Personalized Patient Care Research Articles

A critical appreciation of pathway analysis in atherosclerotic disease. Cellular phenotypic plasticity as an illustrative example

The rapid advancements in genome-scale (omics) techniques has created significant opportunities to investigate complex disease mechanisms in tissues and cells. Nevertheless, interpreting -omics data can be challenging, and pathway enrichment analysis is a frequently used method to identify candidate molecular pathways that drive gene expression changes. With a growing number of -omics studies dedicated to atherosclerosis, there has been a significant increase in studies and hypotheses relying on enrichment analysis.This brief review discusses the benefits and limitations of pathway enrichment analysis within atherosclerosis research. We highlight the challenges of identifying complex biological processes, such as cell phenotypic switching, within -omics data. Additionally, we emphasize the need for more comprehensive and curated gene sets that reflect the biological complexity of atherosclerosis.Pathway enrichment analysis is a valuable tool for gaining insights into the molecular mechanisms of atherosclerosis. Nevertheless, it is crucial to remain aware of the intrinsic limitations of this approach. By addressing these weaknesses, enrichment analysis in atherosclerosis can lead to breakthroughs in identifying the mechanisms of disease progresses, the identification of key driver genes, and consequently, advance personalized patient care.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Assessment of rehabilitation effectiveness in patients with COPD as part of the project "PulmoRehab – Access to healthcare services through a personalized care system for patients with COPD, including remote monitoring and tele-rehabilitation based on Artificial Intelligence methods"

Introduction. In the project “PulmoRehab – Access to Healthcare Services through a Personalized Patient Care System for COPD including Remote Monitoring and Tele-rehabilitation Based on Artificial Intelligence Methods,” SP ZOZ Specialized Hospital Ministry of Internal Affairs and Administration in Głuchołazy and 10 partners conducted research on tele-rehabilitation for people suffering from Chronic Obstructive Pulmonary Disease (COPD). The tele-rehabilitation program, based on personalized approach and technology, allowed patients to use therapy at home or a convenient location. The project aimed to reduce social inequalities in healthcare by providing remote access to health services for COPD patients. Materials and Methods. The study involved 80 patients aged 50 to 76 years, hospitalized at Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, meeting specific criteria. Qualification for the tele-rehabilitation program took into account the assessment of exercise tolerance, dyspnea, fatigue, lung ventilatory function, and saturation. The program included exercises adapted to the individual needs of the patients. Results. Analysis of the results conducted using XLSTAT software 2021.2.2 showed statistically significant differences between spirometric values before and after rehabilitation. Similarly, significant improvement was observed in the results of the 6MWT test and blood saturation. The tele-rehabilitation program proved effective in improving respiratory health parameters in COPD patients. Conclusions. 1. The tele-rehabilitation program improves lung ventilatory function in people with COPD. It reduces the feeling of dyspnea, decreases fatigue, improves SpO2, and increases exercise tolerance, demonstrating the beneficial effects of the rehabilitation applied. 2. The obtained results encourage further research on a representative group with the use of randomization.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature.

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Sex Differences in Cardiac and Clinical Phenotypes and Their Relation to Outcomes in Patients with Heart Failure.

Biological sex is one of the major factors characterizing the heart failure (HF) patient phenotype. Understanding sex-related differences in HF is crucial to implement personalized care for HF patients with various phenotypes. There are sex differences in left ventricular (LV) remodeling patterns in the HF setting, namely, more likely concentric remodeling and diastolic dysfunction in women and eccentric remodeling and systolic dysfunction in men. Recently supra-normal EF (snLVEF) has been recognized as a risk of worse outcome. This pathology might be more relevant in female patients. The possible mechanism may be through coronary microvascular dysfunction and sympathetic nerve overactivation from the findings of previous studies. Further, estrogen deficit might play a significant role in this pathophysiology. The sex difference in body composition may also be related to the difference in LV remodeling and outcome. Lower implementation in guideline-directed medical therapy (GDMT) in female HFrEF patients might also be one of the factors related to sex differences in relation to outcomes. In this review, we will discuss the sex differences in cardiac and clinical phenotypes and their relation to outcomes in HF patients and further discuss how to provide appropriate treatment strategies for female patients.

