Comparing three data mining algorithms for identifying associated risk factors of Type 2 Diabetes

Introduction: Type 2 diabetes (T2DM) shows increasing prevalence and global health burden, causing a concern among health service providers and health administrators. The current study is aimed at developing and comparing some statistical models that are useful in measuring or establishing such associations. The three particular statistical methods investigated in this study are artificial neural network (ANN), support vector machines (SVM) and multivariate logistic regression (MLR) using demographic, anthropometric and biochemical characteristics on a sample of 9528 individuals from Mashhad city. Methods: The statistical methods involved in this study are also known as machine learning algorithms and require dividing the available data in to training and testing dataset. This study has randomly selected 70% cases (6654 cases) for training and reserved the remaining 30% (2874 cases) for testing. The three methods are compared with help of the receiver operating characteristic (ROC) curve. Results: The prevalence rate of T2DM is 14% in our population. The ANN model has 78.7% , accuracy, 63.1% sensitivity and 81.2% specificity. Values of these three parameters are 76.8%, 64.5% and 78.9% respectively for SVM and 77.7%, 60.1% and 80.5%, respectively for MLR. The area under the ROC curve (AUC) is 0.71 for ANN, in SVM model was 0.73 for SVM, and 0.70 for MLR.  Conclusion: The overall conclusion is that ANN performs better than two models and can be used effectively to identify associated risk factors of T2DM. &nbsp

Applying decision tree for detection of a low risk population for type 2 diabetes: A population based study

Introduction: The aim of current study was to create a prediction model using data mining approach, decision tree technique, to identify low risk individuals for incidence of Type 2 diabetes (T2DM), using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. Methods: a prediction model was developed using classification by the decision tree method on 9528 subjects recruited from MASHAD database. Moreover, the receiver operating characteristic (ROC) curve was applied. Results: The prevalence rate of T2DM was ~14% in our population. For decision tree model, the accuracy, sensitivity, and specificity value for identifying the related factors with T2DM were 78.7%, 47.8% and 83%, respectively. In addition, the area under the ROC curve (AUC) value for recognizing the risk factors associated with T2DM was 0.64. Moreover, we found that subjects with family history of T2DM, age>=48, SBP>=130, DBP>=81, HDL>=29, LDL>=148 and occupation=other have more than 59% chance of this disorder, while the chance of T2DM in subjects without history with TG>=184, age>=48 and hs-CRP>=2.2, have approximately 51% chance. Conclusion: Our findings demonstrated that decision tree analysis, using routine demographic, clinical, anthropometric and biochemical measurements, which combined with other risk score models, could create a simple strategy to predict individuals at low risk for type 2 diabetes in order to decrease substantially the number of subjects needing for screening and recognition of subject at high risk

Challenges and opportunities beyond structured data in analysis of electronic health records

Electronic health records (EHR) contain a lot of valuable information about individual patients and the whole population. Besides structured data, unstructured data in EHRs can provide extra, valuable information but the analytics processes are complex, time-consuming, and often require excessive manual effort. Among unstructured data, clinical text and images are the two most popular and important sources of information. Advanced statistical algorithms in natural language processing, machine learning, deep learning, and radiomics have increasingly been used for analyzing clinical text and images. Although there exist many challenges that have not been fully addressed, which can hinder the use of unstructured data, there are clear opportunities for well-designed diagnosis and decision support tools that efficiently incorporate both structured and unstructured data for extracting useful information and provide better outcomes. However, access to clinical data is still very restricted due to data sensitivity and ethical issues. Data quality is also an important challenge in which methods for improving data completeness, conformity and plausibility are needed. Further, generalizing and explaining the result of machine learning models are important problems for healthcare, and these are open challenges. A possible solution to improve data quality and accessibility of unstructured data is developing machine learning methods that can generate clinically relevant synthetic data, and accelerating further research on privacy preserving techniques such as deidentification and pseudonymization of clinical text

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care

Anemia is associated with cognitive impairment in adolescent girls: a cross-sectional survey

