DEEP LEARNING FOR SYMPTOM-TO-DISEASE TRIAGE TO IMPROVE DIAGNOSIS IN RURAL AND UNDERSERVED COMMUNITIES

AI-Assisted Healthcare Kiosk: Offline Disease Prediction, Medicine Recommendation, and Telemedicine Access for Rural India

An AI-assisted healthcare kiosk, a web-based, fully offline system that provides automated disease prediction, medication recommendations, BMI assessment, cardiovascular risk evaluation, and doctor referrals without needing a permanent physician or internet connection is discussed.

Harmony Search-Powered Stacked Model For Heart Disease Prediction

A stacking framework that uses harmony search optimization (HSO) to predict early cardiac disease in the course of the disease diagnosis process and the overall accuracy attained is 87%, multilayer stacking is 89%, and the stacking model employing HSO is 92%.

Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records

Accurate emergency triage decision is critical to avoid clinical deterioration, morbidity, and mortality. Machine learning-based triage system involves acquiring the main presenting complaint in text form and assessing vital signs in numerical data, enabling an automated and efficient analysis of patient information for timely and accurate prioritization of medical attention. However, modelling the intricacies of both data types requires a comprehensive understanding of the temporal structure and dependencies within the data. Thus, the aim of this study is to propose a multimodal deep learning architecture that can effectively handle both tabular and textual data. Furthermore, the proposed model exploits self-attention to to capture both local and global relationships between the features. A dataset consisting of 11,102 triage data collected from emergency department of Hospital Universiti Sains Malaysia is used for model development and validation. The proposed model demonstrated an increase of 1.95% in accuracy, 2.49% in F1-score, and 1.41% in ROC AUC compared to the baseline model. The experimental results demonstrated the potential of the proposed model in predicting triage decisions.

Intelligent technique in early liver disease prediction using real data samples from the perspective of Bangladesh

Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques

Sepsis is one of the most deadly illnesses with a high risk of mortality. Consequently, identifying it at the beginning of illness symptoms is crucial and plays a key role in improving patient outcomes. This study presents a customized solution for the early detection of sepsis with an emphasis on the use of interpretability and explainability techniques, utilizing a range of machine learning approaches and interpretable artificial intelligence methods. The database on which this research study is based has many problems; the main ones being large data gaps and class disparities. Employing robust methods, precise categorizations, and rigorous computations, Approximately 12 diverse models were developed and optimized. With ROC-AUC indicators of 0.9566 and 0.9595 and F1 scores of 0.85 and 0.85 respectively, Bidirectional Long Short-Term Memory (BiLSTM) and Temporal Convolutional Network (TCN) models performed better than conventional models in terms of sepsis prediction. These two approaches have shown remarkable progress in detecting clinical patterns while avoiding false negative results—an essential aspect of the medical field. To assess model performance and offer clear insights into model predictions, interpretation-based techniques were employed. This improved clinical confidence and facilitated well-informed decisions in crucial medical diagnoses.

AI-Driven Early Detection and Classification of Skin Diseases Using Deep Learning and Mobile Edge Computing

Skin disorders are experienced by millions of people across the globe, thus, calling for timely and accurate diagnosis in order to ensure proper treatment and good prognosis of the disease. Nevertheless, lack of access to dermatologists in some parts of the world, especially in rural areas and poor regions, often hinders proper and timely diagnosis of the condition. This work proposes an AI-based framework for automatic detection of skin disorders via deep learning and edge computing technology. Grad-CAM powered explainable artificial intelligence makes the model more transparent through region of interest identification in clinical images, whereas the lightweight web application makes predictions along with disease prediction confidence and visualization of diagnosis. Evaluation on HAM10000 and ISIC datasets attained 94.8% accuracy, 93.6% precision, 94.1% recall, 93.8% F1-score, and 95.4% mAP. The proposed solution presents an effective and scalable solution for AI-assisted dermatological diagnosis.