Task

DATA ACQUISTION AND MANAGEMENT ON HEALTHCARE INDUSTRY

1. INTRODUCTION 

Unstructured data analysis in the health sector using artificial intelligence (AI) has played a major role in boosting the growth of the health industry. Clinical imaging with particulars of artificial intelligence (AI) and simulated intelligence. These most recent developments have improved patient outcomes while also leading to better long-term outcomes because of an enhanced ability for detecting adverse events. Clinical evaluation and therapy must consider unstructured clinical data, thus requiring artificial intelligence (Zheng et al.,2 018).

Medical experts can use sophisticated artificial intelligence techniques to detect subtle clinical images. It helps them in this way that they can identify and make correct diagnosis. Improvement of AI-based system diagnosis and forecasting has been notable. The analysis of enormous amounts of data by an artificial intelligence system allows for individual treatment. They also improved response to medication and interpretation of treatment outcomes in researches (Wang et al., 2018).

Medicine is being disrupted by technologies like artificial intelligence (AI), machine learning etc. This link allows doctors to analyze and comprehend various complicated patterns through data retrieval from different databases. This makes the comprehending of complex issues easy for precise, fast, and dependable medical attention.

2. Healthcare Using Unstructured Data in AI 

In most instances, critical medical information is stored in the form of a free wording. These examples of data can be research publications, patients’ records, and doctors’ notes among others. It also highlights the importance of unstructured data given that it is now possible to incorporate clinical image analysis and artificial intelligence technology (Chen et al., 2014).

Throughout this complicated study, they search for many diverse factors, events, collisions, strange signs, etc. to reveal every suspected medical necessity. Data mining technology is used in extracting unstructured data from a digital image. The information comprises the spatial distribution, brightness, and the location. If we consider such criteria then, with certainty, radiography is likely to be successful. The use of very accurate models which include simulation and machine learning which gives a lot of information on the risk a patient might undergo.

Due to its speed in retrieving usable data from vast, unstructured texts, NLP is an essential part of computational reasoning. Such type of content includes published works in the scientific community, medical records etc (Pastorino et al., 2019). It would be another achievement that having the power of analysing and evaluating disorganized text. Armed with this information, they can venture into large data set searching for latent associations, relations or dependencies that were unknown before.

Using NLP tools, healthcare providers will improve the therapy, discover new clinical data and increase medical service level to any illness. NLP is a technique which supports automatic analysis and interpretation of texts. The use of powerful personal computers to provide convenient access to crucial medical data has been facilitated by recent improvements in both artificial intelligence and human cognition. For example, combining structured and unstructured medical data would greatly enhance quality of clinical imaging and other diagnostic procedures in healthcare.


Figure 1: Healthcare Using Unstructured Data in AI

3. Healthcare AI's Best Practices for Unstructured Data 

a. Acquiring: In order to gather live data in the medical industry, you are required to cultivate partnerships with health organizations, maintain compliance with data protection rules such as HIPAA, and make use of sensors or Internet of Things devices (Al-khafajiy et al., 2019).
b. Storage: When dealing with unstructured data, you will almost always want a robust storage solution that can be expanded. The deployment of cloud-based storages for the purpose of storing such a large volume and variety of healthcare data in a hierarchical and encrypted fashion is being proposed.
c. Sharing: In order to ensure the safety of the data, the blockchain methodology, in conjunction with clearly defined guidelines for the safe exchange of information, will be utilized. Standards for interoperability that are based on HL7 are also included in this category.

It is possible that academics and professionals working in the medical field will have access to data that will be updated in order to ensure that the data are accurate (Mora et al., 2017).

Documentation: The unstructured data, which includes metadata and data files, will be of assistance in tracing the origin of the data and determining the components that make them up in order to comprehend them for better understanding.

Maintaining the data in the healthcare industry requires that it be accurate, consistent, and valid. As a result, it is necessary to conduct data quality checks on a regular basis, to implement data cleansing procedures, and to adhere to data governance principles.

 
Figure 2: Healthcare AI

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4. "Can AI algorithms accurately detect early signs of cancer in radiological images?" 

The proposal for research work on the feasibility of artificial intelligence in disease screening using medical images. This architecture consists of TensorFlow, an open-source artificial intelligence framework (Zheng et al. 2019) It is the CNN TensorFlow pre-prepared models that excel in working with clinical images.

Versatility and ruggedness in use of artificial intelligence in clinical image analysis. The adaptation of artificial intelligence algorithms to unorganised clinical image data by TensorFlow could increase diagnostic precision in identifying vague signs of sicknesses such as cancer.
 
Figure 3: Artificial intelligence cancer detection

 

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References

Al-Khafajiy, M., Baker, T., Chalmers, C., Asim, M., Kolivand, H., Fahim, M. and Waraich, A., 2019. Remote health monitoring of elderly through wearable sensors. Multimedia Tools and Applications, 78(17), pp.24681-24706.
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Dash, S., Shakyawar, S.K., Sharma, M. and Kaushik, S., 2019. Big data in healthcare: management, analysis and future prospects. Journal of big data, 6(1), pp.1-25.
Habibzadeh, H., Dinesh, K., Shishvan, O.R., Boggio-Dandry, A., Sharma, G. and Soyata, T., 2019. A survey of healthcare Internet of Things (HIoT): A clinical perspective. IEEE Internet of Things Journal, 7(1), pp.53-71.
Li, C., Hu, X. and Zhang, L., 2017. The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia computer science, 112, pp.2328-2334.
Luo, E., Bhuiyan, M.Z.A., Wang, G., Rahman, M.A., Wu, J. and Atiquzzaman, M., 2018. Privacyprotector: Privacy-protected patient data collection in IoT-based healthcare systems. IEEE Communications Magazine, 56(2), pp.163-168.
Mora, H., Gil, D., Munoz Terol, R., Azorín, J. and Szymanski, J., 2017. An IoT-based computational framework for healthcare monitoring in mobile environments. Sensors, 17(10), p.2302.
Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W. and Boccia, S., 2019. Benefits and challenges of Big Data in healthcare: an overview of the European initiatives. European journal of public health, 29(Supplement_3), pp.23-27.
Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, pp.3-13.
Zheng, X., Mukkamala, R.R., Vatrapu, R. and Ordieres-Mere, J., 2018, September. Blockchain-based personal health data sharing system using cloud storage. In 2018 IEEE 20th international conference on e-health networking, applications and services (Healthcom) (pp. 1-6). IEEE.