Developing disease risk prediction model based on environmental factors

To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.

With the advent of the era of big data, machine learning theory is more and more widely used in disease prediction. At the same time, the use of disease risk and cost prediction model (DRCPM) (See Chapter 2 for details) to predict the chronic disease prevalence rates and medical expenses of patients has been recognized in the industry. In this paper, on the basis of DRCPM, In order to improve the prediction accuracy of the model. GA algorithm is used to optimize the super parameters and prediction weights of XGBoost and RF, and to construct GA_XGBoost_RF prediction model. Through experiments, the average prediction accuracy is 99.74% and the prediction variance is 1.7% on 1 million data sets, which is an extremely effective prediction method.

Development of a coronary heart disease risk prediction model for type 1 diabetes: The Pittsburgh CHD in Type 1 Diabetes Risk Model

OBJECTIVESProper risk assessment is important for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, no validated risk prediction tools are currently in use in Korea. This study sought to develop a 10-year risk prediction model for incident ASCVD.METHODSUsing the National Sample Cohort of Korea, 325,934 subjects aged 20-80 years without previous ASCVD were enrolled. ASCVD was defined as a composite of cardiovascular death, myocardial infarction, and stroke. The Korean atherosclerotic cardiovas cular disease risk prediction (K-CVD) model was developed separately for men and women using the development dataset and validated in the validation dataset. Furthermore, the model performance was compared with the Framingham risk score (FRS) and pooled cohort equation (PCE).RESULTSOver 10 years of follow-up, 4,367 ASCVD events occurred in the overall population. The predictors of ASCVD included in the model were age, smoking status, diabetes, systolic blood pressure, lipid profiles, urine protein, and lipid-lowering and blood pressure-lowering treatment. The K-CVD model had good discrimination and strong calibration in the validation dataset (time-dependent area under the curve=0.846; 95% confidence interval, 0.828 to 0.864; calibration χ2=4.73, goodness-of-fit p=0.32). Compared with our model, both FRS and PCE showed worse calibration, overestimating ASCVD risk in the Korean population.CONCLUSIONSThrough a nationwide cohort, we developed a model for 10-year ASCVD risk prediction in a contemporary Korean population. The K-CVD model showed excellent discrimination and calibration in Koreans. This population-based risk prediction tool would help to appropriately identify high-risk individuals and provide preventive interventions in the Korean population.

Risk prediction model estimates event occurrence based on related data. Conventional statistical metrics that utilized primary data generates simple descriptive analysis that often provide insufficient knowledge for decision making. In contrast, data mining techniques that have the capability to find hidden pattern from the secondary data in large databases and create prediction for de- sired output has become a popular approach to develop any risk prediction model. In healthcare particularly, data mining techniques can be applied in disease risk prediction model to provide reliable prediction on the possibility of acquiring the disease based on individual’s clinical and non-clinical data. Due to the increased use of data mining in healthcare, this study aims at identifying the data mining techniques and algorithms that are commonly implemented in studies related to various disease risk prediction model as well as finding the accuracy of the algorithms. The accuracy evaluation consists of various method, but this paper is focusing on overall accuracy which is measured by the total number of correctly predicted output over the total number of prediction. A systematic literature review approach that search across five databases found 170 articles, of which 7 articles were selected in the final process. This review found that most prediction model used classification technique, with a focus on decision tree, neural network, support vector machines, and Naive Bayes algorithms where heart-related disease is commonly studied. Further research can apply similar algorithms to develop risk prediction model for other types of diseases, such as infectious disease prediction.

Table grape production and quality is highly influenced by environmental conditions, with abrupt weather changes increasing the risk of vineyard diseases. The integration of smart agriculture technologies enables early detection and effective management of these threats, improving crop quality and yield. This work proposes the GraDA system, a data collection and grape disease risk assessment system, consisting of three main components, namely the GraDA platform, a mobile application and an AI-driven grape disease risk prediction model. The mobile application is responsible for collecting images of grapes and leaves, while the AI-driven disease risk prediction model processes multi-modal input data—grape images, leaf images, and meteorological data—to achieve enhanced grape disease risk prediction accuracy. Finally, the GraDA platform is concerned with the collection and visualization of imaging and meteorological data through a web-based interface, enabling agronomists and farmers to monitor in real-time the weather conditions and the estimated disease risk scores, as well as manage farming activities, thus facilitating data-driven decision-making for vineyard health optimization. The proposed AI model’s accuracy was evaluated in a multi-modal dataset, achieving a performance above 93% in grape disease risk estimation, while the usability of the overall GraDA system was assessed through questionnaires distributed to agronomists and farmers, leading to a score of 79.83% in the SUS scale, indicating excellent usability and user satisfaction from the proposed system. Finally, a pilot study of the GraDA system was performed to assess the applicability of the system in real-life scenarios.

