Globally, the outbreaks of infectious diseases have exerted an extremely profound and severe influence on health security and the economy. During the critical phases of epidemics, devising effective intervention measures poses a significant challenge to both the academic and practical arenas. There is numerous research based on reinforcement learning to optimize intervention measures of infectious diseases. Nevertheless, most of these efforts have been confined within the differential equation based on infectious disease models. Although a limited number of studies have incorporated reinforcement learning methodologies into individual-based infectious disease models, the models employed therein have entailed simplifications and limitations, rendering it incapable of modeling the complexity and dynamics inherent in infectious disease transmission. We establish a decision-making framework based on an individual agent-based transmission model, utilizing reinforcement learning to continuously explore and develop a strategy function. The framework's validity is verified through both experimental and theoretical approaches. Covasim, a detailed and widely used agent-based disease transmission model, was modified to support reinforcement learning research. We conduct an exhaustive exploration of the application efficacy of multiple algorithms across diverse action spaces. Furthermore, we conduct an innovative preliminary theoretical analysis concerning the issue of "time coverage". The results of the experiment robustly validate the effectiveness and feasibility of the methodological framework of this study. The coping strategies gleaned therefrom prove highly efficacious in suppressing the expansion of the epidemic scale and safeguarding the stability of the economic system, thereby providing crucial reference perspectives for the formulation of global public health security strategies.
Recent advances in reasoning with large language models (LLMs)has shown remarkable reasoning capabilities in domains such as mathematics and coding, yet their application to clinical diagnosis remains underexplored. Here, we introduce ClinicalGPT-R1, a reasoning enhanced generalist large language model for disease diagnosis. Trained on a dataset of 20,000 real-world clinical records, ClinicalGPT-R1 leverages diverse training strategies to enhance diagnostic reasoning. To benchmark performance, we curated MedBench-Hard, a challenging dataset spanning seven major medical specialties and representative diseases. Experimental results demonstrate that ClinicalGPT-R1 outperforms GPT-4o in Chinese diagnostic tasks and achieves comparable performance to GPT-4 in English settings. This comparative study effectively validates the superior performance of ClinicalGPT-R1 in disease diagnosis tasks. Resources are available at https://github.com/medfound/medfound.
Electronic health records (EHRs) enable strong clinical prediction, but explanations are often coarse and hard to use for patient-level decisions. We propose a knowledge graph (KG)-guided chain-of-thought (CoT) framework for visit-level disease prediction on MIMIC-III. We map ICD-9 codes to PrimeKG, mine disease-relevant nodes and paths, and use these paths to scaffold temporally consistent CoT rationales, retaining only samples whose conclusions match observed outcomes. We fine-tune lightweight instruction-tuned LLMs (LLaMA-3.1-Instruct-8B and Gemma-7B) on two small cohorts (400 and 1,000 index visits) across ten PrimeKG-mapped diseases. Our models outperform strong classical baselines, reaching AUROC 0.66-0.70 and macro-AUPR 0.40-0.47. Without additional training, the models transfer zero-shot to the CRADLE cohort, improving accuracy from 0.40-0.51 to 0.72-0.77. In a blinded clinician study, KG-guided CoT rationales are consistently preferred for clarity, relevance, and correctness. Code is available at: https://github.com/JonathanWry/KG-guided-LLM-pipeline
If brain anatomy and dynamics have a genuine complex network structure as it has become standard to posit, it is also reasonable to assume that such a structure should play a key role not only in brain function but also in brain dysfunction. However, exactly how network structure is implicated in brain damage and whether at least some pathologies can be thought of as "network diseases" is not entirely clear. Here we discuss ways in which a complex network representation can help characterising brain pathology, but also subjects' vulnerability to and likelihood of recovery from disease. We show how the way disease is defined is related to the way function is defined and this, in turn, determines which network property may be functionally relevant to brain disease. Thus, addressing brain disease "networkness" may shed light not only on brain pathology, with potential clinical implications, but also on functional brain activity, and what is functional in it.
