Early Risk Prediction Research Articles

Machine learning-based prediction model for sports injury risk in biomechanics: A case study of joint injuries in basketball at a university in Xi’an

Basketball players are prone to joint injuries due to the sport’s high intensity and physical demands. Early prediction of injury risk is crucial for implementing effective prevention strategies. Incorporating biomechanics, this study focuses on basketball players at a university in Xi’an, China, aiming to develop a machine learning-based model to predict joint injury risk using easily collectable data such as training load, fatigue levels, and previous injury history. Considering regional differences, we observed that local and northern Chinese students are generally taller, while students from southern China are typically shorter. This anthropometric variation was included in our sampling and analysis. Utilizing data from 100 basketball players, the Random Forest algorithm achieved the best predictive performance with an accuracy of 85%. Key risk factors identified include high training load, elevated subjective fatigue scores, and a history of previous joint injuries. Additionally, biomechanical data were integrated to elucidate the underlying mechanisms of joint injuries, and the cellular responses to injury were explored. The results demonstrate that even with limited data types, machine learning methods can effectively predict joint injury risk among basketball players, providing a valuable tool for injury prevention.

Wearable Aptasensors.

This Perspective explores the revolutionary advances in wearable aptasensor (WA) technology, which combines wearable devices and aptamer-based detection systems for personalized, real-time health monitoring. The devices leverage the specificity and sensitivity of aptamers to target specific molecules, offering broad applications from continuous glucose tracking to early diagnosis of diseases. The integration of data analytics and artificial intelligence (AI) allows early risk prediction and guides preventive health measures. While challenges in miniaturization, power efficiency, and data security persist, these devices hold significant potential to democratize healthcare and reshape patient-doctor interactions.

Development and validation of Galectin-3 and CVAI-based model for predicting cognitive impairment in type 2 diabetes.

The objective of this study is to develop a predictive model combining multiple indicators to quantify the risk of mild cognitive impairment (MCI) in T2DM patients. This study included Chinese T2DM patients who were hospitalized at Zhongda Hospital between November 2021 and May 2023. Clinical data, including demographics, medical history, biochemical tests, and cognitive status, were collected. Cognitive assessment was performed using neuropsychological tests, and MCI was diagnosed based on the Montreal Cognitive Assessment (MoCA) scores. The dataset was randomly divided into a training set and a validation set in a 7:3 ratio. Logistic regression analysis was conducted to identify factors influencing MCI in the training set. A nomogram-based scoring model was then developed by integrating these findings with high-risk clinical variables, and its performance was validated in the validation set. In this study, T2DM patients were divided into a training set and a validation set in a 7:3 ratio. There were no significant differences in MCI incidence, demographics, or clinical characteristics between the two groups, confirming the appropriateness of model construction. In the training set, Galectin-3 and CVAI were significantly negatively correlated with cognitive function (MoCA and MMSE scores), and this negative correlation remained after adjusting for confounding variables. Logistic regression analysis revealed that age, CVAI, and Galectin-3 significantly increased the risk of MCI, while years of education had a protective effect. The constructed nomogram model, which integrated age, sex, education level, hypertension, CVAI, and Galectin-3 levels, exhibited high predictive performance (C-index of 0.816), with AUCs of 0.816 in the training set and 0.858 in the validation set, outperforming single indicators. PR curve analysis further validated the superiority of the nomogram model. The straightforward, highly accurate, and interactive nomogram model developed in this study facilitate the early risk prediction of MCI in individuals with T2DM by incorporating Galectin-3, CVAI, and other common clinical risk factors.

Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.

Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated. This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing. The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants' experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform. Word count analysis revealed a generally positive sentiment toward the digital platform, with "like," "well," and "good" being the most frequently mentioned positive terms. However, terms such as "problem" and "hard" indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from -1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort. The study highlights the importance of understanding older adults' attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.

Machine learning-based prediction of one-year mortality in ischemic stroke patients.

Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke. Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model's outcomes and defined the influential predictive factors. Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation. This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.

Constructing a predictive model for early-onset sepsis in neonatal intensive care unit newborns based on SHapley Additive exPlanations explainable machine learning.

The clinical characteristics of neonatal sepsis (NS) are subtle and non-specific, posing a serious threat to the lives of newborn infants. Early-onset sepsis (EOS) is sepsis that occurs within 72 hours after birth, with a high mortality rate. Identifying key factors of NS and conducting early diagnosis are of great practical significance. Thus, we developed a robust machine learning (ML) model for the early prediction of EOS in neonates admitted to the neonatal intensive care unit (NICU), investigated the pivotal risk factors associated with EOS development, and provided interpretable insights into the model's predictions. A retrospective cohort study was conducted. This includes 668 newborns (EOS and non-EOS) admitted to the NICU of Bozhou People's Hospital from January to December 2023, excluding 72 newborns born more than three days ago and 166 newborns with medical record data missing more than 30%. Finally, 430 newborns (EOS and non-EOS) were included in the study. Clinical case data were meticulously analyzed, and the dataset was randomly partitioned, allocating 75% for model training and the remaining 25% for test. Data preprocessing was meticulously performed using R language, and the least absolute shrinkage and selection operator (LASSO) regression was implemented to select salient features, mitigating the risk of overfitting. Six ML models were leveraged to forecast the incidence of EOS in neonates. The predictive performance of these models was rigorously evaluated using the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Furthermore, the SHapley Additive exPlanations (SHAP) framework was employed to provide intuitive explanations for the predictions made by the Categorical Boosting (CatBoost) model, which emerged as the top performer. The ROC area under the curve (ROCAUC) of six ML models, CatBoost, random forest (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR) all exceeded 0.900 on the test set. Especially the CatBoost model exhibited superior performance, with favorable outcomes in calibration, decision curve analysis (DCA), and learning curves. Notably, the ROCAUC attained 0.975, and the area under the PR curve (PRAUC) reached 0.947, signifying a high degree of predictive accuracy. Utilizing the SHAP method, seven key features were identified and ranked by their importance: respiratory rate (RR), procalcitonin (PCT), nasal congestion (NC), yellow staining (YS), white blood cell count (WBC), fever, and amniotic fluid turbidity (AFT). By constructing a precision-oriented ML model and harnessing the SHAP method for interpretability, this study effectively identified crucial risk factors for EOS development in neonates. This approach enables early prediction of EOS risk, thereby facilitating timely and targeted clinical interventions for precise diagnosis and treatment.

PREDIKSI RISIKO DEMAM BERDARAH MENGGUNAKAN DECISION TREE BERDASARKAN GEJALA KLINIS DAN DATA LABORATORIUM

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.

Tracing the Lipid Fingerprints of Preeclampsia.

Preeclampsia (PE) is the most common pregnancy-related complication responsible for maternal mortality and morbidity. PE pathogenesis is characterized by placental dysfunction, impaired invasion of trophoblast, and defective spiral artery remodelling. Even after many years of research on PE, the etiology and pathophysiology of PE is still elusive. Our earlier studies have shown deregulated maternal and placental fatty acid and lipid metabolism to be associated with the pathogenesis of PE. Currently available lipidomics data have shown that glycerophospholipids, sphingolipid and cholesterol metabolism are mainly altered in preeclampsia. Including these five metabolites (SM C28:1, SM C30:1, LPC C19:0, LPE C20:0, propane-1,3-diol) with currently used protein biomarkers like sFlt-1/PlGF will improve PE prediction. Similarly, CE17:1 and CER(d20:1/24:1) alongwith sFlt-1/PlGF makes a better prediction of PE than sFlt-1/PlGF alone A comprehensive map of lipid profiles in early pregnancy may provide an improved understanding of disease pathogenesis and will be useful predictive biomarkers. In this article, we aimed to summarize the significance of lipid metabolism in the preeclampsia pathogenesis and altered lipidome signatures in preeclampsia. We also discuss the future scope of lipidomics in aiding early prediction of PE and future cardiovascular risk in both mother and child.

Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

In this study, we aimed to develop and validate a nomogram to predicting the risk of sepsis-associated acute kidney injury (SA-AKI) in patients admitted to emergency departments (EDs). We randomly divided a retrospective dataset of 391 patients with sepsis into a 294-person training cohort and a 97-person validation cohort, and developed three predictive models using multivariate logistic regression analysis and clinical insight. No difference was observed between the three models using the DeLong test and Model 3 was selected as the risk prediction model based on the principle of least inclusion indicators. The use of vasopressor drugs, patient age, platelet count, procalcitonin, and D-dimer levels were included. The training and validation cohorts had a consistency index of 0.832 and 0.866, respectively, indicating high accuracy and stability in predicting SA-AKI risk. The area under the receiver operating characteristic curve was 0.832, showing excellent discrimination. The calibration curves for the training and validation cohorts showed excellent calibration. The decision curve and clinical impact curve analyses showed that the net clinical benefit of using the nomogram was greatest over a probability threshold of 0.05–0.90. In addition, the model showed moderate validity in predicting the 30-day survival and the incidence of major adverse renal events within 30 days. The nomogram developed for SA-AKI risk assessment in patients in EDs showed good discriminability and clinical utility. It can provide a theoretical basis for emergency physicians to prevent SA-AKI.

Interpretable machine learning model for predicting long-term cardiovascular mortality risk in chronic kidney disease patients using SHAP explainers

Abstract Background Patients with chronic kidney disease (CKD) exhibited a pronounced burden of cardiac mortality. However, early prediction of cardiovascular mortality risk in CKD patients remains an unmet medical need. Purpose This study aimed to evaluate the performance of machine learning (ML) algorithms and identify the most influential factors in predicting long-term cardiac mortality among CKD patients. Methods Six ML models were developed, with the most effective one chosen for predicting and categorising patients into high-risk and low-risk groups based on the maximal Youden’s index. Differences in survival rates were assessed using the log-rank test on Kaplan-Meier curves. Cox regression analysis was employed to investigate the relationship between ML-predicted risk scores and mortality. Furthermore, the SHapley Additive exPlanations (SHAP) method was implemented to provide personalised explanations for model decisions. Results The auto-encoder (AE) model achieved the highest AUC of 0.94, with a sensitivity of 80.95% and specificity of 96.34%. Compared to individuals in low-risk group, those in high-risk group exhibited a significantly elevated risk of cardiovascular mortality (adjusted hazard ratio [HR]: 48.24; P <0.001). According to the SHAP model, age, C-reactive protein, blood urea nitrogen and hypertension were identified as the four most influential factors in the AE model. Conclusions Our study successfully developed an AE machine learning model for predicting 10-year cardiovascular mortality among CKD patients. The combination of ML model and SHAP aids doctors in understanding key features in the model, improving their insight into decision-making related to disease severity assessment, thus facilitating early preventive strategies in clinical practice.

Development and validation of a risk prediction model for feeding intolerance in neurocritical patients with enteral nutrition.