A population-based recalibration method for updating survival neural networks models for cardiovascular risk prediction in United Kingdom and China.

Background/Introduction Accurate cardiovascular disease (CVD) risk prediction models can facilitate the identification and cost-effective treatment of high-risk individuals. Discrimination and calibration are the most commonly recommended metrics for evaluating model performance. However, both are population-level measures, and it remains unclear whether these metrics are sufficiently sensitive to assess the performance of models designed for use with individual patients. Purpose Using a real-world dataset, we aimed to compare CVD risk estimates for example individuals (individual-level measures) and discrimination and calibration metrics (population-level measures) from two CVD risk prediction models; one with "age" as the only predictor and one with multiple predictors. Methods We constructed a cohort of almost all New Zealanders without CVD or heart failure, alive and aged 30–79 years in 2014, with follow-up linkage to hospitalisations and mortality until 2018 (N = 2,098,359). We derived two sets of sex-specific Cox regression models to predict 5-year CVD risk. The age-only models included "age" as the only predictor, and the full models used nine pre-defined routinely available CVD risk predictors with interaction terms. We calculated and compared individuals’ 5-year CVD risk from the age-only and full models and assessed model performance using Harrell’s C-statistics and calibration plots. Results Risk estimates for individuals using the two models showed substantial differences. A 65-year-old woman had predicted 5-year risks of 4.9% with the age-only model but 16.1% with the full model; a 74-year-old man had predicted risks of 15.2% and 9.2%, respectively, with age-only and full models (Figure 1). However, the models had similar discrimination and calibration (Figure 2). Age-only models had good discrimination with C-statistics of 0.773 (95% CI: 0.771–0.775) in women and 0.740 (0.738–0.742) in men, and calibrated well in both sexes. The full models had slightly better discrimination (C-statistics: 0.814 [0.812–0.816] in women and 0.769 [0.767–0.771] in men), and very similar calibration to the age-only models in both sexes. Conclusions Age-only models showed good discrimination and were well calibrated, but predicted very different risks in individual case studies compared to models with multiple predictors. This study calls into question the use of discrimination and calibration metrics as the main measures for comparing the performance of different CVD risk prediction models. We recommend that examples of predicted risk for individuals and an assessment of the magnitude and prevalence of cardiovascular predictors should also be considered when comparing the performance of prognostic models.

Introduction High-density lipoprotein cholesterol (HDL-C) has traditionally been included in atherosclerotic cardiovascular disease (ASCVD) risk prediction models, such as the pooled cohort equations. However, growing evidence suggests that lipoprotein particle concentration may serve as a more reliable predictor of cardiovascular outcomes than cholesterol content alone. Studies, including the MESA trial, indicate that HDL particle concentration (HDL-P) is a stronger determinant of cardiovascular risk than HDL-C. Larger HDL particles may be dysfunctional, leading to misleading HDL-C levels that do not accurately reflect cardiovascular protection. Adjusting HDL-C for HDL-P may improve risk stratification. We hypothesize that incorporating HDL-P into risk prediction models will refine cardiovascular risk assessment. Purpose This study evaluates the limitations of HDL-C in risk prediction and assesses whether particle-adjusted values provide a more accurate measure of cardiovascular risk when integrated into standard risk calculations. Methods A total of 67 lipid profiles from Scripps Health were analyzed, including traditional lipid concentrations, HDL and LDL particle concentrations, and age at the time of lipid profile collection. Cardiovascular risk was calculated using unadjusted baseline lipid values within the PREVENT risk prediction calculator. To isolate the impact of lipid parameter adjustments on risk estimation, risk factors such as diabetes, smoking, eGFR, SBP, and BMI were held constant across all calculations. The calculations were then repeated using adjusted HDL-C and Total Cholesterol values, accounting for LDL and HDL particle concentrations. Adjusted values were derived using statistical regression models based on real-world data from the Framingham Offspring Study and pooled cohort analyses (DHS, ARIC, MESA, PREVEND). Results After adjustment, 47 patients (70.1%) demonstrated an increase in predicted cardiovascular risk, with an average absolute risk increase of 0.834% and an average relative change of 15.31%. Conversely, 20 patients (29.9%) exhibited a decrease in predicted risk, with an average absolute risk decrease of 0.2785% and an average relative change of 6.72%. The total combined relative risk change across both groups was 22.03%, suggesting that incorporating HDL-P and LDL-P meaningfully alters risk assessment. Conclusions Adjusting for particle concentration significantly influences cardiovascular risk prediction, with the majority of patients experiencing an increase in estimated risk. This supports the hypothesis that HDL-P, rather than HDL-C, provides a more precise assessment of cardiovascular risk. Current risk models relying solely on HDL-C and total cholesterol may misclassify risk, underscoring the need to incorporate particle-adjusted values into predictive algorithms for more accurate cardiovascular risk stratification.