Anemia is associated with impairment in oxygen transport, affecting an individual’s physical and mental wellbeing, and work performance. The aim of this study was to examine the prevalence of anemia and its possible association with serum antibody titers to Hsp27 (as an indicator of cellular stress), cognitive function, measures of emotion, sleep patterns in adolescent girls. A total of 940 adolescent girls were assessed to evaluate neuropsychological function with validated questionnaires. A complete blood count was determined as part of the assessment of hematological parameters. Serum anti-Hsp27 was measured for each subject. Among the total of 940 participants, 99 girls (10.5%) were anemic [hemoglobin <12(g/dl)]. Serum anti-HSP27 was significantly higher in anemic compared to healthy girls (p<0.05). There was no significant differences in depression, aggression, insomnia, daytime sleepiness and sleep apnea score between two groups. However, the total cognitive abilities score was significantly lower in the anemic girls (76.8±2.1 versus 85.7±2.5, p = 0.002). Logistic regression analysis showed that anemic girls were 1.73 times more likely than non-anemic girls to have cognitive impairment (95% confidence interval [CI] = 1.07-2.78; P = 0.025). Anemia was associated with elevated levels of anti-HSP27 and supports the hypothesis that cellular stress may be associated with anemia. Anemia was adversely associated with an assessment of cognitive abilities and was an independent risk factor for cognitive impairment in this grou

Depression and anxiety symptoms are associated with prooxidant-antioxidant balance: a population-based study

Background: Depression and anxiety are significantly associated with systemic inflammation. Moreover, oxidative stress resulting from a disturbance in the prooxidant-antioxidant balance is linked to inflammation-related conditions. Therefore, depression/anxiety symptoms may also be associated with oxidative stress. Objective: To examine the association between depression/anxiety symptoms and serum prooxidant-antioxidant balance (PAB) in adults who participated in a large population-based, cross-sectional study. Methods: Serum PAB values were measured in 7,516 participants (62% females and 38% males) aged 35–65 years, enrolled in a population-based cohort study. Beck Depression and Anxiety Inventories were used to evaluate symptoms of depression and anxiety. Multinomial logistic regression was used to examine the effect of confounders on the status of serum PAB change. Results: Among men, serum PAB values were increased incrementally from 1.55±0.47 to 1.59±0.47, 1.69±0.38, and 1.68±0.38 in the no or minimal, mild, moderate and severe depression groups, respectively (P trend<0.001). Serum PAB values also increased significantly across these four corresponding groups among women [1.70±0.45, 1.73±0.44, 1.75±0.44, and 1.76±0.40, (P trend=0.005)]. About anxiety, serum PAB values increased significantly across the four groups in men (P trend=0.02) but not in women (P trend=0.2). The adjusted odds ratios for serum PAB values among men with severe depression and anxiety symptoms were 1.75 and 1.27, respectively. Moreover, the adjusted odds ratios for serum PAB values among women with severe depression and anxiety symptoms were 1.40 and 1.17, respectively. Conclusion: Symptoms of depression and anxiety appear to be associated with higher degrees of oxidative stress, expressed by higher serum PAB values

The prevalence of metabolic syndrome increases with serum hs-CRP concentration in individuals without a history of cardiovascular disease: a report from a large Persian cohort

BACKGROUND: Metabolic syndrome (MetS) is defined by a clustering of cardiovascular (CV) risk factors and is associated with a heightened inflammatory state. A raised serum high-sensitivity C-reactive protein (hs-CRP), a marker of inflammation, is also known to associate with CV risk. We have investigated the relationship between the presence of MetS and serum hs-CRP concentration in a large representative Persian population cohort without a history of cardiovascular disease (CVD). METHODS: The MASHAD study population cohort comprised 9 778 subjects, who were recruited from the city of Mashhad, Iran, between 2007 and 2008. Several cardiovascular risk factors were measured in this population without CVD. Individuals were categorized into quartiles of serum hs-CRP concentration: 1st quartile - 0.72 (0.59-0.85) [median (range)] mg/L, 2nd quartile - 1.30 (1.14-1.4) mg/L, 3rd quartile - 2.29 (1.92-2.81) mg/L and 4th quartile - 6.63 (4.61-11.95) mg/L respectively. The prevalence of MetS in each quartile was determined using either International Diabetes Federation (IDF) or Adult Treatment Panel III (ATPIII) criteria. RESULTS: The prevalence of MetS was highest in the 4th quartile for serum hs-CRP [1220 subjects (50.0%)], and significantly higher than that in the 1st quartile (reference group) [634 subjects (25.9%)] (p<0.001). A positive smoking habit [OR, 1.47 (1.26-1.70), p<0.001] and the presence of either MetS-IDF [OR, 1.35 (1.18-1.55), p<0.001] or Mets-ATPIII [OR, 1.40 (1.18-1.50), p<0.001] were strong predictors of a 4th quartile for serum hs-CRP concentration. CONCLUSIONS: There was a significant association between high levels of serum hs-CRP and the presence of MetS among individuals without a history of CVD in our Persian cohort

