搜索结果：Chronic illness

This paper presents the results of a study on the perception of illness and adaptation parameters in patients with type 2 diabetes. The study involved 173 patients diagnosed with "Type 2 Diabetes" (ICD-11 code 5 A 11). The average age of the patients was 55.21+/-13.47 and the average duration of the disease was 11.79+/-8.16. Two profiles of illness perception were identified: Profile 1 - "Perception of illness threat" and Profile 2 - "Perception of illness and treatment controllability". Three types of illness perception were also identified: Type 1 - "Formed illness threat and negative beliefs about illness and treatment control" (Group 1); Type 2 - "Unformed illness threat and neutral beliefs about illness and treatment control" (Group 2); Type 3 - "Formed illness threat and positive beliefs about illness and treatment control" (Group 3). Targets for further psychological interventions were formulated for each identified type.

We use the illness-death model (IDM) for chronic conditions to derive a new analytical relation between the transition rates between the states of the IDM. The transition rates are the incidence rate (i) and the mortality rates of people without disease (m0) and with disease (m1). For the most generic case, the rates depend on age, calendar time and in case of m1 also on the duration of the disease. In this work, we show that the prevalence-odds can be expressed as a convolution-like product of the incidence rate and an exponentiated linear combination of i, m0 and m1. The analytical expression can be used as the basis for a maximum likelihood estimation (MLE) and associated large sample asymptotics. In a simulation study where a cross-sectional trial about a chronic condition is mimicked, we estimate the duration dependency of the mortality rate m1 based on aggregated current status data using the ML estimator. For this, the number of study participants and the number of diseased people in eleven age groups are considered. The ML estimator provides reasonable estimates for the parameters including their large sample confidence bounds.

Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.

This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.

Background: Hemiparesis after subcortical stroke is classically described as distal upper-extremity (UE) predominant, but prevalence data in chronic stroke is limited. Objective: Determine the prevalence of distal predominant UE weakness in exclusively subcortical chronic stroke versus other stroke distributions, characterize cohort differences, and describe UE weakness patterns in chronic stroke overall. Methods: Outpatient records were retrospectively reviewed to identify chronic stroke subjects. Lesion locations were classified from radiographic reports as exclusively subcortical or not (using a whole brain and supratentorial definition). UE weakness was categorized as distal predominant or not. Prevalence was compared with $χ$-squared testing and odds ratios (OR). Results: 250 subjects were included (mean 861 days post-stroke). Using the whole-brain definition, distal predominant weakness occurred in 30.6% of exclusively subcortical versus 17.4% of non-exclusively subcortical strokes (OR 2.09, 95% CI 1.15-3.81; p=0.014). Using the supratentorial definition, distal predominant weakness occurred in 27.9% versus 17.9%, respectively (OR 2.16, 95% CI 1.17-3.96; p=0.012). Across all

Wearable sensor technologies and deep learning are transforming healthcare management. Yet, most health sensing studies focus narrowly on physical chronic diseases. This overlooks the critical need for joint assessment of comorbid physical chronic diseases and depression, which is essential for collaborative chronic care. We conceptualize multi-disease assessment, including both physical diseases and depression, as a multi-task learning (MTL) problem, where each disease assessment is modeled as a task. This joint formulation leverages inter-disease relationships to improve accuracy, but it also introduces the challenge of double heterogeneity: chronic diseases differ in their manifestation (disease heterogeneity), and patients with the same disease show varied patterns (patient heterogeneity). To address these issues, we first adopt existing techniques and propose a base method. Given the limitations of the base method, we further propose an Advanced Double Heterogeneity-based Multi-Task Learning (ADH-MTL) method that improves the base method through three innovations: (1) group-level modeling to support new patient predictions, (2) a decomposition strategy to reduce model complexi

Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior perfor

This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI models that rely on features from patients' labs--our approach focuses on routinely available data, such as medical history, vitals, diagnoses, and medications, to preemptively assess the risks of chronic diseases in the next year. We trained three distinct models for each chronic disease: prediction models that forecast the risk of a disease 3, 6, and 12 months before a potential diagnosis. We developed Random Forest models, which were internally validated using F1 scores and AUROC as performance metrics and further evaluated by a panel of expert physicians for clinical relevance based on inferences grounded in medical knowledge. Additionally, we discuss our implementation of integrating these models into a practical EMR system. Beyond using Shapley attributes and surrogate models for explainability, we also introduce a new rule-engineering framework to enhance the i

Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to form a comprehensive view of a patient's health, which is crucial for informed therapeutic decision-making. Yet, most predictive models fail to fully capture the interactions, redundancies, and temporal patterns across multiple data modalities, often focusing on a single data type or overlooking these complexities. In this paper, we present CURENet, a multimodal model (Combining Unified Representations for Efficient chronic disease prediction) that integrates unstructured clinical notes, lab tests, and patients' time-series data by utilizing large language models (LLMs) for clinical text processing and textual lab tests, as well as transformer encoders for longitudinal sequential visits. CURENet has been capable of capturing the intricate interaction between different forms of clinical data and creating a more reliable predictive model for chronic illnesses. We evaluated CURENet using the public MIMIC-III and private FEMH datasets, where it achi

Chronic stress was implicated in cancer occurrence, but a direct causal connection has not been consistently established. Machine learning and causal modeling offer opportunities to explore complex causal interactions between psychological chronic stress and cancer occurrences. We developed predictive models employing variables from stress indicators, cancer history, and demographic data from self-reported surveys, unveiling the direct and immune suppression mitigated connection between chronic stress and cancer occurrence. The models were corroborated by traditional statistical methods. Our findings indicated significant causal correlations between stress frequency, stress level and perceived health impact, and cancer incidence. Although stress alone showed limited predictive power, integrating socio-demographic and familial cancer history data significantly enhanced model accuracy. These results highlight the multidimensional nature of cancer risk, with stress emerging as a notable factor alongside genetic predisposition. These findings strengthen the case for addressing chronic stress as a modifiable cancer risk factor, supporting its integration into personalized prevention str

Chronic pain is a significant global health issue, with many patients experiencing persistent pain despite no identifiable organic cause, classified as nociplastic pain. Increasing evidence highlights the role of danger signal processing in the maintenance of chronic pain. In response, we developed Personal Danger Signals Reprocessing (PDSR), an online, group-based intervention designed to modify these mechanisms using coaching techniques to enhance accessibility and affordability. This study evaluated the efficacy of PDSR in reducing pain and mental health comorbidities. A cohort of women (N=19, mean age 43) participated in an 8-week online program, receiving weekly sessions on chronic pain mechanisms within a systemic framework. Outcomes were assessed at three time points: pre-intervention, mid-intervention, and post-intervention. A waiting list group (N=20, mean age 43.5) completed assessments at the same intervals. Participants in the PDSR group showed significant pain reduction (p < .001), with moderate to large effects observed at mid-intervention (Cohen's D = 0.7) and post-intervention (Cohen's D = 1.5) compared to controls. Pain interference significantly decreased (p &l

In recent years, the intersection of Natural Language Processing (NLP) and public health has opened innovative pathways for investigating various domains, including chronic pain in textual datasets. Despite the promise of NLP in chronic pain, the literature is dispersed across various disciplines, and there is a need to consolidate existing knowledge, identify knowledge gaps in the literature, and inform future research directions in this emerging field. This review aims to investigate the state of the research on NLP-based interventions designed for chronic pain research. A search strategy was formulated and executed across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology to find studies published in English between 2014 and 2024. After screening 132 papers, 26 studies were included in the final review. Key findings from this review underscore the significant potential of NLP techniques to address pressing challenges in chronic pain research. The past 10 years in this field have showcased the utilization of advanced methods (transformers like RoBERTa and BERT) achieving high-performance metrics (e.g., F1>0.8) in classification tasks, while unsupervised approaches

A teenager's experience of chronic pain reverberates through multiple interacting aspects of their lives. To self-manage their symptoms, they need to understand how factors such as their sleep, social interactions, emotions and pain intersect; supporting this capability must underlie an effective personalized healthcare solution. While adult use of personal informatics for self-management of various health factors has been studied, solutions intended for adults are rarely workable for teens, who face this complex and confusing situation with unique perspectives, skills and contexts. In this design study, we explore a means of facilitating self-reflection by youth living with chronic pain, through visualization of their personal health data. In collaboration with pediatric chronic pain clinicians and a health-tech industry partner, we designed and deployed MyWeekInSight, a visualization-based self-reflection tool for youth with chronic pain. We discuss our staged design approach with this intersectionally vulnerable population, in which we balanced reliance on proxy users and data with feedback from youth viewing their own data. We report on extensive formative and in-situ evaluatio

