Digital Twin approach to Clinical DSS with Explainable AI

Rao, Dattaraj; Mane, Shraddha

doi:10.48550/arxiv.1910.13520

Cited by 6 publications

(6 citation statements)

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“…It aims to optimise surgical trauma procedures and improve postoperative management decision making. Furthermore, the authors in [ 23 ] proposed a DT-based approach to improve healthcare decision support systems. These authors used state-of-the-art explainability concepts to interpret machine learning models to give doctors a more generic perspective that helps the diagnosis.…”

Section: State Of Researchmentioning

confidence: 99%

“…Therefore, PDT technology could gain importance in helping clinicians expand their diagnosis by providing real-time data feeds about the patients’ current conditions. Furthermore, combing PDTs with explainable AI could enhance diagnosis and personalised treatments by obtaining an accurate picture of the patient health and recommending the proper care at the right time [ 16 , 23 , 35 ].…”

Section: Proposed Reference Framework For Smart Personalised Healthca...mentioning

confidence: 99%

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Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry

Sahal

Alsamhi

Brown

2022

Sensors

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Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry.

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Section: State Of Researchmentioning

confidence: 99%

Section: Proposed Reference Framework For Smart Personalised Healthca...mentioning

confidence: 99%

Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry

Sahal

Alsamhi

Brown

2022

Sensors

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“…In the area of DTs for wellbeing, several applications are presented, including applications for the detection of liver disease [10], ischemic heart disease detection [11], fitness management [4], blood circulation analysis [12], and trauma management [13], for example. Ferdousi et al ( 2021) also report on the progress in industrial production of corresponding products.…”

Section: Human Digital Twins: State Of Researchmentioning

confidence: 99%

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

Lehmann,

Granrath,

Browne

et al. 2024

Sustainability

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One of the central social challenges of the 21st century is society’s aging. AI provides numerous possibilities for meeting this challenge. In this context, the concept of digital twins, based on Cyber-Physical Systems, offers an exciting prospect. The e-VITA project, in which a virtual coaching system for elderly people is being created, allows the same to be assessed as a model for development. This white paper collects and presents relevant findings from research areas around digital twin technologies. Furthermore, we address ethical issues. This paper shows that the concept of digital twins can be usefully applied to older adults. However, it also shows that the required technologies must be further developed and that ethical issues must be discussed in an appropriate framework. Finally, the paper explains how the e-VITA project could pave the way towards developing a Digital Twin for Ageing.

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“…For example, the DigitTwin Consortium provides a platform for researchers in academia, industry, and government agencies to explore the value of digital twins technology across various sectors, promote such technology, determine best practices for its use, and to advance the technology [9,10]. The area of digital twins has been described as the marriage of three components-data science, software engineering, and expert knowledge-which culminates in clinical decision support (CDS) tools that are based upon a personalized medicine approach [11]. Unlike traditional engineering models, which are reflective of generic instances, digital twins reflect the individual characteristics of the subject [1].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike traditional engineering models, which are reflective of generic instances, digital twins reflect the individual characteristics of the subject [1]. In health care, the digital twin model provides the opportunity to see many predicted disease trajectories for a patient which has the potential to inform the patient's current state using data derived from that individual to avoid providing a "black box" prediction, in which an explanation for the trajectory is not readily apparent [11]. Digital twins are statistically indistinguishable from actual subjects and allow for individual-level statistical analyses of disease progression [1].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

et al. 2021

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Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient management. Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke. The digital twin’s ability to model patient clinical features was assessed with regard to its ability to forecast clinical measurement trajectories leading up to the onset of the acute medical event and beyond using International Classification of Diseases (ICD) codes for ischemic stroke and lab values as inputs. Results: The simulated patient trajectories were virtually indistinguishable from real patient data, with similar feature means, standard deviations, inter-feature correlations, and covariance structures on a withheld test set. A logistic regression adversary model was unable to distinguish between the real and simulated data area under the receiver operating characteristic (ROC) curve (AUCadversary = 0.51). Conclusion: Through accurate projection of patient trajectories, this model may help inform clinical decision making or provide virtual control arms for efficient clinical trials.

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Digital Twin approach to Clinical DSS with Explainable AI

Cited by 6 publications

References 0 publications

Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry

Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

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