Growing Importance of Machine Learning in Healthcare to Determine Potential Risk

The growing convenience of electronic healthcare data represents a significant opportunity within the healthcare segment, offering the potential for both pioneering discoveries as well as practical applications aimed at improving the overall quality of healthcare. Nevertheless, for healthcare epidemiologists to fully harness the potential of all these data, there is a pursuing need for computational techniques capable of handling extensive and intricate datasets. Machine learning (ML), which involves the investigation of tools and methodologies for discovering hidden patterns within data, develops as a valuable resource in this context. The cautious implementation of Machine Learning techniques with electronic healthcare data embraces the potential of a comprehensive transformation of patient risk assessment, traversing across the entire spectrum of medical disciplines and predominantly impacting the domain of infectious diseases. Such a transformation could ultimately lead to the development of precise interventions designed to mitigate the proliferation of healthcare-associated pathogens. Healthcare epidemiologists are facing an increasingly demanding task of processing and deciphering extensive and intricate datasets. This challenge arises in the cycle with the expanding role of healthcare epidemiologists, paralleled by the growing prevalence of electronic health data. The availability of substantial volumes of high-quality data at both the patient and facility levels has opened new avenues for exploration. Specifically, these data hold the potential to enhance our comprehension of the risk factors associated with healthcareassociated infections (HAIs), refine patient risk assessment methodologies, and unveil the pathways responsible for the intra- and interfacility transmission of infectious diseases. These insights, in turn, pave the way for targeted preventive measures.

Historically, a significant portion of clinical data remained unutilized, often due to the sheer magnitude and intricacy of the data itself, as well as the absence of suitable techniques for data collection and storage. These valuable data resources were frequently underappreciated and underutilized. However, the advent of novel and improved data collection and storage methods, such as electronic health records, has presented a unique opportunity to address this issue. Especially, machine learning has begun to permeate the realm of clinical literature at large. The prudent application of Machine Learning within the domain of healthcare epidemiology (HE) holds the promise of yielding substantial returns on the considerable investments made in data collection within the field. In the context of this research work, the initiative has been given by elucidating the fundamental principles of Machine Learning, subsequently investigating its relevance and applications within the realm of healthcare epidemiology, reinforced by illustrative instances of successful research endeavours.

Finally, we outline some of the reasonable considerations essential for the design and execution of ML methodologies within the field of healthcare epidemiology. Within the scope of this research, an effort has been initiated by providing an introductory overview of the fundamental principles of Machine Learning.

Subsequently, it is explored into an exploration of how Machine Learning stands poised to revolutionize healthcare epidemiology, substantiating our discussion with illustrative instances of successful applications.

Keywords: Clinical data, Data-driven computation, Healthcare epidemiologist, Healthcare-associated infections (HAIs), Machine learning, Patient risk stratification.