ABSTRACT Electronic health records have become more prevalent worldwide, and with this, the opportunity for more accurate and automated prediction of health events has grown. Such predictions are crucial for providing preventive and proactive healthcare to patients. Although various advanced methods have been explored, they often fail to fully leverage medical domain knowledge, understand interrelations between diseases and patients comprehensively, and efficiently integrate unstructured clinical notes into predictive models. To address these challenges, we propose the Medical Domain Knowledge Collaborative Graph Learning (MED‐CGL) model. MED‐CGL incorporates external medical knowledge bases to enhance the predictive power of unstructured clinical notes and extracts learnable features from the MIMIC‐III health record dataset using medical domain knowledge and collaborative graph learning. We introduce the Enhanced Medical Knowledge Integration (EMKI) module, which employs a novel attention mechanism to connect clinical notes with disease descriptions precisely. It also enhances the system's performance by integrating medical knowledge from the semantically labelled knowledge‐enhanced (SLAKE) dataset during the training phase. Furthermore, our model considers the complexities of unstructured clinical notes, providing a nuanced perspective on the interplay between diseases and patient profiles. Our experiments show that the MED‐CGL model exhibited outstanding performance in diagnosis prediction, achieving an F1 score of 27.32%, and in heart failure prediction, where it attained an accuracy of 91.39%. This significant improvement demonstrates the robustness and effectiveness of our model, which is further supported by our in‐depth ablation study.

Social media users often use disease or symptom terms in ways other than describing their health conditions, which can lead to flawed conclusions in data-driven public health surveillance. The health mention classification (HMC) task aims to identify posts in which users use disease or symptom terms to discuss their health conditions instead of using them for other reasons. Existing methods rely on features extracted from external resources and are tested on data from either Twitter or Reddit; therefore, their generalizability and transferability are unproven. In this work, we present MASK-Net, which masks disease or symptom terms and relies on the context of a post. Furthermore, to capture the negative sentiments associated with the experience of having a disease, we incorporate sentiment information to improve the HMC. We conduct experiments using publicly available health-mention datasets collected from Twitter and Reddit. Experimental results demonstrate that our method outperforms state-of-the-art methods on both HMC datasets, highlighting the relevance of context words in identifying HMC. Additionally, we evaluate our method in cross-domain and multidomain settings to analyze the transferability and generalizability of MASK-Net and conclude with a discussion on the empirical and ethical considerations of our study.

The rising rate of mental health issues in the digital age underscores the critical need for proactive interventions to assess an individual’s well-being. This problem is further exacerbated by the social stigma surrounding the subject, which suppresses the willingness of victims to seek help. Social media can serve as an outlet for such individuals to express their negative emotions or thoughts of self-harm. The social media account of an individual can offer a plethora of valuable information that can be used to predict their mental health. By unifying principles of robust classifier training and selective classification, we propose a novel framework, SAFENet, to predict the suicide risk of users by using their historical social media posts. When the confidence of prediction is low or the individual is classified as a high-risk user, SAFENet delegates the analysis of the posts to a human evaluator for further intervention. Our experiments show that SAFENet outperforms existing state-of-the-art frameworks. We further qualitatively analyze predictions from SAFENet and demonstrate that it performs robustly on difficult samples that may cause contemporary methods to make errors. Our system addresses the urgent need for efficient and effective mental health intervention in the digital era.

The digitization of healthcare systems has led to the proliferation of electronic health records (EHRs), serving as comprehensive repositories of patient information. However, the vast volume and complexity of EHR data present challenges in extracting meaningful insights. This paper addresses the need for automated analysis of EHRs by proposing a novel graph learning model with label attention (GLLA) for temporal event prediction. GLLA utilizes graph neural networks to capture intricate relationships between medical codes and patients, incorporating hierarchical structures and shared risk factors. Furthermore, it introduces the Label Attention and Attention-based Transformer (LAAT) algorithm to analyze unstructured clinical notes as a multi-label classification problem. Evaluation on the widely-used MIMIC III dataset demonstrates the efficacy of GLLA in enhancing diagnostic prediction performance. The contributions of this research include a comprehensive analysis of existing models, the identification of limitations, and the development of innovative approaches to improve the accuracy and effectiveness of EHR analysis. Ultimately, GLLA aims to advance healthcare decision-making, disease management strategies, and patient outcomes.

The creative web is all about combining different types of media to create a unique and engaging online experience. Multimodal data, such as text and images, is a key component in the creative web. Social media posts that incorporate both text descriptions and images offer a wealth of information and context. Text in social media posts typically relates to one topic, while images often convey information about multiple topics due to the richness of visual content. Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. Our experimental results show that CTM-MM outperforms traditional multimodal topic models in terms of classification and topic coherence.

Short texts are a common source of knowledge, and the extraction of such valuable information is beneficial for several purposes. Traditional topic models are incapable of analyzing the internal structural information of topics. They are mostly based on the co-occurrence of words at the document level and are often unable to extract semantically relevant topics from short text datasets due to their limited length. Although some traditional topic models are sensitive to word order due to the strong sparsity of data, they do not perform well on short texts. In this paper, we propose a novel word embedding-based topic model (WETM) for short text documents to discover the structural information of topics and words and eliminate the sparsity problem. Moreover, a modified collapsed Gibbs sampling algorithm is proposed to strengthen the semantic coherence of topics in short texts. WETM extracts semantically coherent topics from short texts and finds relationships between words. Extensive experimental results on two real-world datasets show that WETM achieves better topic quality, topic coherence, classification, and clustering results. WETM also requires less execution time compared to traditional topic models.

BACKGROUND: Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. METHODS: In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. RESULTS: Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. CONCLUSION: We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.