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General General

State of the evidence: a survey of global disparities in clinical trials.

In BMJ global health

INTRODUCTION : Ideally, health conditions causing the greatest global disease burden should attract increased research attention. We conducted a comprehensive global study investigating the number of randomised controlled trials (RCTs) published on different health conditions, and how this compares with the global disease burden that they impose.

METHODS : We use machine learning to monitor PubMed daily, and find and analyse RCT reports. We assessed RCTs investigating the leading causes of morbidity and mortality from the Global Burden of Disease study. Using regression models, we compared numbers of actual RCTs in different health conditions to numbers predicted from their global disease burden (disability-adjusted life years (DALYs)). We investigated whether RCT numbers differed for conditions disproportionately affecting countries with lower socioeconomic development.

RESULTS : We estimate 463 000 articles describing RCTs (95% prediction interval 439 000 to 485 000) were published from 1990 to July 2020. RCTs recruited a median of 72 participants (IQR 32-195). 82% of RCTs were conducted by researchers in the top fifth of countries by socio-economic development. As DALYs increased for a particular health condition by 10%, the number of RCTs in the same year increased by 5% (3.2%-6.9%), but the association was weak (adjusted R2=0.13). Conditions disproportionately affecting countries with lower socioeconomic development, including respiratory infections and tuberculosis (7000 RCTs below predicted) and enteric infections (9700 RCTs below predicted), appear relatively under-researched for their disease burden. Each 10% shift in DALYs towards countries with low and middle socioeconomic development was associated with a 4% reduction in RCTs (3.7%-4.9%). These disparities have not changed substantially over time.

CONCLUSION : Research priorities are not well optimised to reduce the global burden of disease. Most RCTs are produced by highly developed countries, and the health needs of these countries have been, on average, favoured.

Marshall Iain James, L’Esperance Veline, Marshall Rachel, Thomas James, Noel-Storr Anna, Soboczenski Frank, Nye Benjamin, Nenkova Ani, Wallace Byron C


geographic information systems, randomised control trial

General General

Investigation of Influential Factors of Predicting Individuals' Use and Non-use of Fitness and Diet Apps on Smartphones: Application of the Machine Learning Algorithm (XGBoost).

In American journal of health behavior

Objectives: In this study, we aimed to find the influential factors in determining individuals' use and non-use of fitness and diet apps on smartphones. To this end, we focused on diverse groups of predictors that would significantly affect people's use and non-use of these apps. Methods: Overall, we considered 105 factors as potential predictors and included them in further analyses using a machine learning algorithm, XGBoost. The main reason for selecting this particular algorithm was that it had been known as one of the most accurate and popular algorithms for predicting consumer behaviors. Results: We found the accuracy score of those factors for predicting people's use and non-use of fitness and diet apps was approximately 71.3%. In particular, the most influential predictors were mainly related to social influence, media use, overeating, social support, health management, and attitudes toward exercise. Conclusion: These findings contribute to helping scholars and practitioners to develop more practical strategies of the implementation of fitness and diet apps.

Cho Jaehee, Kim Sehwan, Jeong Gwangjin, Kim Chonghye, Seo Ja-Kyoung


Pathology Pathology

Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management.

In Urologia

Machine learning (ML) is the subfield of artificial intelligence (AI), born from the marriage between statistics and computer science, with the unique purpose of building prediction algorithms able to improve their performances by automatically learning from massive data sets. The availability of ever-growing computational power and highly evolved pattern recognition software has led to the spread of ML-based systems able to perform complex tasks in bioinformatics, medical imaging, and diagnostics. These intelligent tools could be the answer to the unmet need for non-invasive and patient-tailored instruments for the diagnosis and management of bladder cancer (BC), which are still based on old technologies and unchanged nomograms. We reviewed the most significant evidence on ML in the diagnosis, prognosis, and management of bladder cancer, to find out if these intelligent technologies are ready to be introduced into the daily clinical practice of the urologist.

Gandi Carlo, Vaccarella Luigi, Bientinesi Riccardo, Racioppi Marco, Pierconti Francesco, Sacco Emilio


Bladder cancer, artificial intelligence, deep learning, machine learning, neural network

General General

Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent System.

In Molecules (Basel, Switzerland)

Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring's absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg-Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring's theory's results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.

Mousavi Seyed Pezhman, Atashrouz Saeid, Nait Amar Menad, Hemmati-Sarapardeh Abdolhossein, Mohaddespour Ahmad, Mosavi Amir


CMIS modeling, Eyring’s theory, artificial intelligence, artificial neural networks, ionic liquids, machine intelligent system, machine learning, viscosity

General General

How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning.

In Journal of religion and health

Nowadays, artificial intelligence (AI) and machine learning (ML) are playing a tremendous role in all aspects of human life and they have the remarkable potential to solve many problems that classic sciences are unable to solve appropriately. Neuroscience and especially psychiatry is one of the most important fields that can use the potential of AI and ML. This study aims to develop an ML-based model to detect the relationship between resiliency and hope with the stress of COVID-19 by mediating the role of spiritual well-being. An online survey is conducted to assess the psychological responses of Iranian people during the Covid-19 outbreak in the period between March 15 and May 20, 2020, in Iran. The Iranian public was encouraged to take part in an online survey promoted by Internet ads, e-mails, forums, social networks, and short message service (SMS) programs. As a whole, 755 people participated in this study. Sociodemographic characteristics of the participants, The Resilience Scale, The Adult Hope Scale, Paloutzian & Ellison's Spiritual Wellbeing Scale, and Stress of Covid-19 Scale were used to gather data. The findings showed that spiritual well-being itself cannot predict stress of Covid-19 alone, and in fact, someone who has high spiritual well-being does not necessarily have a small amount of stress, and this variable, along with hope and resiliency, can be a good predictor of stress. Our extensive research indicated that traditional analytical and statistical methods are unable to correctly predict related Covid-19 outbreak factors, especially stress when benchmarked with our proposed ML-based model which can accurately capture the nonlinear relationships between the collected data variables.

Nooripour Roghieh, Hosseinian Simin, Hussain Abir Jaafar, Annabestani Mohsen, Maadal Ameer, Radwin Laurel E, Hassani-Abharian Peyman, Pirkashani Nikzad Ghanbari, Khoshkonesh Abolghasem


Covid-19, Hope, Machine learning, Resiliency, Spiritual well-being, Stress

Radiology Radiology

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

In Healthcare (Basel, Switzerland)

Thanks to the incredible changes promoted by Information and Communication Technology (ICT) conveyed today by electronic-health (eHealth) and mobile-health (mHealth), many new applications of both organ and cellular diagnostics are now possible [...].

Giansanti Daniele