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The digital revolution in medicine produced a paradigm shift in the healthcare industry. One of the major benefits of the digital healthcare system and electronic medical records is the improved access to the healthcare records both for health professionals and patients. The success of initiatives that provides bitcoin proof of work difficulty urinating with the access to their electronic healthcare records, such as OpenNotes, suggests their potential to improve the quality and efficiency of medical care bitcoin proof of work difficulty urinating 12 ].
At the same time, biomedical data is not limited to the clinical records created by physicians, the substantial amount of data bitcoin proof of work difficulty urinating retrieved from biomedical imaging, laboratory testing such as basic blood tests, and omics data.
Notably, the amount of genomic data alone is projected to surpass the amount of data generated by other data-intensive fields such as social networks and online video-sharing platforms [ 3 ]. However, while increased data volume and complexity offers new exciting perspectives in healthcare industry development, it also introduces new challenges in data analysis and interpretation, and of course, privacy and security.
Due to huge demand for the treatments and prevention of chronic diseases, mainly driven by aging of the population, there is a clear need for the new global integrative healthcare approaches [ 5 ]. Majority of the recent approaches to personalized medicine in bitcoin proof of work difficulty urinating and other diseases relied on the various data types including the multiple types of genomic [ 6 - 10 ], transcriptomic [ 11 - 13 ], microRNA [ 14 ], proteomic [ 15 ], antigen [ 16 ], methylation [ 17 ], imaging [ 1819 ], metagenomic [ 20 ], mitochondrial [ 21 ], metabolic [ 22 ], physiological [ 23 ] and other data.
Introduction of new technologies, such as an artificial intelligence and blockchain, may enhance and scale up the progress in health care sciences and lead to effective and cost-efficient healthcare ecosystems.
In this article we first review one of the recent achievements in next-generation artificial intelligence, deep learning, that holds the great promise as a biomedical research tool with many applications.
We then discuss basic concepts of highly distributed storage systems HDSS as one of the advantageous solutions for data storage, introduce the open-source blockchain framework Exonum and review the bitcoin proof of work difficulty urinating of blockchain for healthcare marketplace. For the first time we introduce half-life period of analysis significance, models of data value for single and group of users and cost of buying data in the context of biomedical applications.
WHere we also present a blockchain-based platform for empowering patients to ensure that they havetake a control over their personal data, manage the access priviledges and to protect their data privacy, as well to allow patients to benefit from their data receiving a crypto tokesscurrency as a reward for their data or for healthy behaviorand to contribute to the overall biomedical progress. We speculate that such systems may be used by the governments on the national scale to increase participation of the general public in preventative medicine and even provide the universal basic income to the their citizens willing to participate in such programs that will greatly decrease the burden of disease on the healthcare systems.
Finally, we cover important aspects of data quality control using the recent advances in deep learning and bitcoin proof of work difficulty urinating machine learning methods. While the amount of health-associated data and the number of large scales global projects increases, integrative analysis of this data is proving to be problematic [ 27 ].
Even high-quality biomedical data is usually highly heterogeneous and complex and requires special approaches for preprocessing and analysis. Computational biology methods are routinely used in various fields of healthcare and are incorporated in pipelines of pharmaceutical companies.
Machine learning techniques are among the leading and the most promising tools of computational analysis. Increased computer processing power and algorithmic advances have led to the significant improvement in the field machine learning. Although machine learning methods are now routinely used in various research fields, including biomarker development and drug discovery [ 28 bitcoin proof of work difficulty urinating 31 ], the machine learning techniques utilizing the Deep Neural Networks DNNs are able to capture bitcoin proof of work difficulty urinating dependencies in the healthcare data [ 32 ].
The feedforward DNNs were recently successfully applied to prediction the various drug properties, such as pharmacological action [ 3334 ], and toxicity [ 35 ]. Biomarker development, a design or search for distinctive characteristics of healthy or pathological conditions, is another area where the application of DNNs has led to significant achievements. For example, an ensemble of neural networks was applied to predict age and sex of patients based on their common blood test profiles [ 36 ].
Convolutional neural networks CNNs were trained to classify cancer patients using immunohistochemistry of tumour tissues [ 37 ]. While the DNNs are able to extract features from the data automatically and usually outperform the other machine learning approaches in feature extraction tasks, one of the good practices is to select a set of relevant features before bitcoin proof of work difficulty urinating the deep model, especially when the dataset is comparatively small.
