Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

Woman-centred ethics: A feminist participatory action research

Background: The maternity system has a complexity of everyday ethical issues. The bioethical principles: non maleficence, beneficence justice and autonomy, that govern health care practice have been criticised as abstract, patriarchal and even rhetorical in maternity care practice (MacLellan, 2014) and consequently may be insufficient in guiding care of childbearing women. Midwifery-led care is guided by the International Confederation of Midwives International Code of Ethics, which considers more than the bioethical principles, such as the importance of relationship. Care ethics is a relational based feminist ethics first described by Gilligan (1983) and has been theorised as an alternate paradigm for midwifery (Newnham & Kirkham, 2019). A paper was published in Nursing Ethics as a result of the literature review; Care ethics framework for midwifery practice: A scoping review and it was determined that care ethics is demonstrated in practice with four domains; Relationship, Context, Caring Practices and Attention to power. In addition, there is limited empirical evidence as to women’s experiences of care from an ethical perspective, and importantly what women describe as ethical, revealing a gap in the literature that has yet to be explored. Objective: The aim of the study reported in this thesis was to investigate women’s experience of maternity care from an ethical perspective and to determine whether a care ethics paradigm would better suit midwifery. Methods: The transformative research was undertaken using Feminist Participatory Action Research (FPAR). FPAR is a feminist and transformative research design, which includes participants as central to research design. Purposive sampling was used to recruit women who had experienced midwifery-led care. Nine women formed the Community action research group (CARG), they worked with me over three years, guided the research and planned action. The CARG participated in five focus groups, for data collection and organising action toward the changes they wished to see. Their involvement in the research included: defining the research problem, creating a priori codes for analysis, reviewing analysis, disseminating findings and provided recommendations for policy change. A paper was published in Woman and Birth: Navigating midwifery solidarity: A feminist participatory action research framework, describing some of the finer points of FPAR including a framework for novice researchers. In phase two a further ten women who had had midwifery-led care were involved in this study and interviewed about their experiences of ethical maternity care. Data were collected from September 2019 to April 2022 via five focus group interviews and ten one-on-one semi-structured interviews. The interviews were recorded, and transcribed, and template and Reflexive thematic analysis was applied (Braun & Clark, 2021). Findings: The findings in this study were presented in two parts. The first phase of the study revealed midwifery-led care demonstrated care ethics in practice. The Community Action Research Group (CARG) created a priori codes and a template analysis determined that midwifery models of care demonstrate care ethics. A paper of these findings was published in Nursing Ethics; Does midwifery-led care demonstrate care ethics? A template analysis. In the second phase of the study, the data corpus was analysed using reflexive thematic analysis and the primary theme, Radical desires: Individuals’ values and context, captures the woman at the centre of the care, her values and context, as central to understanding ethics. The quality of the relationship, the knowledge that was shared, and the manner of the care given were deemed important elements of ethical care. I assigned categories Woman-centred ethics or Authoritarian ethics to describe these elements of ethical or unethical aspects of care. Woman-centred ethics contains the subcategories of: harmonised relationship, transparent wisdom, and midwifery solidarity. The category Authoritarian ethics contains the subcategories of: uneasy alliance, opaque information, and saving women from themselves. How the woman experienced these categories affected the liminality and sense of self, and are described in subthemes, Claiming power and Surrendered power. Discussion: The themes were explained, discussed, and contrasted against the extant literature in the discussion. Pregnancy and birth as a transformative rite of passage was valued by the women in this study and they perceived care as more ethical when the care providers respected this. Authoritarian ethics, when viewed with a feminist and care ethics lens highlighted continued female oppression from the maternity system structures and culture. A conceptual model, Woman-centred ethics, was developed based on midwifery philosophy and feminist care ethics, which may help midwives embody a different kind of ethics and provides a way to enhanced ethical practice. A paper was published that shared the conceptual model in Midwifery Journal: Woman-centred ethics: A feminist participatory action research. Conclusion: This study has contributed to the body of knowledge that describes how women perceive ethics in maternity, and honours women’s voices as central to ethical care. The study advances midwifery philosophy through exploring midwifery ethics and offers a conceptual model to guide practice. The woman-centred ethics model describes an embodied way of practicing ethical care and may provide a starting point for moving the field forward in ethical discussion. The CARG group involvement in the research and action together were an important feature of this project. Several recommendations arose from this study for practice, organisational, and educational processes

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Deep Learning Models For Biomedical Data Analysis

The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis. During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset. Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics. In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts. Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning

An analytical framework of tissue-patch clustering for quantifying phenotypes of whole slide images

Histopathology is considered the most practical diagnostic method for patient with early stage cancer. This is because at the very first pre-screening, patient’s tissue samples are delivered to pathologist for examining evidence of cancer. Computational scientists aid pathologist by heavily producing research on machine learning-based morphological pattern recognition of tissue image. Many data modelling investigations on histopathology have been conducted in supervised manner and some of them were further employed in real-life clinical diagnosis. This study proposes an approach to developing clusters of tissue tile. The main aim is to obtain ’high-quality clusters’ with respect to phenotypic annotations. In order to achieve this goal, two colorectal datasets namely 100k-nct and TCGA-COAD are experimented, one of which is directly annotated with tissue type, and other dataset is annotated through derivation from patient metadata, quiescent status. Four main independent variables were explored in this study (i) feature extraction by Resnet50, InceptionV3, VGG16 and an unsupervised generative model, PathologyGAN. (ii) feature space transformer including original feature, 3D PCA feature and 3D-UMAP feature and (iii) clustering algorithms namely Gaussian Mixture Model and Hierarchical clustering and their primary hyper-parameters. As a result, Resnet50 empowered by UMAP outperformed the most in clustering tissue type on 100k-nct dataset at v-measure of 0.74. The other dataset of which quiescent status is derived from patients encountered nearly zero in v-measure. However, clustering this quiescence-based dataset on 3D-UMAP Pathology-GAN yielded far higher V-measure than the rest of cluster configurations and illustrates ability to capture quiescence-related phenotype through visualisation

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors