A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques

S., Abdul Razak M.; R., Nirmala C.; Aljohani, Maha; Sreenivasa, B. R.

doi:10.3389/frai.2022.950659

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…It is also planned to analyze hybrid methods [17], aiming to enhance prediction accuracy by capturing a broader range of features presented in spot market data. Lastly, data-driven models must be flexible to adapt to changing data sets [48] and require updated strategies [49,50]. With the suggested methodology, an automatic trading system can be created by monitoring the model's performance and possible data drifts.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Kılıç,

Nielsen,

Thibbotuwawa

2024

Energies

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For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Kılıç,

Nielsen,

Thibbotuwawa

2024

Energies

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“…Drift is particularly relevant to the healthcare industry, as disease patterns can change due to new strains of viruses and bacteria and disease patterns can change due to changes in treatment protocols ( 17 ). Data characteristics may also change due to improvements in medical equipment, changes in data collection methods, and changes in demographics.…”

Section: Architecting Resilient Mlops-based Medical Diagnostic Systemmentioning

confidence: 99%

Resilience-aware MLOps for AI-based medical diagnostic system

Moskalenko,

Kharchenko

2024

Front. Public Health

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BackgroundThe healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, so far, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences.MethodsPost-hoc resilience optimization, post-hoc predictive uncertainty calibration, uncertainty monitoring, and graceful degradation are incorporated as additional stages in MLOps. To optimize the resilience of the AI based medical diagnostic system, additional components in the form of adapters and meta-adapters are utilized. These components are fine-tuned during meta-training based on the results of adaptation to synthetic disturbances. Furthermore, an additional model is introduced for post-hoc calibration of predictive uncertainty. This model is trained using both in-distribution and out-of-distribution data to refine predictive confidence during the inference mode.ResultsThe structure of resilience-aware MLOps for medical diagnostic systems has been proposed. Experimentally confirmed increase of robustness and speed of adaptation for medical image recognition system during several intervals of the system’s life cycle due to the use of resilience optimization and uncertainty calibration stages. The experiments were performed on the DermaMNIST dataset, BloodMNIST and PathMNIST. ResNet-18 as a representative of convolutional networks and MedViT-T as a representative of visual transformers are considered. It is worth noting that transformers exhibited lower resilience than convolutional networks, although this observation may be attributed to potential imperfections in the architecture of adapters and meta-adapters.СonclusionThe main novelty of the suggested resilience-aware MLOps methodology and structure lie in the separating possibilities and activities on creating a basic model for normal operating conditions and ensuring its resilience and trustworthiness. This is significant for the medical applications as the developer of the basic model should devote more time to comprehending medical field and the diagnostic task at hand, rather than specializing in system resilience. Resilience optimization increases robustness to disturbances and speed of adaptation. Calibrated confidences ensure the recognition of a portion of unabsorbed disturbances to mitigate their impact, thereby enhancing trustworthiness.

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“…However, these models are not predicting accurately over a period. This is known a Model or Concept drift in different systems [ 116 ]. Many of the drift detection approaches has been proposed in different streams of applications [ 117 , 118 , 119 ]; however, much less in stream of RHMS.…”

Section: Major Challenges In Rhmsmentioning

confidence: 99%

Remote Health Monitoring Systems for Elderly People: A Survey

Ahmed

Irfan²,

Masood

et al. 2023

Sensors

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This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.

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A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques

Cited by 10 publications

References 8 publications

Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

Resilience-aware MLOps for AI-based medical diagnostic system

Remote Health Monitoring Systems for Elderly People: A Survey

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