Search Results (6)

Background: Dietary supplements (DS) are widely used to address nutritional deficiencies and promote health, yet their indiscriminate use often leads to reduced efficacy, adverse effects, and safety concerns. Biomarker-driven approaches have emerged as a promising strategy to optimize DS prescriptions, ensuring precision and reducing risks associated with generic recommendations. Methods: This narrative review synthesizes findings from key studies on biomarker-guided dietary supplementation and the integration of artificial intelligence (AI) in biomarker analysis. Key biomarker categories—genomic, proteomic, metabolomic, lipidomic, microbiome, and immunological—were reviewed, alongside AI applications for interpreting these biomarkers and tailoring supplement prescriptions. Results: Biomarkers enable the identification of deficiencies, metabolic imbalances, and disease predispositions, supporting targeted and safe DS use. For example, genomic markers like MTHFR polymorphisms inform folate supplementation needs, while metabolomic markers such as glucose and insulin levels guide interventions in metabolic disorders. AI-driven tools streamline biomarker interpretation, optimize supplement selection, and enhance therapeutic outcomes by accounting for complex biomarker interactions and individual needs. Limitations: Despite these advancements, AI tools face significant challenges, including reliance on incomplete training datasets and a limited number of clinically validated algorithms. Additionally, most current research focuses on clinical populations, limiting generalizability to healthier populations. Long-term studies remain scarce, raising questions about the sustained efficacy and safety of biomarker-guided supplementation. Regulatory ambiguity further complicates the classification of supplements, especially when combinations exhibit pharmaceutical-like effects. Conclusions: Biomarker-guided DS prescription, augmented by AI, represents a cornerstone of personalized nutrition. While offering significant potential for precision and efficacy, advancing these strategies requires addressing challenges such as incomplete AI data, regulatory uncertainties, and the lack of long-term studies. By overcoming these obstacles, clinicians can better meet individual health needs, prevent diseases, and integrate precision nutrition into routine care. Full article

Background: Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner. Objective: In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes. Methods: This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024. Results: Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions. Conclusions: The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders. Full article

Emerging evidence suggests that personalized dietary supplement regimens can significantly influence lipid metabolism and cardiovascular risk. The efficacy of AI-guided dietary supplement prescriptions, compared with standard physician-guided prescriptions, remains underexplored. In a randomized, parallel-group pilot study, 70 patients aged 40–75 years with LDL-C levels between 70 and 190 mg/dL were enrolled. Participants were randomized to receive either AI-guided dietary supplement prescriptions or standard physician-guided prescriptions for 90 days. The primary endpoint was the percent change in LDL-C levels. Secondary endpoints included changes in total cholesterol, HDL-C, triglycerides, and hsCRP. Supplement adherence and side effects were monitored. Sixty-seven participants completed the study. The AI-guided group experienced a 25.3% reduction in LDL-C levels (95% CI: −28.7% to −21.9%), significantly greater than the 15.2% reduction in the physician-guided group (95% CI: −18.5% to −11.9%; p < 0.01). Total cholesterol decreased by 15.4% (95% CI: −19.1% to −11.7%) in the AI-guided group compared with 8.1% (95% CI: −11.5% to −4.7%) in the physician-guided group (p < 0.05). Triglycerides were reduced by 22.1% (95% CI: −27.2% to −17.0%) in the AI-guided group versus 12.3% (95% CI: −16.7% to −7.9%) in the physician-guided group (p < 0.01). HDL-C and hsCRP changes were not significantly different between groups. The AI-guided group received a broader variety of supplements, including plant sterols, omega-3 fatty acids, red yeast rice, coenzyme Q10, niacin, and fiber supplements. Side effects were minimal and comparable between groups. AI-guided dietary supplement prescriptions significantly reduce LDL-C and triglycerides more effectively than standard physician-guided prescriptions, highlighting the potential for AI-driven personalization in managing hypercholesterolemia. Full article

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic. Full article

Context. This commentary is based on an innovative approach to the development of predictive analytics. It is centered on the development of predictive models for varying stages of chronic disease through integrating all types of datasets, adds various new features to a theoretically driven data warehousing, creates purpose-specific prediction models, and integrates multi-criteria predictions of chronic disease progression based on a biomedical evolutionary learning platform. After merging across-center databases based on the risk factors identified from modeling the predictors of chronic disease progression, the collaborative investigators could conduct multi-center verification of the predictive model and further develop a clinical decision support system coupled with visualization of a shared decision-making feature for patient care. The Study Problem. The success of health services management research is dependent upon the stability of pattern detection and the usefulness of nosological classification formulated from big-data-to-knowledge research on chronic conditions. However, longitudinal observations with multiple waves of predictors and outcomes are needed to capture the evolution of polychronic conditions. Motivation. The transitional probabilities could be estimated from big-data analysis with further verification. Simulation or predictive models could then generate a useful explanatory pathogenesis of the end-stage-disorder or outcomes. Hence, the clinical decision support system for patient-centered interventions could be systematically designed and executed. Methodology. A customized algorithm for polychronic conditions coupled with constraints-oriented reasoning approaches is suggested. Based on theoretical specifications of causal inquiries, we could mitigate the effects of multiple confounding factors in conducting evaluation research on the determinants of patient care outcomes. This is what we consider as the mechanism for avoiding the black-box expression in the formulation of predictive analytics. The remaining task is to gather new data to verify the practical utility of the proposed and validated predictive equation(s). More specifically, this includes two approaches guiding future research on chronic disease and care management: (1) To develop a biomedical evolutionary learning platform to predict the risk of polychronic conditions at various stages, especially for predicting the micro- and macro-cardiovascular complications experienced by patients with Type 2 diabetes for multidisciplinary care; and (2) to formulate appropriate prescriptive intervention services, such as patient-centered care management interventions for a high-risk group of patients with polychronic conditions. Conclusions. The commentary has identified trends, challenges, and solutions in conducting innovative AI-based healthcare research that can improve understandings of disease-state transitions from diabetes to other chronic polychronic conditions. Hence, better predictive models could be further formulated to expand from inductive (problem solving) to deductive (theory based and hypothesis testing) inquiries in care management research. Full article

The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs. Full article