Top 10 Real-world Bayesian Network Applications – Know the importance!
This tutorial is all about Bayesian Network applications. We will discuss all these applications in detail so that you can understand the importance of Bayesian Network in different sectors. As you can see in the below image, Bayesian Network is used in various kind of fields.
So, let’s explore all these fields with the use of Bayesian Network in them.
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Latest Applications of Bayesian Network
Let’s discuss some major applications of the Bayesian Network one by one:
1. Gene Regulatory Network
GRN is Gene Regulatory Network or Genetic Regulatory Network. It comprises of several DNA segments in a cell. It interacts with other substances in the cell and also with each other indirectly. Indirectly means through their protein and RNA expression products. Thus, it governs the expression levels of mRNA and proteins. GRNs reproduce the behaviour of the system using Mathematical models. In some cases, corresponding with experimental observations, it generates predictions.
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It is the science or practice of diagnosis. For the treatment and prevention of any disease, we use medicines. We are using medicines since ancient times. Over the years, medicines and drugs have evolved to cater to a variety of health care practices. In order to provide better healthcare, machines and other computer devices assist us in the diagnosis of the disease.
We use biomonitoring to quantify the concentration of chemicals. It measures the concentration in blood and tissue of humans, etc. Hence, it is the measurement of the body burden in analytical chemistry. Biomonitoring involves the use of indicators. These measurements are often done in blood and urine. To determine the levels of many ECCs in humans, DTSC scientist is conducting biometric studies.
4. Document Classification
It is a problem in library science, computer science, and information science. The main task is to assign a document to multiple classes. We can also do it manually or algorithmically. Manual classification is intellectual classification and it takes time. We use the algorithmic classification of documents in information science and computer science.
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5. Information Retrieval
It is the activity of obtaining information resources. Information retrieval concerns retrieving the information from databases. It is a continuous process. During the process, we can consider, reconsider and refine our research problem. Metadata or full-text indexing is the basis of searching. To reduce “information overload“, we use automated information retrieval systems.
6. Semantic Search
By understanding searcher intent and the contextual meaning of terms, it improves search accuracy. It enhances the accuracy in the searchable dataspace, whether on the web or within a closed system, to generate more relevant results.
7. Image Processing
It is the processing of images by using mathematical operations. We can also use image processing to convert images into digital format. After converting the images, we can also apply some operations on it to enhance the image. Image processing is any form of signal processing.
In this, the input can be formed as an image, such as a photograph or video frame. The output of image processing may be either a set of characteristics or parameters related to the image or an image. Hence, in image processing techniques, we generally treat the image as a two-dimensional signal. After that, we apply standard signal processing on it.
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8. Spam Filter
The spam filter is a program. We use a spam filter to detect unsolicited and unwanted email. Bayesian spam filter calculates whether the message is spam or not. The Bayesian spam filter is more robust than other spam filters. We use filtering to learn from spam and ham messages.
9. Turbo Code
The turbo codes are a class of high-performance forward error correction codes. Thus, turbo code uses the Bayesian Network. Turbo codes are the state of the art of codecs. 3G and 4G mobile telephony standards use these codes. Hence the Bayesian Network represents turbo coding and decoding process.
10. System Biology
We can also use BN to infer different types of biological network from Bayesian structure learning. In this, the main output is the qualitative structure of the learned network.
Using Bayesian Networks for Medical Diagnosis – A Case Study
Bayesian Networks were introduced as a formalism for reasoning with methods that involved uncertainty. Bayesian Networks allow easy representation of uncertainties that are involved in medicine like diagnosis, treatment selection and prediction of prognosis. BN models are being used to assist doctors in judging the diagnosis and selecting an appropriate selection to address the problem.
In order to represent formalisms for the development of Bayesian Networks, there are particular guidelines. One particular issue that patients face in hospitals is the contraction of Ventilator-associated Pneumonia (VAP). Effective treatment and diagnosis of VAP are one of the major challenges in medicine. In order to address this problem, the right antibiotics must be selected that are effective against causative organisms and do not cause any major side-effects.
A Bayesian Network model of VAP was built using the knowledge of causal dependencies, influences or correlations. This was derived mostly from the domain experts or structure learning algorithms. The above graph represents the causal relationship between different variables. For example – When a particular pathogen colonizes a patient, there is a high probability that the patient will develop symptoms of pneumonia. Therefore, an arc is drawn from ‘colonisation’ to ‘pneumonia’ variable. Furthermore, the duration of the patient’s stay in mechanical ventilation and the immunological status also affects the probability of contracting pneumonia. This is the reason why arcs from ‘mechanical ventilation’ and ‘immunological status’ are drawn towards the ‘pneumonia’ variable.
This same network can be used for making the medical-related decision making. It is difficult to ascertain whether the problem is VAP as the symptoms that are reflected by the patient are not only constrained to it. For example – A patient exposed to a mechanical ventilator may show symptoms of fever which makes it difficult to decide on a diagnosis as there are multiple causes of common fever. In order to determine the diagnosis based on the evidence, ROC (Receiver Operating Characteristics) analysis method is used to establish the diagnosis through the determination of the probability cut-off point.
In order to predict the causative organisms, it is essential to incorporate temporal knowledge into the Bayesian Network model of VAP. Treatment selection involves the selection of an antibiotic combination to provide optimized results. As discussed above, antibiotics need to be able to tackle the pathogens without causing major side effects. This requires an extension of Bayesian Networks with decision theory. This decision theory involves utility function which should be maximized to obtain optimal treatment.
We discussed various applications of Bayesian Network that justifies its versatile nature. We covered all the core applications of Bayesian Network, still, is we missed any, feel free to share it in the comment section below.
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