The value of mathematical modelling approaches in epidemiology for public health decisión making

It is discussed the relevance of quantitative approaches, specifically mathematical modelling in epidemiology, in the public health decision-making process. This topic is discussed here based on the experience of various experts in mathematical epidemiology and public health. First, the definition of mathematical modelling is presented, especially in the context of epidemiology. Second, the different uses and socio-political implications, including empirical examples of recent experiences that have taken place at the international level are addressed. Finally, some general considerations regarding the challenges encountered in the use and application of mathematical modelling in epidemiology in the decision-making process at the local and national levels.

Se trata sobre la importancia de los abordajes cuantitativos, específicamente la formulación de modelos matemáticos en epidemiología, dentro del proceso de toma de decisiones en salud pública. Esta importante temática se analiza basándose en la experiencia de algunos expertos en epidemiología matemática y salud pública. En primer lugar, se presenta la definición de modelación matemática, particularmente dentro del contexto de la epidemiología. En segundo lugar, se abordan los diferentes usos y las implicaciones socio-políticas, incluyendo ejemplos de experiencias recientes que han ocurrido a nivel internacional. Finalmente, se hace referencia a ciertas consideraciones generales respecto a los retos que representa el uso y la aplicación de modelos matemáticos en epidemiología para el proceso de toma de decisiones a nivel local y nacional.

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

BACKGROUND: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. METHODS: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. RESULTS: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. CONCLUSION: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Towards intellectual freedom in an AI Ethics Global Community.

The recent incidents involving Dr. Timnit Gebru, Dr. Margaret Mitchell, and Google have triggered an important discussion emblematic of issues arising from the practice of AI Ethics research. We offer this paper and its bibliography as a resource to the global community of AI Ethics Researchers who argue for the protection and freedom of this research community. Corporate, as well as academic research settings, involve responsibility, duties, dissent, and conflicts of interest. This article is meant to provide a reference point at the beginning of this decade regarding matters of consensus and disagreement on how to enact AI Ethics for the good of our institutions, society, and individuals. We have herein identified issues that arise at the intersection of information technology, socially encoded behaviors, and biases, and individual researchers' work and responsibilities. We revisit some of the most pressing problems with AI decision-making and examine the difficult relationships between corporate interests and the early years of AI Ethics research. We propose several possible actions we can take collectively to support researchers throughout the field of AI Ethics, especially those from marginalized groups who may experience even more barriers in speaking out and having their research amplified. We promote the global community of AI Ethics researchers and the evolution of standards accepted in our profession guiding a technological future that makes life better for all.

Correction: Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis.

[This corrects the article DOI: 10.2196/25925.].

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis.

BACKGROUND: Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily. OBJECTIVE: This study aims to characterize Spanish-speaking users showing anorexia signs on Twitter through the extraction and inference of behavioral, demographical, relational, and multimodal data. By using the transtheoretical model of health behavior change, we focus on characterizing and comparing users at the different stages of the model for overcoming AN, including treatment and full recovery periods. METHODS: We analyzed the writings, posting patterns, social relationships, and images shared by Twitter users who underwent different stages of anorexia nervosa and compared the differences among users going through each stage of the illness and users in the control group (ie, users without AN). We also analyzed the topics of interest of their followees (ie, users followed by study participants). We used a clustering approach to distinguish users at an early phase of the illness (precontemplation) from those that recognize that their behavior is problematic (contemplation) and generated models for the detection of tweets and images related to AN. We considered two types of control users-focused control users, which are those that use terms related to anorexia, and random control users. RESULTS: We found significant differences between users at each stage of the recovery process (P<.001) and control groups. Users with AN tweeted more frequently at night, with a median sleep time tweets ratio (STTR) of 0.05, than random control users (STTR=0.04) and focused control users (STTR=0.03). Pictures were relevant for the characterization of users. Focused and random control users were characterized by the use of text in their profile pictures. We also found a strong polarization between focused control users and users in the first stages of the disorder. There was a strong correlation among the shared interests between users with AN and their followees (ρ=0.96). In addition, the interests of recovered users and users in treatment were more highly correlated to those corresponding to the focused control group (ρ=0.87 for both) than those of AN users (ρ=0.67), suggesting a shift in users' interest during the recovery process. CONCLUSIONS: We mapped the signs of AN to social media context. These results support the findings of previous studies that focused on other languages and involved a deep analysis of the topics of interest of users at each phase of the disorder. The features and patterns identified provide a basis for the development of detection tools and recommender systems.

Predicting risk of dyslexia with an online gamified test.

Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -a tablet instead of a desktop computer- reaching a recall of over 78% for the class with dyslexia for children 12 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool in Spanish based on our methods has already been used by more than 200,000 people.

Author Correction: Reclassifying neurodegenerative diseases.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis.

BACKGROUND: Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. OBJECTIVE: This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. METHODS: We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). RESULTS: We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. CONCLUSIONS: The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.

