Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

A Non-Invasive Interpretable NAFLD Diagnostic Method Combining TCM Tongue Features

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by hepatic steatosis resulting from the exclusion of alcohol and other identifiable liver-damaging factors. It has emerged as a leading cause of chronic liver disease worldwide. Currently, the conventional methods for NAFLD detection are expensive and not suitable for users to perform daily diagnostics. To address this issue, this study proposes a non-invasive and interpretable NAFLD diagnostic method, the required user-provided indicators are only Gender, Age, Height, Weight, Waist Circumference, Hip Circumference, and tongue image. This method involves merging patients' physiological indicators with tongue features, which are then input into a fusion network named SelectorNet. SelectorNet combines attention mechanisms with feature selection mechanisms, enabling it to autonomously learn the ability to select important features. The experimental results show that the proposed method achieves an accuracy of 77.22\% using only non-invasive data, and it also provides compelling interpretability matrices. This study contributes to the early diagnosis of NAFLD and the intelligent advancement of TCM tongue diagnosis. The project in this paper is available at: https://github.com/cshan-github/SelectorNet

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Enfermagem e lógica fuzzy: uma revisão integrativa

This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.Este estudio tuvo como objetivo realizar una revisión integradora investigando como la lógica fuzzy ha sido utilizada en investigaciones con participación de enfermeros. La búsqueda de los artículos fue realizada en las bases de datos CINAHL, Embase, SCOPUS, Medline y PubMed, sin especificar un intervalo de años determinado. Fueron incluidos artículos en los idiomas: portugués, inglés y castellano; con una temática relacionada a la enfermería y a la lógica fuzzy; y con autoría o participación de enfermeros. La muestra final fue de 21 artículos, de ocho países. Para el análisis, los artículos fueron distribuidos en las categorías: teoría, método y modelo. En la enfermería, la lógica fuzzy ha contribuido significativamente para la comprensión de temas relativos a la imprecisión o a la necesidad del especialista, como método de investigación y en el desarrollo de modelos o sistemas de apoyo a la decisión y de tecnologías duras. El uso de la lógica fuzzy en la enfermería ha demostrado gran potencial y representa un vasto campo para investigaciones.Este estudo teve como objetivo realizar revisão integrativa, investigando como a lógica fuzzy tem sido utilizada em pesquisas com participação de enfermeiros. A busca dos artigos foi realizada nas bases de dados CINAHL, Embase, Scopus, MEDLINE e PubMed, sem intervalo de anos especificado. Foram incluídos artigos na língua portuguesa, inglesa e espanhola; com temática relacionada à enfermagem e à lógica fuzzy, e autoria ou participação de enfermeiros. A amostra final foi de 21 artigos, de oito países. Para análise, os artigos foram distribuídos nas categorias: teoria, método e modelo. Na enfermagem, a lógica fuzzy tem contribuído significativamente para a compreensão de temas relativos à imprecisão ou à necessidade do especialista, como método de pesquisa e no desenvolvimento de modelos ou sistemas de apoio à decisão e de tecnologias duras. O uso da lógica fuzzy, na enfermagem, tem demonstrado grande potencial e representa vasto campo para pesquisas

Improving Patient Care with Machine Learning: A Game-Changer for Healthcare

Machine learning has revolutionized the field of healthcare by offering tremendous potential to improve patient care across various domains. This research study aimed to explore the impact of machine learning in healthcare and identify key findings in several areas.Machine learning algorithms demonstrated the ability to detect diseases at an early stage and facilitate accurate diagnoses by analyzing extensive medical data, including patient records, lab results, imaging scans, and genetic information. This capability holds the potential to improve patient outcomes and increase survival rates.The study highlighted that machine learning can generate personalized treatment plans by analyzing individual patient data, considering factors such as medical history, genetic information, and treatment outcomes. This personalized approach enhances treatment effectiveness, reduces adverse events, and contributes to improved patient outcomes.Predictive analytics utilizing machine learning techniques showed promise in patient monitoring by leveraging real-time data such as vital signs, physiological information, and electronic health records. By providing early warnings, healthcare providers can proactively intervene, preventing adverse events and enhancing patient safety.Machine learning played a significant role in precision medicine and drug discovery. By analyzing vast biomedical datasets, including genomics, proteomics, and clinical trial information, machine learning algorithms identified novel drug targets, predicted drug efficacy and toxicity, and optimized treatment regimens. This accelerated drug discovery process holds the potential to provide more effective and personalized treatment options.The study also emphasized the value of machine learning in pharmacovigilance and adverse event detection. By analyzing the FDA Adverse Event Reporting System (FAERS) big data, machine learning algorithms uncovered hidden associations between drugs, medical products, and adverse events, aiding in early detection and monitoring of drug-related safety issues. This finding contributes to improved patient safety and reduced occurrences of adverse events.The research demonstrated the remarkable potential of machine learning in medical imaging analysis. Deep learning algorithms trained on large datasets were able to detect abnormalities in various medical images, facilitating faster and more accurate diagnoses. This technology reduces human error and ultimately leads to improved patient outcomes.While machine learning offers immense benefits, ethical considerations such as patient privacy, algorithm bias, and transparency must be addressed for responsible implementation. Healthcare professionals should remain central to decision-making processes, utilizing machine learning as a tool to enhance their expertise rather than replace it. This study showcases the transformative potential of machine learning in revolutionizing healthcare and improving patient care

A voyage to Mars: A challenge to collaboration between man and machines

A speech addressing the design of man machine systems for exploration of space beyond Earth orbit from the human factors perspective is presented. Concerns relative to the design of automated and intelligent systems for the NASA Space Exploration Initiative (SEI) missions are largely based on experiences with integrating humans and comparable systems in aviation. The history, present status, and future prospect, of human factors in machine design are discussed in relation to a manned voyage to Mars. Three different cases for design philosophy are presented. The use of simulation is discussed. Recommendations for required research are given