An intelligent multimodal medical diagnosis system based on patients’ medical questions and structured symptoms for telemedicine

Faris, Hossam; Habib, Maria; Faris, Mohammad; Elayan, Haya; Alomari, Alaa

doi:10.1016/j.imu.2021.100513

Cited by 21 publications

(17 citation statements)

References 23 publications

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“…Domain-specific embedding is used for disease diagnosis to analyze patients' medical inquiries and structured symptoms. The fusion-based technique obtains the maximum accuracy of 84.9% and effectively supports telemedicine for meaningful drug prescriptions (Faris et al 2021 ).…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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“…The output of the combination showed promising predictive ability with a classification accuracy of 84.9%, indicating the potential of the model in predicting the diagnosis of possible patient conditions based on the given symptoms and patients’ questions that consequently could aid clinicians in making the right decisions. 22 …”

Section: Telemedicine and Ai During Covid-19 Era: Current Applicationmentioning

confidence: 99%

Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges

et al. 2022

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Background Asymptomatic and high-risk COVID-19 patients are advised to self-isolate at home. However, patients may not realize that the condition is deteriorating until too late. Objective This study aims to review various artificial intelligence-based telemedicine research during the COVID-19 outbreak and proposes a framework for developing telemedicine powered by artificial intelligence to monitor progression in COVID-19 patients during isolation at home. It also aims to map challenges using artificial intelligence-based telemedicine in the community. Methods A systematic review was performed for the related articles published in 2019–2021 and conducted in the PubMed and ScienceDirect database using the keywords “telemedicine,” “artificial intelligence,” and “COVID-19”. The inclusion criteria were full-text articles and original research written in the English language. Results Thirteen articles were included in this review to describe the current application of artificial intelligence-based telemedicine during the COVID-19 pandemic. Various current applications have been implemented, such as for early diagnosis and tracing of contact for the users, to monitor symptoms and decision-making treatment, clinical management, and virtual and remote treatment. We also proposed the framework of telemedicine powered by artificial intelligence for support the self-isolation of COVID-19 patients based on the recent update in technology. However, we identified some challenges for using digital health technologies because of the ethical and practical use, the policy and regulation, and device use both for healthcare workers and patients. Conclusion Artificial intelligence promises to improve the practice of medicine in various ways. However, practical applications still need to be explored, and medical professionals also need to adapt to these advances for better healthcare delivery to the public.

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“…The CNN features are concatenated before passing through the classification model. Faris et al (2021) proposed a multimodal framework for medical diagnosis from 263, 867 unstructured medical questions and structured symptom data. Term frequency (TF) and inverse document frequency (IDF), hashing vectorizer and doc2vec models were utilized to extract features from structured and unstructured data.…”

Section: Multimodal Medical Data Extractionmentioning

confidence: 99%

“…presented a late fusion framework to detect early diagnosis of prostate cancer by fusing MRI and clinical biomarkers using the Stacked Nonnegativity Constraint Sparse Autoencoders (SNCSAE) technique. The individual features are passed through two classifiers, and the final fusion technique yields diagnostic probabilities Faris et al (2021). proposed a late fusion strategy including summation, ranking and multiplication to fuse unstructured features extracted from patient questionnaires and structured symptom data.…”

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confidence: 99%

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Shetty¹,

Ananthanarayana²,

Mahale³

2022

AIP

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Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems. Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system. Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential. This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification. A brief outline of the existing medical multimodal data processing work is presented. The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications. The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies. The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.

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An intelligent multimodal medical diagnosis system based on patients’ medical questions and structured symptoms for telemedicine

Cited by 21 publications

References 23 publications

Impact of word embedding models on text analytics in deep learning environment: a review

Impact of word embedding models on text analytics in deep learning environment: a review

Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

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