Advancements in Heart Transplantation: Donor-Derived Cell-Free DNA as Next-Generation Biomarker.

Heart failure, particularly in its advanced stages, significantly impacts quality of life. Despite progress in Guideline-Directed Medical Therapy (GDMT) and invasive treatments, heart transplantation (HT) remains the primary option for severe cases. However, complications such as graft rejection present significant challenges that necessitate effective monitoring. Endomyocardial biopsy (EMB) is the gold standard for detecting rejection, but its invasive nature, associated risks, and healthcare costs have shifted interest in non-invasive techniques. Donor-derived cell-free DNA (dd-cfDNA) has gained attention as a promising non-invasive biomarker for monitoring graft rejection. Compared to EMB, dd-cfDNA detects graft rejection early and enables clinicians to adjust immunosuppression promptly. Despite its advantages, dd-cfDNA testing faces challenges, such as the need for specialized technology and potential inaccuracies due to other clinical conditions. Additionally, dd-cfDNA cannot yet differentiate between types of graft rejection, and its effectiveness in chronic rejection remains unclear. Research is ongoing to set precise standards for dd-cfDNA levels, which would enhance its diagnostic accuracy and help in clinical decisions. The article also points to the future of HT monitoring, which may involve combining dd-cfDNA with other biomarkers and integrating artificial intelligence to improve diagnostic capabilities and personalize patient care. Furthermore, it emphasizes both global and racial inequalities in dd-cfDNA testing and the ethical issues related to its use in transplant medicine.

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

Personalized 3D-printed cranial implants for complex cranioplasty using open-source software.

Cranioplasty is a routine neurosurgery treatment used to correct cranial vault abnormalities. Utilization of 3D printing technology in the field of cranioplasty involving the reconstruction of cranial defects emerged as an advanced possibility of anatomical reshaping. The transformative impact of patient-specific 3D printed implants, focuses on their remarkable accuracy, customization capabilities, and enhanced biocompatibility. The precise adaptation of implants to patient-specific anatomies, even in complex cases we presented, result in improved aesthetic outcomes and reduced surgical complications. The ability to create highly customized implants addresses the functional aspects of cranial defects and considers the psychological impact on patients. By combining technological innovation with personalized patient care, 3D printed cranioplasty emerges as a transformative avenue in cranial reconstruction, ultimately redefining the standards of success in neurosurgery. 3D printing allows an excellent cranioplasty cosmesis achieved at a reasonable price without sacrificing patient outcomes. Wider implementation of this strategy can lead to significant healthcare cost savings.

Cluster Analysis to Identify Long COVID Phenotypes Using 129Xe Magnetic Resonance Imaging: A Multi-centre Evaluation.

Long COVID impacts ∼10% of people diagnosed with COVID-19, yet the pathophysiology driving ongoing symptoms is poorly understood. We hypothesised that 129Xe magnetic resonance imaging (MRI) could identify unique pulmonary phenotypic subgroups of long COVID, therefore we evaluated ventilation and gas exchange measurements with cluster analysis to generate imaging-based phenotypes. COVID-negative controls and participants who previously tested positive for COVID-19 underwent 129XeMRI ∼14-months post-acute infection across three centres. Long COVID was defined as persistent dyspnea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully-recovered. 129XeMRI ventilation defect percent (VDP) and membrane (Mem)/Gas, red blood cell (RBC)/Mem and RBC/Gas ratios were used in k-means clustering for long COVID, and measurements were compared using ANOVA with post-hoc Bonferroni correction. We evaluated 135 participants across three centres: 28 COVID-negative (40±16yrs), 34 fully-recovered (42±14yrs) and 73 long COVID (49±13yrs). RBC/Mem (p=0.03) and FEV1 (p=0.04) were different between long- and COVID-negative; FEV1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster1 was the youngest with normal MRI and mild gas-trapping; Cluster2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster3 had mildly increased Mem/Gas with normal PFTs; and Cluster4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern. We identified four 129XeMRI long COVID phenotypes with distinct characteristics. 129XeMRI can dissect pathophysiologic heterogeneity of long COVID to enable personalised patient care.

