques address the data imbalance issue common in most real-world datasets. The accuracy and effectiveness of the proposed approach is demonstrated using a benchmark dataset (DAIC-WOZ). We report F1 scores of 82% and 64% for normal and distressed classes respectively, which outperform the state-of-the-art models. We conclude with a discussion on the empirical evaluation of the proposed approach in cyber-physical environments and directions for future work.
Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers ...of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions.
The emergence of IoT and advanced multimedia information systems have undoubtedly created a proliferation of video sensor data. Although diverse machine learning approaches are utilized to extract ...useful insights from these data, limitations occur when processing and accommodating the large volumes of video data, which are unlabeled and have previously unseen data structures. This brings out the importance of using self-structuring intelligence that can adapt to the nature of the data and with the ability to learn from multi-modal, spatiotemporal and unstructured data. Encompassing these advances, we propose a recurrent self-structuring machine learning approach for video processing using multi-stream hierarchical recurrent growing self-organizing maps (RGSOM) architecture. We have designed, implemented and evaluated the said approach using a human activity recognition video dataset (Weizmann dataset), achieving state-of-the-art accuracy of 93.5% in the unsupervised domain. We used both spatial and temporal data from the video as separate input feature streams, where RGSOMs were used to self-structure the video data in multi-streams for visual exploratory analysis and video classification. As potential implications, this study can contribute to the existing literature in advancing self-adaptation techniques for video sensor data processing.
Direct policy learning (DPL) is a widely used approach in imitation learning for time-efficient and effective convergence when training mobile robots. However, using DPL in real-world applications is ...not sufficiently explored due to the inherent challenges of mobilizing direct human expertise and the difficulty of measuring comparative performance. Furthermore, autonomous systems are often resource-constrained, thereby limiting the potential application and implementation of highly effective deep learning models. In this work, we present a lightweight DPL-based approach to train mobile robots in navigational tasks. We integrated a safety policy alongside the navigational policy to safeguard the robot and the environment. The approach was evaluated in simulations and real-world settings and compared with recent work in this space. The results of these experiments and the efficient transfer from simulations to real-world settings demonstrate that our approach has improved performance compared to its hardware-intensive counterparts. We show that using the proposed methodology, the training agent achieves closer performance to the expert within the first 15 training iterations in simulation and real-world settings.
Social media encapsulates one of the most prominent human information behaviours that has rapidly evolved to create a new data-driven paradigm that uses data-intensive digital environments to ...communicate, collaborate, express opinions and support decisions. This has established social media as a unique information asset for value co-creation as it empowers individuals to actively express opinions and sentiment on all facets of interactions with an external entity. Despite recent research on the theoretical underpinnings of social media in open service innovation, practical demonstrations of actionable insights are limited, mainly due to the voluminous and unstructured nature of social media data. We address this limitation by presenting an evidence-based study that uses machine learning algorithms to generate actionable insights of strategic value from this data-driven paradigm. These outcomes provide fresh perspectives and new thinking that advances social media as an emergent information asset for end-to-end open innovation and incremental value co-creation.
The event-indexing situation models are introduced as event models derived from language to facilitate comprehension and memory retrieval. These models explain how fragmental information about events ...are collected, integrated and updated into a coherent set of views of what the text is about. The models are adopted as the basis of this study as an attempt to capture the event with contextual, dynamic, and social features, as conveyed by the vast volumes of online textual resources. Information in social media is received through highly personalized channels and is reshaped and interpreted in a more individual, segmental and real-time manner. The reprocessed information is then spread at high speed to a wider range of receivers. Therefore, the interpretation of mainstream media content is influenced by ongoing and dynamic contribution of opinions by users empowered by social media. This new phenomenon has not been examined so far from the perspective of the impact on conventional situation models. This paper explores how collaborative and sharing aspects of social media emphasize subjectivity of interpretation of mainstream media and proposes an extended cognitive situation model which better represents event-centric knowledge. This study investigates the mechanisms for constructing and updating the situation models with continuous textual information streamed from heterogeneous forms of media. It also empirically demonstrates how the proposed model can enhance the understanding of subjective aspects of events with dynamic social opinions.
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