Multi-horizon air pollution forecasting with deep neural networks

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures

Air pollution prediction with multi-modal data and deep neural networks

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem

Literature on applied machine learning in metagenomic classification: A scoping review

Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries’ search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Connected Health in Europe: Where are we today?

This report, which has grown out of an ENJECT survey of 19 European countries, examines the situation of Connected Health in Europe today. It focuses on creating a clear understanding of the current and developing presence of Connected Health throughout European healthcare systems under five headings: The Policy Environment, Education, Business and Health Models, Interoperability, and The Perso

Smart phone applications for self-monitoring of the menstrual cycle: a review and content analysis

Background: In last years, a spread of smart phone applications (apps) for the self-monitoring of individual health has been recorded, especially among young people. A broad number of healthcare apps is designed for women, encouraging the self-responsibility in the surveillance of their menstrual cycle. Aim of the present study was to provide a review and features analysis of the apps for the self-monitoring of the menstrual cycle available on the major official mobile-phone application platforms. Materials and Methods: A systematic search in Google Play Store and iTunes was performed from January to December 2017. The most popular apps for the monitoring of the menstrual cycle were downloaded and their functions and features were evaluated and compared. Results: The authors found a considerable difference between applications in the number of tracking functions. While some apps are more sophisticated and combine almost all possible functions for tracking the menstrual cycle (Clue, Life, and Period Tracker Lite), some others are simpler, and their purpose is merely to record menstrual days, without any precise calculation of the fertile days (Cycles). With iPeriod, the tracking of menstrual cycle and the received drugs can be recorded. Conclusions: All the studied apps are excellent in providing awareness of the menstrual cycle. Some of them record valuable information for the self-monitoring of the menstrual cycle. Which app to be used mainly depends on the data wanted to be gathered from the monitoring. Although most of the devices and apps are excellent in providing direct information to the user, some improvements are still possible. A future challenge will be how data would be gathered through smart phone apps and how could be used in clinical practice

Short-term air pollution forecasting based on environmental factors and deep learning models

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure

Integration heterogener medizinischer und biologischer Daten in elektronische Patientenakten

The shortage of data for patients with chronic and other diseases and previous medical treatments shows significant weakness in the diagnosis and treatment of patients. Due to the healthcare system insufficiency, patients with comorbidities might not survive the diseases, especially when the disease is novel. The lack of information on patients' genetic disorders, especially when they are unaware of them, also contributes to increased patient deaths. This conveys the necessity to integrate medical and health data with various biological omics and other data, especially in pandemic circumstances. Patients' health data matters are apparent, but they are stored in multiple hospitals and health systems such as electronic health records (EHRs), healthcare institutions, and laboratories. Furthermore, biological data are often not integrated and cannot be used by patients, physicians, and specialists to treat particular diseases. Although the urgent need for healthcare and medical data integration is apparent, personal data protection regulations are severe. They do not allow much progress in the area without implementing security and privacy standards for patient healthcare data. One solution for this issue is setting a personal health record (PHR) as an integrative system for the patient. Many ontological frameworks have been proposed to unify the record formats, but none of them is accepted as a healthcare standard. The efforts toward approving the Health Level Seven (HL7) standards and the common medical coding systems ensure further data integration. Some efforts are made to associate particular diseases with data obtained from external environmental sensors that measure disease-associated data. Using these data, which are called exposome, the increasing symptoms of particular diseases influenced by external factors can be clarified. This paper suggests a cloud-based model for integrating healthcare and medical data from different sources such as EHRs, health information systems, and measurement sensors into the PHR as the first stage toward integrating patient health data. Besides the patients' personal and clinical data, various omics data should be integrated for improved individualized disease prognosis and treatment of the patients. These data are stored in the cloud following the required data security and privacy standards.Der Mangel an Daten über PatientInnen mit chronischen und anderen Krankheiten und medizinischen Vorbehandlungen zeigt eine erhebliche Schwäche bei der Diagnose und Behandlung vieler PatientInnen auf. Aufgrund der Unzulänglichkeit des Gesundheitssystems kann es sein, dass PatientInnen mit Komorbiditäten die Krankheiten nicht überleben, insbesondere wenn es sich um eine neue Krankheit handelt. Der Mangel an Informationen über die genetischen Störungen der PatientInnen, vor allem wenn sie sich derer nicht bewusst sind, trägt ebenfalls zu einer erhöhten PatientInnensterblichkeit bei. Daraus ergibt sich die Notwendigkeit, medizinische und gesundheitliche Daten mit verschiedenen biologischen Omics und anderen Daten zu integrieren, insbesondere unter Pandemiebedingungen. Die Relevanz des Themas der Gesundheitsdaten von PatientInnen ist offensichtlich, aber die Daten werden in verschiedenen Krankenhäusern und Gesundheitssystemen wie der elektronischen Patientenakte (ePA), Gesundheitseinrichtungen und Laboren gespeichert. Darüber hinaus werden biologische Daten oft nicht integriert und können von PatientInnen, ÄrztInnen und SpezialistInnen nicht zur Behandlung bestimmter Krankheiten genutzt werden. Obwohl der dringende Bedarf an der Integration von Gesundheits- und medizinischen Daten offensichtlich ist, sind die Vorschriften zum Schutz personenbezogener Daten streng. Sie lassen keine großen Fortschritte in diesem Bereich zu, ohne dass Sicherheits- und Datenschutzstandards für Gesundheitsdaten von PatientInnen eingeführt werden. Eine Lösung für dieses Problem ist die Einrichtung eines Personal Health Records (PHR) als integratives System für die PatientInnen. Viele ontologische Rahmenwerke wurden vorgeschlagen, um die Datensatzformate zu vereinheitlichen, aber keines von ihnen ist als Standard im Gesundheitswesen anerkannt. Die Bemühungen um die Annahme der Health Level Seven (HL7)-Standards und der gängigen medizinischen Codierungssysteme sorgen für eine weitere Datenintegration. Es gibt Bestrebungen, bestimmte Krankheiten mit Daten in Verbindung zu bringen, die von externen Umweltsensoren gewonnen werden, die krankheitsassoziierte Daten messen. Anhand dieser Daten, die als Exposom bezeichnet werden, können die zunehmenden Symptome bestimmter Krankheiten, die durch externe Faktoren beeinflusst werden, geklärt werden. In diesem Artikel wird ein Cloud-basiertes Modell zur Integration von Gesundheits- und medizinischen Daten aus verschiedenen Quellen wie der ePA, Gesundheitsinformationssystemen und Messsensoren in den PHR als erster Schritt zur Integration von Gesundheitsdaten vorgeschlagen. Neben den persönlichen und klinischen Daten der PatientInnen sollen auch verschiedene Omics-Daten integriert werden, um eine bessere individualisierte Krankheitsprognose und Behandlung der PatinentInnen zu ermöglichen. Diese Daten werden in der Cloud unter Einhaltung der erforderlichen Datensicherheits- und Datenschutzstandards gespeichert

Development and Evaluation of Methodology for Personal Recommendations Applicable in Connected Health.

In this paper, a personal recommendation system of outdoor physical activities using solely user’s history data and without application of collaborative filtering algorithms is proposed and evaluated. The methodology proposed contains four phases: data fuzzyfication, activity usefulness calculation, estimation of most useful activities, activities classification. In the process of classification several data mining techniques were compared such as: decision trees algorithms, decision rules algorithm, Bayes algorithm and support vector machines. The pro-posed algorithm has been experimentally validated using real dataset collected in a certain period of time from a community of 1000 active users. Recommendations generated by the system were related to weight loss. The results show that our generated recommendations have high accuracy, up to 95%