Objective: To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology. Methods: This was a retrospectively study. Newborn screening data (n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data (n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results: A total of 3 665 697 newborns' screening data were collected including 3 019 cases' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment (n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion: An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction research, many datasets are collected by professional medical equipment. In addition, the currently researched models cannot be run directly on mobile terminals. In order to predict the health risks of the elderly without relying on professional medical testing equipment in the application scenarios of nursing homes, we use the datasets collected by non-professional medical testing equipment. Based on transfer learning and lightweight neural networks, we propose a disease risk prediction model, Diplin (disease risk prediction model based on lightweight neural network), applied to nursing homes. This model achieved 98% accuracy, 97% precision, 96% recall, 95% specificity, 97% F1 score, and 1.0 AUC (area under ROC curve) value on the validation set. The experimental results show that in the application scenario of nursing homes, the Diplin model can provide practical support for predicting the health risks of the elderly, and this model can be run directly on the tablet.

ObjectivesSarcopenia is a known risk factor for non-alcoholic fatty liver disease (NAFLD). Studies evaluating the association between the fat-to-muscle ratio (FMR) and NAFLD are limited. Therefore, the aim of our...

BackgroundAsthma is a chronic disease that exacerbates due to various risk factors, including the patient’s biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model’s performance, and determine the need to improve research in this field.MethodA systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article’s quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review.ResultFrom 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field.ConclusionAsthma attack predictive models become more significant when using both patient’s biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

Recent research has shown that environmental and lifestyle factors play a significant role in the development of cancer, and can include carcinogenic agents in soil, water, air, and food. Modifiable factors such as these are imperative to understand and address to optimize cancer prevention. This article is a review focusing on the impact of environmental exposures and lifestyle behaviors on cancer in women and aiming to identify the role of risk factors in gynecologic cancer risk and progression. Environmental and lifestyle factors play a role in many gynecologic cancers. Ovarian cancer has one of the highest mortality rates of any gynecologic cancer and is influenced by factors including high body mass index (BMI) and dietary habits such as a lack of fruits and vegetables. Less modifiable risk factors include multiparity and long-term use of contraceptives, which can lower ovarian cancer risk, and air pollution that increases the risk. Uterine cancer is one of the most prevalent gynecologic cancers in high-income countries, and is associated with obesity, physical inactivity, and diets high in animal fat and low in fiber. Pesticides have also been implicated due to an association with hormonal disruption and systemic inflammation. Cervical cancer, on the other hand, is primarily related to sexual behaviors, as it is directly associated with HPV infection. Other risk factors include tobacco use, nutritional deficiencies such as folate and antioxidants, and an imbalance of the vaginal microbiome. Less common cancers, such as vulvar and vaginal cancers, are also strongly influenced by lifestyle and environmental factors, such as obesity, smoking, and exposure to carcinogens. Genetic and environmental factors interact in a nuanced way in the development of gynecologic cancer, and some risk factors are modifiable while some are not. Certain genetic mutations have been associated with an increased risk of several types of cancers, and this is outside of an individual’s control; however, the risk of gynecologic cancer can be reduced via a healthy diet and activity level and can mitigate some of the increased genetic risk. Polygenic risk scores (PRSs) have been developed as a tool to estimate individual risk of certain diseases, including gynecologic cancers, and calculate a score based on genetic factors that are present and their association with each disease based on genome-wide association studies (GWAS). Genetic factors with the highest relevance for gynecologic cancers include BRCA mutation carriers, aberrant DNA methylation, abnormal histone acetylation and methylation, and dysregulation of noncoding RNAs such as microRNAs. Evidence has shown that modifying risk factors even after diagnosis of gynecologic cancer can improve long-term outcomes; these can include smoking cessation, increased physical activity, diet, and body weight. These factors can improve both cancer survival and quality of life. Interplay between genetic and environmental factors is nuanced but imperative for understanding individualized gynecologic cancer risk. Some nuances of modifiable and nonmodifiable factors include cultural context and sociodemographic factors that can influence the ability of individuals to adhere to health recommendations, such as physical activity and dietary habits, as well as influence their access to health care and education. These disparities must be acknowledged when determining individual cancer risk. Future studies and interventions should include education and information dissemination, public health initiatives, regular cancer screenings, and supporting cancer survivors. (Summarized from Farina S, Sabatelli A, Boccia S, et al. Environment, lifestyle, and cancer in women. Int J Gynaecol Obstet. 2025;171 Suppl 1(Suppl 1):138-146. doi: https://doi.org/10.1002/ijgo.70156)

Beyond Gene Discovery in Inflammatory Bowel Disease: The Emerging Role of Epigenetics