Sarcoidosis. A review of diagnosis, clinical features and treatment

Introduction: Sarcoidosis is a progressive chronic multisystemic inflammatory disease of unclear pathogenesis, which presents with noncaseating granulomas and is complicated to diagnose due to the variable clinical presentation of patients. Sarcoidosis may attack any organ, but especially the pulmonary, cutaneous, lymphatic, ocular, hepatic and splenic systems. The most frequent presentations are prolonged cough, rash, visual problems, peripheral lymphadenopathy, tiredness, and occasional abnormality of the chest X-ray. The diagnostic evaluation of sarcoidosis is difficult and often involves a combined effort of clinical data and appropriate imaging modalities. Purpose: The purpose of this article is to review the available literature on the epidemiology, clinical manifestations and management of patients with sarcoidosis Materials and method: The available literature in PubMed was reviewed to write the article, using the keywords ,,sarcoidosis”, ,,cardiac sarcoidosis”, ,,pulmonary sarcoidosis” and ,,cutaneous sarcoidosis”. Conclusion: Sarcoidosis is associated with a significant increase in patient mortality, as well as decreased quality of life and physical disability. Efforts to accurately predict disease progression allow for more comprehensive and personalized patient care. As a life threatening disease, sarcoidosis needs to be diagnosed early and treated appropriately.

Reimagine the ICU: Healthcare Professionals' Perspectives on How Environments (Can) Promote Patient Well-Being.

This study aims (1) to understand the needs and challenges of the current intensive care unit (ICU) environments in supporting patient well-being from the perspective of healthcare professionals (HCPs) and (2) to explore the new potential of ICU environments enabled by technology. Evidence-based design has yielded how the design of environments can advocate for patient well-being, and digital technology offers new possibilities for indoor environments. However, the role of technology in facilitating ICU patient well-being has been unexplored. This study was conducted in two phases. First, a mixed-method study was conducted with ICU HCPs from four Dutch hospitals. The study investigated the current environmental support for care activities, as well as the factors that positively and negatively contribute to patient experience. Next, a co-creation session was held involving HCPs and health technology experts to explore opportunities for technology to support ICU patient well-being. The mixed-method study revealed nine negative and eight positive patient experience factors. HCPs perceived patient emotional care as most challenging due to the ICU workload and a lack of environmental support in fulfilling patient emotional needs. The co-creation session yielded nine technology-enabled solutions to address identified challenges. Finally, drawing from insights from both studies, four strategies were introduced that guide toward creating technology to provide holistic and personalized care for patients. Patient experience factors are intertwined, necessitating a multifactorial approach to support patient well-being. Viewing the ICU environment as a holistic unit, our findings provide guidance on creating healing environments using technology.