Burden of neurological diseases in the US revealed by web searches.

BACKGROUND: Analyzing the disease-related web searches of Internet users provides insight into the interests of the general population as well as the healthcare industry, which can be used to shape health care policies. METHODS: We analyzed the searches related to neurological diseases and drugs used in neurology using the most popular search engines in the US, Google and Bing/Yahoo. RESULTS: We found that the most frequently searched diseases were common diseases such as dementia or Attention Deficit/Hyperactivity Disorder (ADHD), as well as medium frequency diseases with high social impact such as Parkinson's disease, MS and ALS. The most frequently searched CNS drugs were generic drugs used for pain, followed by sleep disorders, dementia, ADHD, stroke and Parkinson's disease. Regarding the interests of the healthcare industry, ADHD, Alzheimer's disease, MS, ALS, meningitis, and hypersomnia received the higher advertising bids for neurological diseases, while painkillers and drugs for neuropathic pain, drugs for dementia or insomnia, and triptans had the highest advertising bidding prices. CONCLUSIONS: Web searches reflect the interest of people and the healthcare industry, and are based either on the frequency or social impact of the disease.

Proteomic analysis of peach fruit mesocarp softening and chilling injury using difference gel electrophoresis (DIGE).

BACKGROUND: Peach fruit undergoes a rapid softening process that involves a number of metabolic changes. Storing fruit at low temperatures has been widely used to extend its postharvest life. However, this leads to undesired changes, such as mealiness and browning, which affect the quality of the fruit. In this study, a 2-D DIGE approach was designed to screen for differentially accumulated proteins in peach fruit during normal softening as well as under conditions that led to fruit chilling injury. RESULTS: The analysis allowed us to identify 43 spots -representing about 18% of the total number analyzed- that show statistically significant changes. Thirty-nine of the proteins could be identified by mass spectrometry. Some of the proteins that changed during postharvest had been related to peach fruit ripening and cold stress in the past. However, we identified other proteins that had not been linked to these processes. A graphical display of the relationship between the differentially accumulated proteins was obtained using pairwise average-linkage cluster analysis and principal component analysis. Proteins such as endopolygalacturonase, catalase, NADP-dependent isocitrate dehydrogenase, pectin methylesterase and dehydrins were found to be very important for distinguishing between healthy and chill injured fruit. A categorization of the differentially accumulated proteins was performed using Gene Ontology annotation. The results showed that the 'response to stress', 'cellular homeostasis', 'metabolism of carbohydrates' and 'amino acid metabolism' biological processes were affected the most during the postharvest. CONCLUSIONS: Using a comparative proteomic approach with 2-D DIGE allowed us to identify proteins that showed stage-specific changes in their accumulation pattern. Several proteins that are related to response to stress, cellular homeostasis, cellular component organization and carbohydrate metabolism were detected as being differentially accumulated. Finally, a significant proportion of the proteins identified had not been associated with softening, cold storage or chilling injury-altered fruit before; thus, comparative proteomics has proven to be a valuable tool for understanding fruit softening and postharvest.

A rapid and efficient method for purifying high quality total RNA from peaches (Prunus persica) for functional genomics analyses.

Prunus persica has been proposed as a genomic model for deciduous trees and the Rosaceae family. Optimized protocols for RNA isolation are necessary to further advance studies in this model species such that functional genomics analyses may be performed. Here we present an optimized protocol to rapidly and efficiently purify high quality total RNA from peach fruits (Prunus persica). Isolating high-quality RNA from fruit tissue is often difficult due to large quantities of polysaccharides and polyphenolic compounds that accumulate in this tissue and co-purify with the RNA. Here we demonstrate that a modified version of the method used to isolate RNA from pine trees and the woody plant Cinnamomun tenuipilum is ideal for isolating high quality RNA from the fruits of Prunus persica. This RNA may be used for many functional genomic based experiments such as RT-PCR and the construction of large-insert cDNA libraries.

A Rapid and Efficient Method for Purifying High Quality Total RNA from Peaches (Prunus persica) for Functional Genomics Analyses

Prunus persica has been proposed as a genomic model for deciduous trees and the Rosaceae family. Optimized protocols for RNA isolation are necessary to further advance studies in this model species such that functional genomics analyses may be performed. Here we present an optimized protocol to rapidly and efficiently purify high quality total RNA from peach fruits (Prunus persica). Isolating high-quality RNA from fruit tissue is often difficult due to large quantities of polysaccharides and polyphenolic compounds that accumulate in this tissue and co-purify with the RNA. Here we demonstrate that a modified version of the method used to isolate RNA from pine trees and the woody plant Cinnamomun tenuipilum is ideal for isolating high quality RNA from the fruits of Prunus persica. This RNA may be used for many functional genomic based experiments such as RT-PCR and the construction of large-insert cDNA libraries.