Algorithms such as the principal component analysis or clustering methods are widely used in bioinformatics [ 39 ]. However, these first-choice approaches transform the data into a set of components and features that may be bitcoin proof of work difficulty urinating hard to interpret from the perspective of biology.
For example, Aliper and bitcoin proof of work difficulty urinating used signaling pathway analysis to reduce the dimensionality of drug-induced gene expression profiles and to train a DNN based predictor of pharmacological properties of drugs [ 33 ]. Selected pathway activation scores were compared to expression changes of over most representative, landmark genes. DNN trained on the pathway scores outperformed DNN trained on the set of landmark genes and achieved the F1 score, the weighted average of precision and recall, of 0.
In addition, signaling pathway-based dimensionality reduction allowed for the more robust performance on the validation set, while classifiers trained on gene expression data demonstrated a significant decrease in predictive accuracy on the validation set compared to the training set performance.
There are many promising machine learning techniques in practice and in development including the upcoming capsule networks and recursive cortical networks and many advances are being made in symbolic learning and natural language processing. However, the recurrent neural networks, generative adversarial networks and transfer learning techniques are gaining popularity in the healthcare applications and can be applied to the blockchain-enabled personal data marketplaces.
Generative adversarial networks GANs are among the most promising recent developments in deep learning. GAN architecture was first introduced by Goodfellow et al. Similar concepts were applied for molecule generation by Kadurin bitcoin proof of work difficulty urinating colleagues [ 41 ].
A dataset of molecules with bitcoin proof of work difficulty urinating different tumor growth inhibition TGI activity was used to train an adversarial Autoencoder AAEwhich combines the properties of both the discriminator and the generator. The trained model then was used to generate the fingerprints of molecules with desired properties.
Further analysis of the generated molecules showed that new molecular fingerprints are matched closely to already known highly effective anticancer drugs such as anthracyclines. As a continuation of this work, authors proposed an enhanced architecture that also included additional molecular parameters such as solubility and enabled the generation of more chemically diverse molecules [ 42 ]. New model clearly showed the improvement in the training and generation processes, suggesting a great potential in drug discovery.
Electronic health records contain the clinical history of patients and could be used to identify the individual risk of developing cardiovascular diseases, diabetes and other chronic conditions [ 43 ]. Recurrent neural networks RNNswhich are naturally suited for sequence analysis, are one of the most promising tools for text or time-series analysis.
And one of the most advantageous applications of RNNs in healthcare is electronic medical record analysis. Recently, RNNs were used to predict heart failure of patients based on clinical events in their records [ 44 ]. Models trained on 12 month period of clinical history and tested on 6 months demonstrated an Area Under the Curve AUC of 0. Interestingly, analysis of cases that were predicted incorrectly, showed that networks tend to predict heart failure based on a patient history of heart diseases, for example hypertension.
At the same time, most of the false negative heart failure predictions are made for cases of acute heart failure with little or no symptoms. Along with cardiovascular disease risk prediction, RNNs were also applied to predict blood glucose level of Type I diabetic patients up to one hour using data from continuous glucose monitoring devices [ 45 ].
The proposed system operates fully automatically and could be integrated with blood glucose and insulin monitoring systems. While mobile health is an attractive and promising field that emerged recently, another exciting area of RNNs application is human activity prediction based on data from wearable devices.
Being exceptionally data hungry, most of deep learning algorithms require a lot of data to train and test the system. Many approaches have been proposed to address this problem, including transfer learning. Transfer learning focuses bitcoin proof of work difficulty urinating translating information learned on one domain or larger dataset to another domain, smaller in size. Transfer learning techniques are commonly used in image recognition when the large data sets required to train the deep neural networks to achieve high accuracy are not available.
The architecture of CNNs allows transferring fitted parameters of a trained neural network to another bitcoin proof of work difficulty urinating. Biomedical image datasets are usually limited by the size of samples, so larger non-biological image collections, such ImageNet, could be used to fine-tune a network first. With an average F1 score of Similarly, CNNs fined-tuned on the ImageNet were applied for glioblastoma brain tumour prediction [ 50 ].
One and zero-shot learning are some of the transfer learning techniques that allow to deal with restricted datasets. Taking into account that real-world data is usually imbalanced, one shot learning bitcoin proof of work difficulty urinating aimed to recognise new data points based on only a few examples in the training sets. Going further, zero-shot learning intents to recognise new object without seeing the examples of those instances in the training set.