The Experience of Being Diagnosed with Alzheimer’s Disease

Rationale and Aims: In research on Alzheimer’s disease (AD), the personal experience of individuals receiving this diagnosis is rarely the subject of research. Rather, the focus is restricted to the clinical symptoms and biological changes. Symptoms of AD are mainly reported by caregivers and rarely by the patients themselves, as they are deemed unreliable due to their memory problems. However, incorporating the voice of patients can improve the design of research projects and make the findings more relevant and meaningful. This could ultimately lead to improved and more personalized care for patients and their families. The aim of this study was to evaluate and quantify the personal experiences of individuals receiving a diagnosis of AD. Methods: A total of 50 patients with newly diagnosed (3–14 months) AD and 50 relatives (1:1) participated. All participants answered a number of questionnaires, including the Alzheimer Dementia Crossroad Questionnaire (ADCQ), which is a novel questionnaire designed to evaluate their perception on how much the disease affected their daily lives. The ADCQ was also administered to their relatives, to assess how they perceived the disease was affecting the patient. Results: Compared with their relatives, the patients were significantly more indifferent towards the diagnosis of AD and its importance/impact on their life (p < 0.001). The patients also estimated their own abilities in daily life to be better, compared with how their abilities were assessed by their relatives. Interpretation: This study suggests that early AD patients experience serenity in relation to their situation. There are probably many potential reasons for this outcome. Individuals with AD diagnosis may have lost insight into their situation and abilities. The relatives might also underestimate the AD individual’s abilities. Irrespective of what might explain this outcome, this study highlights how differently an AD diagnosis affects patients and caregivers. Larger studies are needed to confirm these findings and further validate the ADCQ.

The Effect of Personalized Comprehensive Care on the Nursing Care of Severe Pneumonia Patients

Objective: To explore the value of receiving personalized comprehensive care for patients with severe pneumonia. Methods: 73 patients with severe pneumonia who visited the clinic from February 2020 to February 2023 were included in this study. The patients were randomly grouped into Group A and Group B. Group A received personalized comprehensive care whereas Group B received conventional care. The value of care was compared. Results: The duration of mechanical ventilation time, the time taken for fever and dyspnea relief, and the hospitalization time of Group A were shorter than those in Group B (P < 0.05). The blood gas indexes such as PaO2, PaCO2, and blood pH of Group A were better than those of Group B (P < 0.05). The pulmonary function indexes such as peak expiratory flow (PEF), forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV1) of Group A were better than those of Group B, P < 0.05. Moreover, the patients in Group A were generally more satisfied with the care given compared to the patients in Group B (P < 0.05). Conclusion: Personalized comprehensive care improves blood gas indexes, enhances lung function, accelerates the relief of symptoms, and also enhances patient satisfaction in severe pneumonia patients.

Digitalization in orthopaedics: a narrative review.

Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.

Federated clustered multi-domain learning for health monitoring

Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.

A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease

Cardiovascular disease (CVD) remains a leading cause of death globally, presenting significant challenges in early detection and treatment. The complexity of CVD arises from its multifaceted nature, influenced by a combination of genetic, environmental, and lifestyle factors. Traditional diagnostic approaches often struggle to effectively integrate and interpret the heterogeneous data associated with CVD. Addressing this challenge, we introduce a novel Attention-Based Cross-Modal (ABCM) transfer learning framework. This framework innovatively merges diverse data types, including clinical records, medical imagery, and genetic information, through an attention-driven mechanism. This mechanism adeptly identifies and focuses on the most pertinent attributes from each data source, thereby enhancing the model’s ability to discern intricate interrelationships among various data types. Our extensive testing and validation demonstrate that the ABCM framework significantly surpasses traditional single-source models and other advanced multi-source methods in predicting CVD. Specifically, our approach achieves an accuracy of 93.5%, precision of 92.0%, recall of 94.5%, and an impressive area under the curve (AUC) of 97.2%. These results not only underscore the superior predictive capability of our model but also highlight its potential in offering more accurate and early detection of CVD. The integration of cross-modal data through attention-based mechanisms provides a deeper understanding of the disease, paving the way for more informed clinical decision-making and personalized patient care.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Immunotherapy in hepatocellular carcinoma: an overview of immune checkpoint inhibitors, drug resistance, and adverse effects