This study collects and analyzes clinical data on enteral nutrition therapy in neurocritical patients, develops and validates a feeding intolerance (FI) risk prediction model, and provides a theoretical basis for screening patients with high risk of feeding intolerance (FI) and delivering personalized care. A convenience sampling method was employed to select 300 patients who were admitted to a tertiary hospital in China for early enteral nutrition therapy in the neurointensive care unit between April 2022 and December 2022. Independent risk factors for FI were identified using univariate and logistic regression analyses. A prediction model was established, and the goodness of fit and discriminant validity of the model were evaluated. The incidence of FI in neurocritical patients receiving enteral nutrition was 71%. Logistic regression analysis identified age, Glasgow Coma Scale (GCS) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, mechanical ventilation, feeding via the nasogastric tube route, hyperglycemia, and low serum albumin as independent risk factors for the development of FI (p < 0.05). The predictive formula for FI risk was established as follows: Logit p = -14.737 + 1.184 × mechanical ventilation +2.309 × feeding route +1.650 × age + 1.336 × GCS tertile (6-8 points) + 1.696 × GCS tertile (3-5 points) + 1.753 × APACHE II score + 1.683 × blood glucose value +1.954 × serum albumin concentration. The Hosmer-Lemeshow test showed χ2 = 9.622, p = 0.293, and the area under the ROC curve was 0.941 (95% confidence interval: 0.912-0.970, p < 0.001). The optimal critical value was 0.767, with a sensitivity of 85.9%, a specificity of 90.8%, and a Youden index of 0.715. The early enteral nutrition FI risk prediction model developed in this study demonstrated good predictive ability. This model can serve as a valuable reference for effectively assessing the risk of FI in neurocritical patients, thereby enhancing clinical outcomes.

Predictive Modeling of Preeclampsia Risk Using Random Forest Algorithm within a Machine Learning Framework

Preeclampsia is a serious pregnancy complication characterized by high blood pressure, potentially leading to organ damage, making early risk prediction crucial to reducing maternal morbidity and mortality. This study aims to develop a preeclampsia risk prediction model using medical and clinical data from 80 patients at Rumah Bersalin Sadan. The data include demographic profiles, blood pressure, weight, maternal age, preeclampsia history, body mass index, number of previous pregnancies, as well as genetic and environmental factors. The dependent variable is the risk of preeclampsia, either as a binary outcome (yes/no) or as a continuous risk score. The predictive model was built using multivariate linear regression and the Random Forest algorithm. The results showed that the Random Forest model achieved an accuracy of 65.22%, with an F-statistic of 7.345 and a very small p-value (1.908e-06), indicating that the model effectively explains data variability. However, the low Kappa value suggests room for improvement through feature refinement, hyperparameter tuning, or exploring other algorithms. Although these findings suggest that Random Forest is a promising method, further evaluation and model optimization are needed to enhance predictive performance and determine whether this method is the most suitable for the dataset used.

Predictive model for acute radiation esophagitis in esophageal carcinoma based on prognostic nutritional index and systemic inflammatory index and its application.

Acute radiation esophagitis (ARE) is a common complication in patients with esophageal cancer undergoing radiotherapy. Therefore, it is important to construct an effective ARE risk-prediction model for clinical treatment. The present study performed a retrospective analysis of 225 patients with esophageal cancer who received radiotherapy at the First Affiliated Hospital of Anhui Medical University (Hefei, China) from January 2018 to December 2022. Univariate and logistic regression analyses were performed to screen patients with esophageal cancer after radiotherapy. The results revealed that 147 patients developed radiation esophagitis. Logistic regression analysis results demonstrated that the prognostic nutritional index [odds ratio (OR), 0.864; 95% confidence interval (CI), 0.809-0.924], neutrophil to lymphocyte ratio (OR, 1.795; 95% CI, 1.209-2.667) and platelet to lymphocyte ratio (OR, 1.011; 95% CI, 1.000-1.022) were independent predictors of ARE in patients receiving intensity-modulated conformal radiotherapy for esophagus cancer (P<0.05). A nomogram model for predicting the occurrence of ARE was established based on the three risk factors. The decision curve suggested a high net benefit value when the threshold probability was within 0.25-1.0. External verification confirmed the reproducibility and generalizability of the nomogram model. In general, the calibration curve of this model was close to the ideal curve and had excellent prediction accuracy. Therefore, it may be used as a new tool for early prediction of the ARE risk.

A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning.

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%.

