Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.

/pdf/tuberculosis-disease-diagnosis-based-on-an-optimized-machine-2q83n3qg.pdf

Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model

A psychiatrist's review of the state of knowledge about the effects of pornography and the reasons for the ferocious arguments on the subject.

Caught in the Cross-Fire.

Big Data scenarios pose a new challenge to traditional data mining algorithms, since they are not prepared to work with such amount of data. Smart Data refers to data of enough quality to improve the outcome from a data mining algorithm. Existing data mining algorithms unability to handle Big Datasets prevents the transition from Big to Smart Data. Automation in data acquisition that characterizes Big Data also brings some problems, such as differences in data size per class. This will lead classifiers to lean towards the most represented classes. This problem is known as imbalanced data distribution, where one class is underrepresented in the dataset. Ensembles of classifiers are machine learning methods that improve the performance of a single base classifier by the combination of several of them. Ensembles are not exempt from the imbalanced classification problem. To deal with this issue, the ensemble method have to be designed specifically. In this paper, a data preprocessing ensemble for imbalanced Big Data classification is presented, with focus on two-class problems. Experiments carried out in 21 Big Datasets have proved that our ensemble classifier outperforms classic machine learning models with an added data balancing method, such as Random Forests.

Smart Data based Ensemble for Imbalanced Big Data Classification

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications. 

/pdf/artificial-intelligence-techniques-to-predict-the-airway-2gwqt1zq.pdf

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Simple Summary Many patient clinical characteristics, such as diagnosis dates, biomarker status, and therapies received, are only available as unstructured text in electronic health records. Obtaining this information for research purposes is a difficult and costly process, requiring trained clinical experts to manually review patient documents. Machine Learning techniques offer a promising solution for efficiently extracting clinically relevant information from unstructured text found in patient documents. However, the use of data produced with machine learning techniques for research purposes introduces unique challenges in assessing validity and generalizability to different cohorts of interest. To enable the effective and accurate use of such data for research purposes, we developed an evaluation framework to be utilized by model developers, data users, and other stakeholders. This framework can serve as a baseline to contextualize the quality, strengths, and limitations of using data produced with machine learning techniques for research purposes. Abstract A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especially when scaling to large datasets is needed. Fortunately, Natural Language Processing (NLP) and Machine Learning (ML) techniques provide promising solutions for a variety of information extraction tasks such as identifying a group of patients who have a specific diagnosis, share common characteristics, or show progression of a disease. However, using these ML-extracted data for research still introduces unique challenges in assessing validity and generalizability to different cohorts of interest. In order to enable effective and accurate use of ML-extracted real-world data (RWD) to support research and real-world evidence generation, we propose a research-centric evaluation framework for model developers, ML-extracted data users and other RWD stakeholders. This framework covers the fundamentals of evaluating RWD produced using ML methods to maximize the use of EHR data for research purposes.

/pdf/considerations-for-the-use-of-machine-learning-extracted-29rzg0ds.pdf

Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.

https://www.mdpi.com/2227-9709/8/2/27/pdf

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

/pdf/benchmarking-of-machine-learning-models-to-assist-the-1s3n4z6d0a.pdf

Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

The number of connected devices and the amount of data traffic exchanged through mobile networks is expected to double in the near future. Long Term Evolution (LTE) and fifth generation (5G) technologies are evolving to support the increased volume, variety and velocity of data and new interfaces the Internet of Things demands. 5G goes beyond increasing data throughput, providing broader coverage and reliable ultra-low latency channels to support challenging future applications. However, this comes with a cost. As such, the architectural design of radio access network requires due consideration. This chapter explains why the radio access network is critical to 5G success and how novel trends on edge computing, network slicing and network virtualisation perform a critical role in optimising resources on emerging 5G infrastructures.

/pdf/flying-to-the-clouds-the-evolution-of-the-5g-radio-access-10u57mxx8d.pdf

Flying to the Clouds: The Evolution of the 5G Radio Access Networks

A systematic literature review about integrating dependability attributes, performability and sustainability in the implantation of cooling subsystems in data center

Lubnnia Morais Florêncio Souza

Papers

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

Flying to the Clouds: The Evolution of the 5G Radio Access Networks

A systematic literature review about integrating dependability attributes, performability and sustainability in the implantation of cooling subsystems in data center

Dependability evaluation and sensitivity analysis of data center cooling systems