Deep-Confidentiality : An IoT-Enabled Privacy-Preserving Framework for Unstructured Big Biomedical Data

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.

Reliability of prisoners’ survey responses: comparison of self-reported health and biomedical data from an australian prisoner cohort

Objective Prisoner health surveys primarily rely on self-report data. However, it is unclear whether prisoners are reliable health survey respondents. This paper aimed to determine the level of agreement between self-report and biomedical tests for a number of chronic health conditions. Method This study was a secondary analysis of existing data from three waves (1996, 2001, 2009) of the New South Wales (NSW) Inmate Health Survey. The health surveys were cross-sectional in nature and included a stratified random sample of men (n=2,114) from all NSW prisons. Self-reported histories of hepatitis, sexually transmissible infections, and diabetes were compared to objective biomedical measures of these conditions. Results Overall, the sensitivity (i.e., the respondents who self-reported having the condition also had markers indicative of the condition using biomedical tests) was high for hepatitis C (96%) and hepatitis B (83%), but low for all other assessed conditions (ranging from 9.1% for syphilis using RPR to 64% for diabetes). However, Kappa scores indicated substantial agreement only for hepatitis C. That is, there were false positives and false negatives which occurred outside of chance leading to poor agreement for all other assessed conditions. Conclusions Prisoners may have been exposed to serious health conditions while failing to report a history of infection. It may be possible that prisoners do not get tested given the asymptomatic presentation of some conditions, were unaware of their health status, have limited health-service usage preventing the opportunity for detection, or are subject to forgetting or misunderstanding prior test results. These findings demonstrate the importance of the custodial environment in screening for health conditions and referral for treatment should this be needed. Testing on entry, periodically during incarceration, and prior to release is recommended.

The Scope, Methods and Applications of Biomedical Data Mining

Most literature assumptions have been drawn from public databases e.g. NHANES (National Health and Nutrition Examination Survey). Nonetheless, the sets of data are typically featured by high-dimensional timeliness, heterogeneity, characteristics and irregularity, hence amounting to valuation of these databases not being applied completely. Data Mining (DM) technologies have been the frontiers domains in biomedical studies, as it shows smart routine in assessing patients’ risks and aiding in the process of biomedical research and decision-making in developing disease-forecasting frameworks. In that case, DM has novel merits in biomedical Big Data (BD) studies, mostly in large-scale biomedical datasets. In this paper, a description of DM techniques alongside their fundamental practical applications will be provided. The objectives of this study are to help biomedical researchers to attain intuitive and clear appreciative of the applications of data-mining technologies on biomedical BD to enhance to creation of biomedical results, which are relevant in a biomedical setting.

Precision Medicine Landscape of Genomic Testing for Patients With Cancer in the National Institutes of Health All of Us Database Using Informatics Approaches

PURPOSE The rapid growth of biomedical data ecosystems has catalyzed research for oncology and precision medicine. We leverage federal cloud-based precision medicine databases and tools to better understand the current landscape of precision medicine and genomic testing for patients with cancer. METHODS Retrospective observational study of genomic testing for patients with cancer in the National Institutes of Health All of Us Research Program, with the cancer cohort defined as having at least two documented or reported cancer diagnoses. RESULTS There were 5,678 (1.8%) All of Us participants in the cancer cohort, with a significant difference between cancer status by age category, sex, race, and ethnicity ( P < .001 for all). There were 295 (5.2%) patients with cancer who received genomic testing compared with 6,734 (2.2%) of noncancer patients, with 752 genomic tests commonly focused on gene mutations (primarily pharmacogenomics), molecular pathology, or clinical cytogenetic reports. CONCLUSION Although not yet ubiquitous, diverse clinical genomic analyses in oncology can set the stage to grow the practice of precision medicine by integrating research patient data repositories, cancer data ecosystems, and biomedical informatics.