A-015 Changes of sST2 and NT-proBNP levels predict early cardiovascular toxicity in breast cancer patients treated with anthracycline-containing chemotherapy

Abstract Background Cardiovascular biomarkers play a crucial role in monitoring the risk for cancer therapy-related cardiovascular toxicity, but their changes and values at the early stage of chemotherapy are still inadequate, especially for some new biomarkers.The purpose of this study was to examine changes in cardiovascular biomarkers and evaluate the prognostic values for cardiovascular toxicity in patients with breast cancer treated with anthracycline-containing chemotherapy. Methods In a prospective cohort of 73 patients treated with anthracycline-containing chemotherapy, soluble ST2 (sST2), high-sensitivity cardiac troponin T (hs-cTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), Myoglobin (Myo), Creatine kinase isoenzyme MB (CK-MB) and heart-fatty acid binding protein (H-FABP) were measured at baseline, during every chemotherapy cycle (C1-C6, about every month).Cardiovascular toxicity was defined as cancer therapy-related arrhythmias, which was differentiated into bradycardia,tachycardia or atrial fibrillation. According to whether arrhythmia occurred, patients were divided into two groups (healthy group and arrhythmias group), and the basic clinical characteristics were collected and compared among the two groups. Logistic regression analyses and receiver operating characteristic (ROC) curves were conducted to investigate the relationship between the changes in biomarkers and anthracycline-induced cardiotoxicity. Results sST2 levels increased significantly from baseline to C1 (P&amp;lt;0.01). NT-proBNP decreased from baseline to C1, C5 (P&amp;lt; 0.01). Logistic regression analysis showed a greater risk of cardiovascular toxicity was associated with greater interval increase in sST2 from baseline to C1(ΔsST2) (OR:1.27; 95% CI:1.03 to1.56; P= 0.024) and interval decline in NT-proBNP from baseline to C1(ΔNT-proBNP) was associated with cardiovascular toxicity (OR: 0.83; 95% CI: 0.70 to 0.98; P= 0.029). ROC curves showed that ΔsST2, ΔNT-proBNP and ΔsST2+ΔNT-proBNP had good predictive value for cardiovascular toxicity (areas under the curves were 0.631, 0.633, and 0.735, respectively, P&amp;lt;0.05). Conclusions Early changes in sST2 and NT-proBNP levels offer additive information in early cardiovascular toxicity risk prediction in patients with breast cancer who receive anthracycline-containing chemotherapy.

DEVELOPING MACHINE LEARNING APPLICATION FOR EARLY CARDIOVASCULAR DISEASE (CVD) RISK DETECTION IN FIJI: A DESIGN SCIENCE APPROACH

CVD (cardiovascular disease) has become a significant contributor to premature deaths for many years in Fiji. CVD's late detection also significantly impacts annual deaths and casualties. Currently, Fiji lacks diagnosis tools that would enable people to know their risk levels. In this paper, a machine learning mobile application was developed that can be easily accessible to the local population for early prediction of CVD risk. The design science approach was used to guide the development of the application. The design process involved identifying the problem and motivation, setting objectives, creating a machine-learning mobile application for medical record analysis, demonstrating the application to selected participants, evaluating its usability and the machine-learning model's performance, and communicating the findings. The results revealed that the application proposed in this paper is an effective tool for CVD prediction in Fiji.

Relationship of proteins and subclinical cardiovascular traits in the population-based LIFE-Adult study