Both one and zero-shot learning are concepts of bitcoin proof of work difficulty urinating transfer learning. Medical chemistry is one of the fields where data is scarce, therefore, to address this problem Altae-Tran and colleagues proposed a one-shot learning approach for the prediction of molecule toxic potential [ 51 ]. In this work, authors use a graph representation of molecules linked to the labels from Tox21 and SIDER databases to train and test models.
One-shot networks as siamese networks, LSTMs with attention and novel Iterative Refinement LSTMs, were compared with each other, with graph convolutional neural networks and with random forest with trees as a conventional model. In addition, to evaluate the translational potential of the one-shot architecture, networks trained on Bitcoin proof of work difficulty urinating data were tested on SIDER, however none of the one-shot networks achieved any predictive power, highlighting the potential limitation in translation from toxic in vitro assays into the human clinic.
The recent explosion in generation and need for data has made it very necessary to find better systems for data storage. Among other requirements, the data storage systems should be better in terms of reliability, accessibility, scalability and affordability, all of which would translate into improved availability.
While there could be many options for optimizing these requirements, HDSS has been found to be a very useful and viable option. Traditionally, a lot of technologies and techniques have been employed to store data since the development of computer systems, bitcoin proof of work difficulty urinating, with the exponential increase in data bitcoin proof of work difficulty urinating and computing power, solutions like HDSS has become very important.
Basically, HDSS involves storing data in multiple nodes, which could simply be databases or host computers. Data stored in these nodes are usually replicated or redundant and HDSS makes a quick access to data over this large number of nodes possible. It is usually specifically used to refer to either a distributed database where users store information on a number of nodes, or a computer network in which users store information on a number of peer network nodes.
In recent years, storage failures have been one of the data handling challenges of higher importance, making reliability one of the important requirements for storage systems. HDSS, which allows data to be replicated in a number of different nodes or storage units and makes it protected from failures, has become very popular. There have been a significant amount of progress both in the applications and the optimization of HDSS. However, some of the key challenges in HDSS applications are ensuring consistency of data across various storage nodes and affordability of the systems.
These challenges have been addressed by many recent HDSS solutions, including distributed non-relational databases and peer network node data stores. This is for example, a case of peer-to-peer node data store implemented in blockchain. Blockchain could be described as a distributed database that is used to maintain a continuously growing list of records.
These records are composed into blocks, which are locked together using certain cryptographic mechanisms to maintain consistency of the data. Normally a blockchain is maintained by a peer-to-peer network of users who collectively adhere to agreed rules which are insured by the software for accepting new blocks. Each record in the block contains a timestamp or signature and a link to a previous block in the chain. By design, blockchain is made to ensure immutability of the data.
So once recorded, the data in any given block cannot be modified afterwards without the alteration of all subsequent blocks and the agreement of the members of the network. Because of its integrity and immutability, blockchain could be used as an open, distributed ledger and can record transactions between different parties or networked database systems in an efficient, verifiable and permanent manner.
Bitcoin proof of work difficulty urinating is also flexible enough to allow adding bitcoin proof of work difficulty urinating logic to process, validate and access the data, which is implemented via so called smart contracts components of business logic shared and synchronized across all bitcoin proof of work difficulty urinating. This makes blockchain very suitable bitcoin proof of work difficulty urinating application in healthcare and other areas where data bitcoin proof of work difficulty urinating very sensitive and strict regulations on how data can be used need to be imposed.
While data could be said to be the lifeblood bitcoin proof of work difficulty urinating the current digital society, many are yet fully to grasp the need for appropriate acquisition and processing of data [ 5253 ].
Among the key concerns in the generation and use of data are privacy issues. This is even more important in healthcare, where a high percentage of personal health data generated could be considered private.
In order to ensure propriety in the handling of data, there have been regulations and rules that guide processes such as generation, use, transfer, access and exchange of data. Although privacy has been recognized as a fundamental human right by the United Nations in the Universal Declaration of Human Rights at the United Nations General Assembly, there is yet to be universal agreement on what constitutes privacy [ 54 ].
As a result, privacy issues and regulatory concerns have often been topics of important but yet varied interpretations wherever data is generated and used. With the dawn of computing and constant advancements in tech, there have been massive amounts of data generated on daily basis, and a substantial amount of these data consists of information which could be considered private.
Some regulatory efforts to ensure proper flow and use of these data could become barriers to meaningful development [ 52 ].
While developers and researchers are usually keen to get down to work; analyzing, processing and using data, some barriers could make getting and using relevant data challenging [ 55 - 58 ].