High-dose vitamin D supplementation is associated with an improvement in several cardio-metabolic risk factors in adolescent girls: a nine-week follow up study

Background: Vitamin D deficiency is a prevalent and important global health problem. Because of its role in growth and development, vitamin D status is likely to be particularly important in adolescent girls. Here we explored the effects of high-dose vitamin D supplementation on cardiometabolic risk factors. Methods: We have examined the effects of vitamin D supplementation on cardio-metabolic risk factors in 988 healthy adolescent girls in Iran. Fasting blood samples and anthropometric measurements were obtained at baseline and after supplementation with high dose vitamin D. All individuals took a capsule of 50000 IU vitamin D/ week for nine weeks. The study was completed by 940 participants. Results: the prevalence of vitamin D deficiency was 90% at baseline, reducing to16.3% after vitamin D supplementation. Vitamin supplementation was associated with a significant increase in serum levels of 25 (OH) vitamin D and calcium. There were significant reductions in diastolic blood pressure, heart rate, waist circumference, and serum fasting blood glucose, total- and low density lipoprotein-cholesterol after the nine-week period on vitamin D treatment, but no significant effects were observed on body mass index, systolic blood pressure, or serum high density lipoprotein-cholesterol and triglyceride. Conclusion: vitamin D supplementation had beneficial effects on cardio-metabolic profile in adolescent girls

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting

Common polymorphisms in genes related to vitamin D metabolism affect the response of cognitive abilities to vitamin D supplementation

It is possible that vitamin D acts as a neurosteroid and that vitamin D deficiency may have an adverse impact on brain function and cognitive function. There are a few reports that have demonstrated an association between polymorphisms of genes involved in vitamin D metabolism and neurodegenerative disease. We aimed to evaluate the relationship between common, functional vitamin D–associated gene variants and cognitive abilities and to investigate the effect size of this polymorphism on cognitive capabilities associated with high-dose vitamin D supplementation. A total of 319 healthy adolescents received a high dose of vitamin D (50,000 IU)/week for 9 weeks. A questionnaire was used to assess cognitive abilities at baseline and after treatment. The genotypes of the CYP2R1-rs10766197 and GC-rs4588 variants were determined using TaqMan genotyping techniques. At baseline, total cognitive ability scores were higher in the AA group who were homozygous for the uncommon allele, compared with the other (AG and GG) genotypes of the CYP2R1-rs10766197 polymorphism (104.9 ± 27.8 vs. 79.1 ± 38.8 vs. 73.1 ± 25.6; p < 0.001, respectively). During the supplementation period, cognitive ability scores increased in individuals with the AG and GG genotypes, while individuals with a AA genotype did not show significant change in total score after intervention (p = 0.17). For GC SNP (rs4588), no major differences at baseline and trial-net change of cognitive tasks score were observed between the genotypes under three genetic models (pSNP = 0.67). Vitamin D supplements have trait-dependent effects on cognitive performance that suggests a causal role for vitamin D in cognitive performance. The rs10766197 variant, near the CYP2R1 gene locus, significantly modified the efficacy of high-dose vitamin D3 supplementation for its effects on improving cognitive abilities indicate that some subjects might require a higher dose to benefit from in terms of cognitive performance