Background and aimsUnderstanding molecular processes of the early phase of atherosclerotic cardiovascular disease conditions is of utmost importance for early prediction and intervention measures. MethodsWe measured 92 cardiovascular-disease-related proteins (Olink, Cardiovascular III) in 2024 elderly participants of the population-based LIFE-Adult study. We analysed the impact of 27 covariables on these proteins including blood counts, cardiovascular risk factors and life-style-related parameters. We also analysed protein associations with 13 subclinical cardiovascular traits comprising carotid intima media thickness, plaque burden, three modes of Vicorder-based pulse-wave velocities, ankle-brachial index and ECLIA-based N-terminal prohormone of brain natriuretic peptide (NT-proBNP). ResultsEstimated glomerular filtration rate, triglycerides and sex where the most relevant covariables explaining more than 1 % variance of 49, 22 and 20 proteins, respectively. A total of 43 proteins were significantly associated with at least one of the analysed subclinical cardiovascular traits. NT-pro-BNP, brachial-ankle pulse-wave velocity (baPWV) and parameters of carotid plaque burden accounted for the largest number of associations. Association overlaps were relatively sparse. Only growth/differentiation factor 15, low density lipoprotein receptor and interleukin-1 receptor type 2 are associated with these three different cardiovascular traits. We confirmed several literature findings and found yet unreported associations for carotid plaque presence (von-Willebrand factor, galectin 4), carotid intima-media thickness (carboxypeptidase A1 andB1), baPWV (cathepsin D) and NT-proBNP (cathepsin Z, low density lipoprotein receptor, neurogenic locus homolog protein 3, trem-like transcript 2). Sex-interaction effects were observed, e.g. for spondin-1 and growth/differentiation factor 15 likely regulated by androgen response elements. ConclusionsWe extend the catalogue of proteome biomarkers possibly involved in early stages of cardiovascular disease pathologies providing targets for early risk prediction or intervention strategies.

Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study

Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.

Exploring machine learning approaches for early diabetes risk prediction: A comprehensive examination of health indicators and models

Background: The increased prevalence of morbidity and mortality associated with Type 2 diabetes is due to changing lifestyles, demanding improved disease management measures. To tackle this, scientists are increasingly looking to technological advances, notably machine learning, for illness prevention and management, particularly in non-communicable diseases. The emphasis is on establishing an early detection system to identify Type 2 diabetes risk factors, enabling prompt treatments and preventative steps to reduce the disease’s rising prevalence. Materials and methods: The research aimed to assess the association of diabetes class with health indicators. Five machine learning models were employed with cross-validation techniques to predict early diabetes risk. The performance matrices of the models were evaluated and compared with the existing work. Results: In multivariate analysis, we found polyuria (β=3.492; Aor=32.872; 95% CI=11.09,97.35; p&lt;0.001), polydipsia (β=-4.100; Aor=60.378; 95%CI=18.28,199.37; p&lt;0.001), polyphagia (β=1.181; Aor=3.25; 95%CI=1.23,8.57; p=0.017), genital thrush (β=1.08; Aor=2.96; 95%CI=1.26,7.53; p=0.023), irritability (β=2.28; Aor=9.82; 95%CI=3.41,28.26; p&lt;0.001), and partial paresis (β=1.2406; Aor=3.45; 95% CI=1.35,8.79; p=0.009) are the potential health risk indicators for positive diabetes class. Conclusion: Using an interpretable feature learning approach for early diabetes prediction improves the use of global health data. This method forecasts hazards correctly and gives insights into influential aspects. As a result, a more proactive healthcare strategy is implemented, allowing for more prompt treatments and encouraging a more hopeful future by improving patient outcomes and lowering the total burden of diabetes on individuals and healthcare systems.

Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors, limiting their predictive accuracy. Although artificial intelligence models offer improved accuracy, their "black box" nature poses a barrier to clinical adoption. To develop an artificial intelligence-based HAPI risk-assessment model enhanced with an explainable artificial intelligence dashboard to improve interpretability at both the global and individual patient levels. An explainable artificial intelligence approach was used to analyze ICU patient data from the Medical Information Mart for Intensive Care. Predictor variables were restricted to the first 48 hours after ICU admission. Various machine-learning algorithms were evaluated, culminating in an ensemble "super learner" model. The model's performance was quantified using the area under the receiver operating characteristic curve through 5-fold cross-validation. An explainer dashboard was developed (using synthetic data for patient privacy), featuring interactive visualizations for in-depth model interpretation at the global and local levels. The final sample comprised 28 395 patients with a 4.9% incidence of HAPIs. The ensemble super learner model performed well (area under curve = 0.80). The explainer dashboard provided global and patient-level interactive visualizations of model predictions, showing each variable's influence on the risk-assessment outcome. The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence-based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions.