Artificial Intelligence in Predicting Heart Failure

Heart Failure is a major chronic disease that is increasing day by day and a great health burden in health care systems world wide. Artificial intelligence (AI) techniques such as machine learning (ML), deep learning (DL), and cognitive computer can play a critical role in the early detection and diagnosis of Heart Failure Detection, as well as outcome prediction and prognosis evaluation. The availability of large datasets from difference sources can be leveraged to build machine learning models that can empower clinicians by providing early warnings and insightful information on the underlying conditions of the patients

Short-Term Risk of Incision Surgery in The Eyelids of Pediatric and Adolescent Patients With Allergic Conjunctivitis: An Increasingly Important Health Perspective

The global prevalence of allergic diseases has increased dramatically in recent decades. From a global health perspective, they have been considered as a major chronic disease, and the related social burden has also been increasing worldwide. In line with this trend, we investigated the short-term risk of incision surgery for eyelid inflammatory masses in pediatric and adolescent patients with allergic conjunctivitis (AC). The prevalence of AC and incision surgery showed a similar pattern of bimodal peaks during the spring and autumn of South Korea, reflecting the peak allergic seasons. The risk of incision surgery in patients with AC is 4.27 times higher than that of patients without AC and the risk of incision surgery among patients with AC is higher in age between 6-9 years and women. These findings strongly suggests that the incision surgery risk due to eyelid inflammation mass is higher in patients with AC than that of patients without AC. Furthermore, greater attention should be paid to its risk on the age between 6-9 years and women.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers

Background and objectives Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. Methods The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. Results A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. Conclusion The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and deserves further validation in clinical trials.

LONG-TERM CARE NEEDS BY CHINESE ELDERLY AND POLICY PRIORITIES IN CHINA

With the life expectancy in China continuing to increase, age-dependent chronic diseases are also likely to increase, as is the number of people with long-term care needs. This study evaluated the Long Term Care (LTC) needs of the Chinese older population and introduced related policy priorities. Using the 2014 and 2016 “China Longitudinal Aging Social Survey”, this study assessed the physical functions of older adults by measuring their ability to perform Activities of Daily Living independently, compared changes within the two years, and explored other related indicators including, Instrumental Activities of Daily Living, major chronic disease, and mental health conditions. The study also discussed the development of long-term care policies in China and highlighted the priorities of these policies.

Association of diet quality and physical activity with healthy ageing in the French NutriNet-Santé cohort

A growing number of studies have explored overall health during ageing in a holistic manner by investigating multidimensional models of healthy ageing (HA). However, little attention has been given to the role of adherence to national nutrition guidelines in that context. This study aimed to investigate the prospective association between adherence to the French nutrition guidelines and HA. The authors analysed data from 21 407 participants of the NutriNet-Santé study with a median baseline age of 55·6 years (2009–2014) and initially free of major chronic diseases. HA was defined as not developing major chronic disease, no depressive symptoms, no function-limiting pain, independence in instrumental activities of daily living, good physical, cognitive and social functioning, as well as good self-perceived health. Adherence to guidelines of the French Nutrition and Health Programme (Programme National Nutrition Santé or PNNS) was measured via the PNNS Guideline Score (PNNS-GS), using baseline data from repeated 24-h dietary records and physical activity questionnaires. After a median follow-up of 5·7 years, 46·3 % of participants met our HA criteria. Robust-error-variance Poisson regression revealed that higher PNNS-GS scores, reflecting higher adherence to nutrition recommendations (including both diet and physical activity guidelines), were associated with a higher probability to age healthily (relative riskquartile 4 v. quartile 1 = 1·17 (95 % CI 1·12, 1·22)). Supplementary analyses revealed that this association may, to a small part, be mediated by weight status. The results suggest that high adherence to the French national nutrition recommendations may be linked to better overall health throughout ageing.

Physical activity interventions for major chronic disease: a matched-pair analysis of Cochrane and non-Cochrane systematic reviews

ObjectiveTo assess the degree of concordance between Cochrane and non-Cochrane systematic reviews with meta-analyses of physical activity interventions.Study Design and SettingWe conducted a matched-pair analysis with individual meta-analyses as the unit of analysis, comparing Cochrane reviews of randomised controlled trials of physical activity interventions with non-Cochrane reviews. Meta-analyses were matched based on the intervention, condition, outcome and publication year. Matched pairs were contrasted statistically in terms of differences between effect estimates, their precision, and number of citations using Wilcoxon two-sample test and agreement using Bland-Altman plots.ResultsOur search yielded 24 matched meta-analyses. Matched pairs were similar in terms of the number of included studies, sample sizes and publication year but only half (51.7%) of 545 individual clinical trials were included in both the Cochrane and non-Cochrane paired reviews. Effect estimates from non-Cochrane reviews were larger for 15 (62.5%) pairs, smaller for 8 (33.3%) and equal to Cochrane reviews for one (4.2%) pair. On average, effect estimates from non-Cochrane reviews were 0.12 log units (or 13%) higher compared with matched Cochrane reviews (z-score −2.312, P=0.012). We observed discrepancies with regard to the statistical (n=6) and clinical interpretation (n=4) of effect estimates, with non-Cochrane reviews reporting more often a statistically significant result (4/6) and effect sizes favouring intervention of greater than a two-fold (4/4) compared with Cochrane matches. Non-Cochrane reviews were also more frequently cited irrespective of whether the results agree or disagree in their statistical conclusion but this finding did not reach statistical significance at the traditional 0.05 threshold.ConclusionOn average, meta-analyses from non-Cochrane reviews reported higher effect estimates and were more likely to show significant effects favouring the intervention compared with meta-analyses from Cochrane reviews. Though differences were small, they were sufficient to result in important discrepancies in statistical and clinical interpretations between a number of reviews.What is new?Key findings:Findings demonstrate non-Cochrane reviews on average report larger effect estimates and have discrepancies in statistical and clinical interpretation more likely to favour physical activity interventions. Potential sources underpinning discrepant review findings are explored in an accompanying sister paper.What this adds to what was known?The first assessment of systematic differences between paired Cochrane and non-Cochrane meta-analyses examining the role of physical activity interventions for preventing and treating major chronic diseaseWhat is the implication and what should change now?Authors should be aware of the need of protocol registration to minimise unnecessary duplication and be mindful of potential discrepant findings depending on the source of review evidence.

ROLE OF GAUMUTRA IN THE MANAGEMENT OF OBESITY W.S.R TO STHAULYA: A REVIEW STUDY

Obesity is a common and preventable disease of clinical and public health importance. It is often a major risk factor for the development of several non-communicable diseases, significant disability and premature death. In Ayurveda, Sthaulya is described in all the literatures. Acharya Charaka has described Sthaulya Purusha among one of the AshtaNinditaPurusha. Sthaulyais caused due to Medovriddhi which includes abnormal and excessive accumulation of medadhatu in the body. This is caused by frequent and excessive intake of madhur and snigdhaaahar , lack of physical and mental exercises. These all results into the increase in kaphadoshaand medodhatu results in the sthaulya. In modern science, Sthaulya can be compared to the obesity. Obesity is one of the metabolic disorders. WHO considers obesity as a Global epidemic and a public health problem. It is estimated that more than 300 million adults are obese and many are overweight. Sthaulya (obesity) is discouraged by the society for social as well as on the medical grounds. Three main causes have been described in modern literature viz. 1) Dietetic, 2) Genetic, 3) Hormonal. Person of every age and sex is suffering by this widely spreaded epidemic i.e obesity. It is the major chronic disease in developing as well as in developed countries. The line of treatment includes the treatment of Dhatvagnimandya. In pathogenesis of Sthaulya, KledakaKapha, Samana&Vyana Vayu, Meda (fat /lipid) and Medodhatvagni Mandyata are main responsible factors Keywords: Medodhatu, Obesity, Sthaulyata, Gaumutra.

