Page 158 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
overview of the various techniques applied by researchers in B. Enhanced Text Processing
this domain, including those who worked on both image and  Contextual Embeddings: Incorporate contextualized
text data processing. word embeddings like BERT, BioBERT or ClinicalBERT
to capture the nuanced meaning of medical texts.
Image Data Processing:
1. Tianyu Han et al. [8]  Clinical Named Entity Recognition (NER): Develop an
 Model Used: Regularized Generative Adversarial NER module to identify and classify medical entities
Network (GAN) combined with a latent nearest (e.g., diseases, medications) within clinical notes.
neighbour algorithm.
C. Multi-Modal Data Fusion
 Methodology: They developed a methodology to predict  Attention Mechanisms: Utilize attention mechanisms to
disease progression by generating plausible images of dynamically weigh the importance of different
future time points. This enabled the prediction of modalities, enhancing the interpretability and accuracy
progression risk and morphology changes in individuals. of predictions.
2. Yaran Chen et al. [9]  Graph Neural Networks (GNNs): Explore the use of
 Model Used: Deep Neural Network (DNN) for multi- GNNs to model complex relationships between different
modal learning. data types and patient records.
 Methodology: Proposed a system combining clinical D. Real-Time Implementation
datasets with multi-modal learning, using facial images  Deployment on Edge Devices: Adapt the system for
and metadata to predict Non-Alcoholic Fatty Liver deployment on edge devices (e.g., mobile phones,
Disease (NAFLD) for diagnosis. portable medical devices) to enable real-time
diagnostics in remote or resource-limited settings.
Text Data Processing:
3. Jingshu Liu et al. [11]  Cloud-Based Integration: Implement a cloud-based
 Model Used: Deep Learning Architectures, including framework for scalable processing and storage of large-
Convolutional Neural Networks (CNN) and Long Short- scale medical data.
Term Memory (LSTM) networks.
E. User Interface and Experience
 Methodology: Developed a multi-task framework for  Intuitive Dashboard: Design an intuitive user interface
predicting disease onset, combining free-text medical for clinicians to interact with the system, visualize
notes and structured information while handling predictions, and access patient data seamlessly.
negations and numerical data in the text.
 User Feedback Loop: Implement a feedback mechanism
4. Jun-En Ding et al. [12] for clinicians to provide input and improve the system's
 Model Used: Large Language Multimodal Models accuracy and usability over time.
(LLMMs).
Our system integrates advanced techniques to revolutionize
 Methodology: Introduced a framework for chronic medical diagnostics. Using 3D imaging for detailed scan
disease risk prediction by integrating multimodal data analysis and image segmentation methods like UNet or Mask
from clinical notes and laboratory test results. They used RCNN, it accurately isolates anatomical regions and
text embedding encoders and multi-head attention abnormalities. For text processing, contextual embeddings
layers to improve prediction accuracy. such as BERT and BioBERT enhance understanding of
medical texts, while Clinical Named Entity Recognition (NER)
The reviewed studies on chronic disease prediction share identifies key medical entities. Multi-modal data fusion
several key factors. Firstly, they predominantly rely on employs attention mechanisms and Graph Neural Networks
machine learning algorithms such as SVM, Decision Trees, (GNNs) to model complex relationships, improving
and Neural Networks. Many studies also integrate predictions. Real-time deployment on edge devices and cloud
multimodal data (clinical, image, and text) to improve integration ensures accessibility and scalability. Personalized
prediction accuracy. Feature selection techniques like medicine is enabled through patient-specific models and
Pearson correlation and dimensionality reduction are predictive analytics, tailoring care to individuals. Clinical
commonly used to optimize model performance. Disease
validation via pilot studies and interdisciplinary
progression is often a focus, with models predicting not just
collaboration ensures efficiency, while an intuitive
the presence of diseases but also their future stages. Lastly,
dashboard and user feedback loop enhance usability and
the models are evaluated using common metrics such as
continuous improvement.
accuracy, sensitivity, and AUC to ensure their effectiveness in
real-world applications. IV. PROPOSED RESEARCH MODEL
III. PROPOSED WORK A. Raw Data Collection
A. Advanced Image Analysis Techniques  Objective: Gather unprocessed information about
patients and their health conditions.
 Integration of 3D Imaging: Extend the system to handle
3D medical imaging data such as MRIs and CT scans for  Sources: Medical records, imaging data, clinical notes,
more detailed analysis. patient-reported symptoms.
 Image Segmentation: Implement advanced image B. Data Pre-processing
segmentation techniques using U-Net or Mask R-CNN to 1. Cleaning the Data:
identify and isolate specific anatomical regions or  Handle missing values or inconsistencies to ensure
abnormalities within medical images. accuracy.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 148