An architectural design study of electronic healthcare record systems with associated context parameters on MIMIC III

Paliwal, Gaurav; Bunglowala, Aaquil; Kanthed, Pravesh

doi:10.1007/s12553-022-00638-x

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…Electronic Health Records (EHR) contain a wealth of data about patients, including personal details, medical history, lab reports, diagnostics, and clinical notes from hospital stays [1]. Extracting accurate contextual information and knowledge from EHRs is crucial due to the grouping and linkage of data with previous healthcare events [35]. EHR data comprises both structured and unstructured types.…”

Section: Introductionmentioning

confidence: 99%

Diagnostics Based Patient Classification for Clinical Decision Support Systems

Paliwal,

Bunglowala,

Kanthed

2024

JAMRIS

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The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines.

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Section: Introductionmentioning

confidence: 99%

Diagnostics Based Patient Classification for Clinical Decision Support Systems

Paliwal,

Bunglowala,

Kanthed

2024

JAMRIS

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“…Two main attributes of HER, interoperability and confidentiality of data, have been addressed in that work. A clinical HER system has been proposed to overcome the limitations in its usage [ 18 ]. Instead of using the HER system for administrative purposes only, the authors proposed a clinical data utilization for clinical usage using associated context parameters.…”

Section: Introductionmentioning

confidence: 99%

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Mohanty¹,

Das

Mohanty

et al. 2022

Healthcare

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Background: The modern era of human society has seen the rise of a different variety of diseases. The mortality rate, therefore, increases without adequate care which consequently causes wealth loss. It has become a priority of humans to take care of health and wealth in a genuine way. Methods: In this article, the authors endeavored to design a hospital management system with secured data processing. The proposed approach consists of three different phases. In the first phase, a smart healthcare system is proposed for providing an effective health service, especially to patients with a brain tumor. An application is developed that is compatible with Android and Microsoft-based operating systems. Through this application, a patient can enter the system either in person or from a remote place. As a result, the patient data are secured with the hospital and the patient only. It consists of patient registration, diagnosis, pathology, admission, and an insurance service module. Secondly, deep-learning-based tumor detection from brain MRI and EEG signals is proposed. Lastly, a modified SHA-256 encryption algorithm is proposed for secured medical insurance data processing which will help detect the fraud happening in healthcare insurance services. Standard SHA-256 is an algorithm which is secured for short data. In this case, the security issue is enhanced with a long data encryption scheme. The algorithm is modified for the generation of a long key and its combination. This can be applicable for insurance data, and medical data for secured financial and disease-related data. Results: The deep-learning models provide highly accurate results that help in deciding whether the patient will be admitted or not. The details of the patient entered at the designed portal are encrypted in the form of a 256-bit hash value for secured data management.

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Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring

Guo,

Liu,

Niu

2023

Front. Neurorobot.

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Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services.

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An architectural design study of electronic healthcare record systems with associated context parameters on MIMIC III

Cited by 3 publications

References 45 publications

Diagnostics Based Patient Classification for Clinical Decision Support Systems

Diagnostics Based Patient Classification for Clinical Decision Support Systems

Design of Smart and Secured Healthcare Service Using Deep Learning with Modified SHA-256 Algorithm

Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring

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