Experience: Automated Prediction of Experimental Metadata from Scientific Publications

While there exists an abundance of open biomedical data, the lack of high-quality metadata makes it challenging for others to find relevant datasets and to reuse them for another purpose. In particular, metadata are useful to understand the nature and provenance of the data. A common approach to improving the quality of metadata relies on expensive human curation, which itself is time-consuming and also prone to error. Towards improving the quality of metadata, we use scientific publications to automatically predict metadata key:value pairs. For prediction, we use a Convolutional Neural Network (CNN) and a Bidirectional Long-short term memory network (BiLSTM). We focus our attention on the NCBI Disease Corpus, which is used for training the CNN and BiLSTM. We perform two different kinds of experiments with these two architectures: (1) we predict the disease names by using their unique ID in the MeSH ontology and (2) we use the tree structures of MeSH ontology to move up in the hierarchy of these disease terms, which reduces the number of labels. We also perform various multi-label classification techniques for the above-mentioned experiments. We find that in both cases CNN achieves the best results in predicting the superclasses for disease with an accuracy of 83%.

Various Approaches to the Quantitative Evaluation of Biological and Medical Data Using Mathematical Models

Biomedical data (structured and unstructured) has grown dramatically in strength and volume over the last few years. Innovative, intelligent, and autonomous scientific approaches are needed to examine the large data sets that are gradually becoming widely available. In order to predict unique symmetric and asymmetric patterns, there is also an increasing demand for designing, analyzing, and understanding such complicated data sets. In this paper, we focused on a different way of processing biological and medical data. We provide an overview of known methods as well as a look at optimized mathematical approaches in the field of biological data analysis. We deal with the RGB threshold algorithm, new filtering based on the histogram and on the RGB model, the Image J program, and the structural similarity index method (SSIM) approaches. Finally, we compared the results with the open-source software. We can confirm that our own software based on new mathematical models is an extremely suitable tool for processing biological images and is important in research areas such as the detection of iron in biological samples. We study even symmetric and asymmetric properties of the iron existence as a design analysis of the biological real data. Unique approaches for clinical information gathering, organizing, analysis, information retrieval, and inventive implementation of contemporary computing approaches are all part of this research project, which has much potential in biomedical research. These cutting-edge multidisciplinary techniques will enable the detection and retrieval of important symmetric and asymmetric patterns, as well as the faster finding of pertinent data and the opening of novel learning pathways.

Hardware and software complex for remote monitoring and control of a pregnant woman's health state

Introduction: The problem of remote monitoring of people's health has become especially urgent nowadays due to the rapid spread of dangerous infectious and viral diseases, such as COVID-19. This period was especially difficult for pregnant women. According to Rosstat statistics, in 2020, maternal mortality in Russia increased by 24.4% compared to 2019 and reached 11.2 per 100,000 newborns. This is the worst level since 2013. In the current conditions, there is a necessity for developing remote monitoring systems which allow you to check the health status of a pregnant woman remotely using tools outside a medical institution. Purpose: To develop the structure and validate the choice of elements for a hardware and software complex which would perform remote monitoring outside a medical institution and assess the condition of pregnant women during their active life. Results: An automated questionnaire for pregnant women has been developed in accordance with the methodological recommendations of the Ministry of Health of the Russian Federation, providing a quantitative assessment of the current state of a pregnant woman in order to study the dynamics of her health. Based on the results of instrumental studies, according to 30 factors of patient's body functioning and the questionnaire data, a set of diagnostically significant indicators was developed. For each of them, a range of values was specified (norm, alarm, pathology). We have developed an experimental sample of the hardware and software complex and tested its functioning, particularly the modes of taking biomedical data by urine tests. The algorithms for processing and analysis of biomedical data have been experimentally studied in order to confirm the validity of the proposed solutions. Practical relevance: The results of the studies allow us to affirmatively answer the question about the possibility of remote monitoring outside a medical institution and assessing the health state of a pregnant woman in order to predict pregnancy complications, as well as to validate the choice of measuring channels for recording a complex of biomedical signals and data, and the choice of algorithms for information processing and analysis.