While agriculture is recognised as vital for improving nutrition, the evidence linking women's participation to sustained nutritional gains remains inconclusive. This review synthesizes studies published between 2000 and 2024 to reflect current agricultural practices and nutritional challenges. We examine how agricultural practices and time use affect nutritional outcomes among rural women through pathways such as income generation food preparation and intra-household labour allocation. A structured methodology with clear inclusion and exclusion criteria was used to assess gender-sensitive and nutrition-sensitive interventions. Using narrative synthesis the review categorizes findings around key themes and contextual factors including socio-economic status seasonality and labour intensity. The results show that while increased involvement in agriculture can boost household dietary diversity and income it also raises time burdens that affect food preparation childcare and self-care. Positive outcomes occur when interventions enhance women's decision-making power income access and use of time-saving technologies whereas negative outcomes emerge when excessive workloads compromise energy balance and limit rest. A conceptual framework is presented to map the dual pathways linking agriculture time use and nutrition capturing the roles of labour distribution social norms and resource access. The framework underscores the need to integrate gender equity time efficiency and nutritional objectives into agricultural policies. In conclusion agricultural interventions have potential for nutritional improvement if they are carefully designed to avoid unintended negative impacts on women.
Mahfuzul Haque, Abu Saleh Musa Miah, Debashish Gupta
et al.
Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,204 annual fatalities in Bangladesh. However, the development of heart disease detection (HDD) systems tailored to the Bangladeshi population remains underexplored due to the lack of benchmark datasets and reliance on manual or limited-data approaches. This study addresses these challenges by introducing new, ethically sourced HDD dataset, BIG-Dataset and CD dataset which incorporates comprehensive data on symptoms, examination techniques, and risk factors. Using advanced machine learning techniques, including Logistic Regression and Random Forest, we achieved a remarkable testing accuracy of up to 96.6\% with Random Forest. The proposed AI-driven system integrates these models and datasets to provide real-time, accurate diagnostics and personalized healthcare recommendations. By leveraging structured datasets and state-of-the-art machine learning algorithms, this research offers an innovative solution for scalable and effective heart disease detection, with the potential to reduce mortality rates and improve clinical outcomes.
Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi
et al.
Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms.
Quan Huu Cap, Atsushi Fukuda, Satoshi Kagiwada
et al.
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to detect disease locations and superior classification performance. One drawback of these detection systems is dealing with unannotated healthy data with no real symptoms present. In practice, healthy plant data appear to be very similar to many disease data. Thus, those models often produce mis-detected boxes on healthy images. In addition, labeling new data for detection models is typically time-consuming. Hard-sample mining (HSM) is a common technique for re-training a model by using the mis-detected boxes as new training samples. However, blindly selecting an arbitrary amount of hard-sample for re-training will result in the degradation of diagnostic performance for other diseases due to the high similarity between disease and healthy data. In this paper, we propose a simple but effective training strategy called hard-sample re-mining (HSReM), which is designed to enhance the diagnostic performance of healthy data and simultaneously improve the performance of disease data by strategically selecting hard-sample training images at an appropriate level. Experiments based on two practical in-field eight-class cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our HSReM training strategy leads to a substantial improvement in the overall diagnostic performance on large-scale unseen data. Specifically, the object detection model trained using the HSReM strategy not only achieved superior results as compared to the classification-based state-of-the-art EfficientNetV2-Large model and the original object detection model, but also outperformed the model using the HSM strategy in multiple evaluation metrics.
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.
Shashwat Jha, Vishvaditya Luhach, Gauri Shanker Gupta
et al.
Crops hold paramount significance as they serve as the primary provider of energy, nutrition, and medicinal benefits for the human population. Plant diseases, however, can negatively affect leaves during agricultural cultivation, resulting in significant losses in crop output and economic value. Therefore, it is crucial for farmers to identify crop diseases. However, this method frequently necessitates hard work, a lot of planning, and in-depth familiarity with plant pathogens. Given these numerous obstacles, it is essential to provide solutions that can easily interface with mobile and IoT devices so that our farmers can guarantee the best possible crop development. Various machine learning (ML) as well as deep learning (DL) algorithms have been created & studied for the identification of plant disease detection, yielding substantial and promising results. This article presents a novel classification method that builds on prior work by utilising attention-based feature extraction, RGB channel-based chromatic analysis, Support Vector Machines (SVM) for improved performance, and the ability to integrate with mobile applications and IoT devices after quantization of information. Several disease classification algorithms were compared with the suggested model, and it was discovered that, in terms of accuracy, Vision Transformer-based feature extraction and additional Green Chromatic Coordinate feature with SVM classification achieved an accuracy of (GCCViT-SVM) - 99.69%, whereas after quantization for IoT device integration achieved an accuracy of - 97.41% while almost reducing 4x in size. Our findings have profound implications because they have the potential to transform how farmers identify crop illnesses with precise and fast information, thereby preserving agricultural output and ensuring food security.