This study presents a mathematical model describing cloned hematopoiesis in chronic myeloid leukemia (CML) through a nonlinear system of differential equations. The primary objective is to understand the progression from healthy hematopoiesis to the chronic and accelerated-acute phases in myeloid leukemia. The model incorporates intrinsic cellular division events in hematopoiesis and delineates the evolution of chronic myeloid leukemia into five compartments: cycling stem cells, quiescent stem cells, progenitor cells, differentiated cells and terminally differentiated cells. Our analysis reveals the existence of three distinct non-zero steady states within the dynamical system, representing healthy hematopoiesis, the chronic phase and the accelerated-acute stage of the disease. We investigate the local and global stability of these steady states and provide a characterization of the hematopoietic states based on this analysis. Additionally, numerical simulations are included to illustrate the theoretical results.

Clinical data informs the personalization of health care with a potential for more effective disease management. In practice, this is achieved by subgrouping, whereby clusters with similar patient characteristics are identified and then receive customized treatment plans with the goal of targeting subgroup-specific disease dynamics. In this paper, we propose a novel mixture hidden Markov model for subgrouping patient trajectories from chronic diseases. Our model is probabilistic and carefully designed to capture different trajectory phases of chronic diseases (i.e., "severe", "moderate", and "mild") through tailored latent states. We demonstrate our subgrouping framework based on a longitudinal study across 847 patients with non-specific low back pain. Here, our subgrouping framework identifies 8 subgroups. Further, we show that our subgrouping framework outperforms common baselines in terms of cluster validity indices. Finally, we discuss the applicability of the model to other chronic and long-lasting diseases.

The best way to treat chronic hepatitis B is with pegylated interferon alone or with oral antiviral drugs. There is limited research comparing the renal safety of entecavir and tenofovir when used with pegylated interferon. This study will compare changes in renal function in chronic hepatitis B patients treated with pegylated interferon and either entecavir or tenofovir. The study included a cohort of 836 patients with chronic hepatitis B (CHB) who received treatment with pegylated interferon (IFN) either alone or in combination with entecavir (ETV) and tenofovir (TDF) between the years 2018 and 2021. Of these patients, 713 were included in a matched analysis comparing outcomes between those who were cured and those who were uncured, while 123 patients received IFN alone as a control group for comparison with the ETV and TDF treatment groups. The primary outcome measured was the change in renal function, specifically estimated glomerular filtration rate (eGFR), cystatin C (CysC), and inorganic phosphorus (IPHOS). Patients were categorized into stage 1 or stage 2 based on a baseline eGFR of less than 90 ml/min/m^2 Results: 125 CHB patients were matched 1:1 in both the combined trea

Recently, we have shown that the age-specific prevalence of a disease can be related to the transition rates in the illness-death model via a partial differential equation (PDE). In case of a chronic disease, we show that the PDE can be used to estimate excess mortality from prevalence and incidence. Applicability of the new method is demonstrated in a simulation and claims data about diabetes in German men.

The three state illness death model has been established as a general approach for regression analysis of semi competing risks data. For observational data the marginal structural models (MSM) are a useful tool, under the potential outcomes framework to define and estimate parameters with causal interpretations. In this paper we introduce a class of marginal structural illness death models for the analysis of observational semi competing risks data. We consider two specific such models, the Markov illness death MSM and the frailty based Markov illness death MSM. For interpretation purposes, risk contrasts under the MSMs are defined. Inference under the illness death MSM can be carried out using estimating equations with inverse probability weighting, while inference under the frailty based illness death MSM requires a weighted EM algorithm. We study the inference procedures under both MSMs using extensive simulations, and apply them to the analysis of mid life alcohol exposure on late life cognitive impairment as well as mortality using the Honolulu Asia Aging Study data set. The R codes developed in this work have been implemented in the R package semicmprskcoxmsm that is publicly