Abstract Hepatocellular carcinoma (HCC) is a concerning liver cancer with rising incidence and mortality rates worldwide. The effectiveness of traditional therapies in managing advanced HCC is limited, necessitating the development of new therapeutic strategies. Immune checkpoint inhibitors (ICIs) have emerged as a promising strategy for HCC management. By preventing tumor cells from evading immune surveillance through immunological checkpoints, ICIs can restore the immune system’s ability to target and eliminate tumors. While ICIs show promise in enhancing the immune response against malignancies, challenges such as drug resistance and adverse reactions hinder their efficacy. To address these challenges, developing individualized ICI treatment strategies is critical. Combining targeted therapy and immunotherapy holds the potential for comprehensive therapeutic effects. Additionally, biomarker-based individualized ICI treatment strategies offer promise in predicting treatment response and guiding personalized patient care. Future research should explore emerging ICI treatment methods to optimize HCC immunotherapy. This review provides an overview of ICIs as a new treatment for HCC, demonstrating some success in promoting the tumor immune response. However, drug resistance and adverse reactions remain important considerations that must be addressed. As tailored treatment plans evolve, the prospect of immunotherapy for HCC is expected to grow, offering new opportunities for improved patient outcomes.

Prognostic Value of the Noble and Underwood Score in Patients with Non-Small Cell Lung Cancer Undergoing Surgical Resection.

Background: This retrospective study aimed to evaluate the clinical utility of the Noble and Underwood (NUn) score as a prognostic marker for overall survival (OS) in patients with stage I to IIIA non-small cell lung cancer (NSCLC). The NUn score is a novel composite marker that integrates C-reactive protein (CRP), serum albumin (ALB) levels, and white blood cell (WBC) count to provide a comprehensive assessment of systemic inflammation and nutritional status. Methods: We included patients with stage I to IIIA NSCLC and assessed the NUn score, calculated using CRP, ALB levels, and WBC count. Hazard ratios for OS were determined using Cox regression analysis. The predictive performance of the models was evaluated through metrics such as area under the curve (AUC), concordance index (C-index), integrated AUC (iAUC), integrated discrimination improvement (IDI), continuous net reclassification index (cNRI), and decision curve analysis (DCA). Results: The median age of the patients was 69 years, and 63.1% of patients were men. The cohort included 152 (63.1%) patients with stage I disease, 54 (22.4%) with stage II disease, and 35 (14.5%) with stage IIIA disease. In the multivariate Cox regression analysis, the NUn score, age, American Society of Anesthesiologists Physical Status, tumor-node-metastasis (TNM) stage, and pleural invasion emerged as independent prognostic factors for OS, forming the NUn model. The C-index and iAUC of the NUn model (0.832 and 0.802, respectively) outperformed those of the baseline model based solely on TNM stage. The NUn model also demonstrated superior discriminative capacity compared with the baseline model using metrics such as AUC, IDI, cNRI, and DCA at 3 and 5 years after surgery. Calibration of the nomogram based on the NUn model showed good accuracy. Conclusions: These findings underscore the prognostic significance of the NUn score in predicting OS among patients with stage I to IIIA NSCLC by integrating markers of inflammation and nutritional status. The NUn model, which integrates the NUn score with other clinical variables, exhibited superior discriminative ability compared with TNM stage alone. These findings highlight the potential of the NUn score as a valuable tool in personalized care for patients with NSCLC. Further external validation with independent cohorts is necessary to confirm the model's applicability to other populations.

AI driven ECG arrhythmia diagnosis

The accurate and timely diagnosis of cardiac arrhythmias is crucial for effective patient management and improved health outcomes. However, the precise identification of arrhythmias in electrocardiogram (ECG) data often requires specialized medical expertise, leading to potential delays and errors in diagnosis. To address these challenges, this project introduces an AI-driven system for ECG arrhythmia diagnosis. Employing advanced deep learning techniques, the proposed system leverages a comprehensive dataset of annotated ECG recordings to train a robust model capable of detecting and classifying various types of arrhythmias. The model is designed to process raw ECG signals, extract relevant features, and generate clinically meaningful insights, enabling automated and rapid identification of arrhythmic patterns. Through a user-friendly interface, medical professionals can upload ECG data for real-time analysis, allowing for prompt decision-making and personalized patient care. Furthermore, the system offers interpretable results, highlighting key indicators and providing detailed explanations to aid clinicians in understanding the diagnostic outcomes.