MACHINE LEARNING APPLICATIONS IN HEALTHCARE

Bhargavi Posinasetty

2024년 4월 · Xoffencerpublication

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The study of healthcare data collection, transmission, processing, storage, and retrieval is called healthcare informatics. This area of study is crucial for preventing sickness, detecting diseases early, diagnosing them early, and treating them early. In the field of healthcare informatics, the sole data that is deemed trustworthy pertains to diseases, patient records, and the computational processes needed to decipher this data. In the past 20 years, traditional medical practices in the US have poured a lot of money on cutting-edge computing and technology infrastructure to help them better serve patients, doctors, and academics. Much effort has gone into improving the quality of medical care that can be delivered using these methods. The driving force behind all of these endeavors was a desire to provide patients with healthcare that was not only affordable and of high quality, but also entirely anxiety-free. Thanks to these initiatives, the value of computational tools for facilitating prescriptions and referrals, establishing and maintaining EHR, and advancing digital medical imaging technology has been increasingly apparent. The installation and administration of electronic health records (EHR) can also be facilitated by these instruments. Clinical trials have demonstrated that computerized physician order entry (CPOE) has the potential to enhance patient care while decreasing medication errors and side effects. By utilizing CPOE, doctors may quickly access relevant patient data without leaving the screen where they are inputting prescriptions. The patient's medical history alerts the treating physician to any potential adverse reactions in advance. Another perk of CPOE is that it lets doctors track their orders as they progress through the system. This provides an additional tool for doctors to assess prescription issues and revise them to remove human error as a potential cause. A logical outgrowth of AI research, machine learning emerged with the field's maturation. Researchers and doctors often turn to machine learning when faced with challenging statistical computations. When people talk about healthcare informatics, they usually imply the study of how to use machine learning in conjunction with healthcare data to find important trends in healthcare. That is why healthcare informatics is all on finding patterns in data so you can learn more. The broad usage of electronic health records (EHRs) has helped bring down the cost of medical treatment by making it easier for hospitals to access and exchange their patients' medical information. Cuts to overhead and elimination of superfluous health exams likely contributed to this price drop. Nevertheless, with the current state of EHR administration, it is difficult to collect and analyze clinical data for trends and patterns across distinct populations. This is because there is now a great deal of uncertainty around the administration of EHR systems. The American Recovery and Reinvestment Act (ARRA) of 20091 and similar programs have made great strides in the direction of standardizing the digitalization of medical records. This makes the possibility of building massive medical databases a real possibility. When data is retrieved from these massive archives, machine learning may be employed to create forecasts and comprehend patterns in other domains. Finding strategies to avoid the computational difficulties that are preventing the distribution, sharing, and standardization of electronic health records (EHRs) is the fundamental objective of research that is being conducted in this area. Because these databases contain sensitive information on patients, the objective is to create openaccess databases that are not just secure but also resistant to a wide variety of cyberthreats. This is because the databases contain sensitive information about individual patients. The regional medical databases that are given below are some samples of some of the most well-known databases in the country: Before these vast data repositories of medical information can be developed, there are a number of obstacles that need to be overcome, as will be illustrated in the following sections; substantial expenditures in research and computer resources are required in order to handle these challenges. In order to resolve these challenges, it is necessary to have a significant amount of money. For instance, in order to integrate newly developed technologies for medical devices and the data that they generate, it will be necessary to manage data structures that are always evolving in order to accommodate these new technologies. It is inevitable that this will occur due to the fact that it will be essential to adapt to the new technology.

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Bhargavi Posinasetty is a seasoned professional with over 7 years of expertise in clinical trials and data management. Holding a Master’s in Public Health (MPH) with a specialization in Epidemiology & Biostatistics from The University of Southern Mississippi, MS, and a Bachelor of Dental Surgery in General Dentistry from Rajiv Gandhi University of Health Sciences, India, Bhargavi uniquely combines healthcare and research in her career.