Background And Objectives: In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs.
Methods: In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres.
Results: In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain.
Conclusions: The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making.
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http://dx.doi.org/10.1016/j.cmpb.2016.10.018 | DOI Listing |
J Imaging Inform Med
January 2025
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
The automated diagnosis of low-resolution and difficult-to-recognize breast ultrasound images through multi-modal fusion holds significant clinical value. However, prevailing fusion methods predominantly rely on image modalities, neglecting the textual pathology information, and only benign and malignant diagnosis of breast tumors is not satisfying for clinical applications. Consequently, this paper proposes a novel multi-modal fusion interactive diagnostic framework, termed the MIC framework, to achieve the multi-label classification of breast cancer, namely benign-malignant classification and breast imaging reporting and data system (BI-RADS) 3, 4a, 4b, 4c, and 5 gradings.
View Article and Find Full Text PDFFront Psychol
December 2024
Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, China.
Objective: This study proposes an emotion correlation-enhanced sentiment analysis model (ECO-SAM), a sentiment correlation modeling-based multi-label sentiment analysis model.
Methods: The ECO-SAM utilizes a pre-trained BERT encoder to obtain semantic embedding of input texts and then leverages a self-attention mechanism to model the semantic correlation between emotions. Additionally, it utilizes a text emotion matching neural network to make sentiment analysis for input texts.
Sci Rep
January 2025
College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161000, China.
Int J Pharm
January 2025
Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Ghent University, 9000 Gent, Belgium. Electronic address:
Cosmetic inspection of freeze-dried products is an important part of the post-manufacturing quality control process. Traditionally done by human visual inspection, this method poses typical challenges and shortcomings that can be addressed with innovative techniques. While many cosmetic defects can occur, some are considered more critical than others as they can be harmful to the patient or affect the drug's efficacy.
View Article and Find Full Text PDFSci Rep
December 2024
Hunan University of Chinese Medicine, Changsha, China.
Stroke has become the leading cause of disability in adults worldwide. Early precise rehabilitation intervention is crucial for the recovery of stroke patients, with the key lying in accurately identifying patients' physical characteristics during the rehabilitation phase. Compared to diagnostic techniques such as medical neuroimaging, traditional Chinese medicine(TCM) tongue diagnosis offers good accessibility and ease of application.
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