Chocolate intake and heart disease and stroke in the Women's Health Initiative: a prospective analysis

ABSTRACT Background Three recent meta-analyses found significant prospective inverse associations between chocolate intake and cardiovascular disease risk. Evidence from these meta-analyses suggests that such inverse associations may only apply to elderly individuals or those with pre-existing major chronic disease. Objective We assessed the association between habitual chocolate intake and subsequent incident coronary heart disease (CHD) and stroke, and the potential effect of modification by age. Design We conducted multivariable Cox regression analyses using data from 83,310 postmenopausal women free of baseline pre-existing major chronic disease in the prospective Women's Health Initiative cohort. Chocolate intake was assessed using a food-frequency questionnaire. Physician-adjudicated events or deaths were ascertained up to 30 September 2013. Results After exclusions, there were 3246 CHD and 2624 stroke events or deaths, representing incidence rates of 3.9% and 3.2% during 1,098,091 and 1,101,022 person-years (13.4 y), respectively. We found no association between consumption of chocolate and risk of CHD (P for linear trend = 0.94) or stroke (P = 0.24). The results for CHD and stroke combined were similar (P = 0.30), but were significantly modified by age (P for interaction = 0.02). For women age <65 y at baseline, those who ate 1 oz (28.35 g) of chocolate <1/mo, 1 to <1.5/mo, 1.5 to <3.5/mo, 3.5/mo to <3/wk, and ≥3/wk had HRs (95% CIs) of 1.00 (referent), 1.17 (1.00, 1.36), 1.05 (0.90, 1.22), 1.09 (0.94, 1.25), and 1.27 (1.09, 1.49), respectively (P for linear trend = 0.005). No association was apparent for older women. Conclusion We observed no association between chocolate intake and risk of CHD, stroke, or both combined in participants free of pre-existing major chronic disease. The relation for both combined was modified by age, with a significant positive linear trend and an increased risk in the highest quintile of chocolate consumption among women age <65 y. This trial was registered at clinicaltrials.gov as NCT03453073.

Survey on classifier algorithms for health care system in diabetes

Health care is huge, complex and heterogeneous platform for finding out missing values as well as predicting human diabetes with the use of data mining techniques. Diabetes mellitus is a major chronic disease which can be a challenging issue among worldwide. An effective medical diagnosis can be possible by discovering necessary information from medical dataset. The diabetes affected zone patterns can be identified with the proper implementation of data mining technique. This paper focuses about diabetes mellitus and research work carried out on data mining technique to solve diabetes mellitus. This paper also focuses on taking a various measurement points and techniques adopted by different researches, and discusses about the recent and effective algorithm to short out diabetes mellitus.

Mediterranean Diet and Health Outcomes in the SUN Cohort

The Mediterranean Dietary (MedDiet) Pattern has been linked to many beneficial health effects. This review summarizes the main findings of a prospective cohort study, the Seguimiento Universidad de Navarra (SUN) cohort, specifically focused on MedDiet and the risk of major chronic disease. It is an open cohort in which 22,786 Spanish university graduates have participated since 1999 until February 2018. Data on diet, lifestyle and clinical diagnosis are collected at baseline and every two years. After reviewing 21 publications from the SUN cohort on the effects of the MedDiet, we conclude that this cohort has provided good evidence that a high MedDiet adherence is associated with a reduced incidence of all-cause mortality, fatal and non-fatal major cardiovascular disease (CVD), type 2 diabetes, weight gain, metabolic syndrome, depression, cognitive decline, and nephrolithiasis. An inverse dose-response relationship was found for many of these associations. The MedDiet was also associated with lower average heart rate, a mitigation of the harmful effects of overweight/obesity on the risk of CVD, and an attenuation of the effects of obesity on type 2 diabetes. A suggestion that the MedDiet may enhance fertility was also found.