Existing learning models often utilise CT-scan images to predict lung diseases. These models are posed by high uncertainties that affect lung segmentation and visual feature learning. We introduce MARL, a novel Multimodal Attentional Representation Learning model architecture that learns useful features from multimodal data under uncertainty. We feed the proposed model with both the lung CT-scan images and their perspective historical patients' biological records collected over times. Such rich data offers to analyse both spatial and temporal aspects of the disease. MARL employs Fuzzy-based image spatial segmentation to overcome uncertainties in CT-scan images. We then utilise a pre-trained Convolutional Neural Network (CNN) to learn visual representation vectors from images. We augment patients' data with statistical features from the segmented images. We develop a Long Short-Term Memory (LSTM) network to represent the augmented data and learn sequential patterns of disease progressions. Finally, we inject both CNN and LSTM feature vectors to an attention layer to help focus on the best learning features. We evaluated MARL on regression of lung disease progression and status classification. MARL outperforms state-of-the-art CNN architectures, such as EfficientNet and DenseNet, and baseline prediction models. It achieves a 91% R^2 score, which is higher than the other models by a range of 8% to 27%. Also, MARL achieves 97% and 92% accuracy for binary and multi-class classification, respectively. MARL improves the accuracy of state-of-the-art CNN models with a range of 19% to 57%. The results show that combining spatial and sequential temporal features produces better discriminative feature.
Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. Both conditions require a tissue biopsy for diagnosis and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. The dataset used in this research is collected from different centers with different staining standards. To solve this problem, we use color balancing in order to train our model with a varying range of colors. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. RMDL combines different architectures and structures of deep learning and the final output of the model is based on the majority vote. CD is a chronic autoimmune disease that affects the small intestine genetically predisposed children and adults. Typically, CD rapidly progress from Marsh I to IIIa. Marsh III is sub-divided into IIIa (partial villus atrophy), Marsh IIIb (subtotal villous atrophy), and Marsh IIIc (total villus atrophy) to explain the spectrum of villus atrophy along with crypt hypertrophy and increased intraepithelial lymphocytes. In the second part of this study, we proposed two ways for diagnosing different stages of CD. Finally, in the third part of this study, these two steps are combined as Hierarchical Medical Image Classification (HMIC) to have a model to diagnose the disease data hierarchically.
Jiahong Ouyang, Qingyu Zhao, Edith V Sullivan
et al.
Many neurological diseases are characterized by gradual deterioration of brain structure and function. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in line with disease progression. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). In all three experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.
Infectious diseases spread via pathogens such as viruses and bacteria. Airborne pathogen transmission via droplets is an important mode for infectious diseases. In this paper, the spreading mechanism of infectious diseases by airborne pathogen transmission between two humans is modeled with a molecular communication perspective. An end-to-end system model which considers the pathogen-laden cough/sneeze droplets as the input and the infection state of the human as the output is proposed. This model uses the gravity, initial velocity and buoyancy for the propagation of droplets and a receiver model which considers the central part of the human face as the reception interface is proposed. Furthermore, the probability of infection for an uninfected human is derived by modeling the number of propagating droplets as a random process. The numerical results reveal that exposure time and sex of the human affect the probability of infection. In addition, the social distance for a horizontal cough should be at least 1.7 m and the safe coughing angle of a coughing human to infect less people should be less than -25$^\circ$.
We propose a mathematical model based on probability theory to optimize COVID-19 testing by a multi-step batch testing approach with variable batch sizes. This model and simulation tool dramatically increase the efficiency and efficacy of the tests in a large population at a low cost, particularly when the infection rate is low. The proposed method combines statistical modeling with numerical methods to solve nonlinear equations and obtain optimal batch sizes at each step of tests, with the flexibility to incorporate geographic and demographic information. In theory, this method substantially improves the false positive rate and positive predictive value as well. We also conducted a Monte Carlo simulation to verify this theory. Our simulation results show that our method significantly reduces the false negative rate. More accurate assessment can be made if the dilution effect or other practical factors are taken into consideration. The proposed method will be particularly useful for the early detection of infectious diseases and prevention of future pandemics. The proposed work will have broader impacts on medical testing for contagious diseases in general.