Translation in Data Mining to Advance Personalized Medicine for Health Equity

Abstract

Personalized medicine is the development of ‘tailored’ therapies that reflect traditional medical approaches, with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention, diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs, requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of ‘big data’ and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

“…They synthesized the emerging aproaches and methodologies and highlighted the gaps between academic research and accessibility of evaluation, informatics, and big data from health information systems. Another paper [ 163 ] reviewed the availability of big data and the role of biomedical informatics in personalized medicine, emphasizing the ethical concerns related to personalized medicines and health equity. Personalized medicine has a potential to reduce healthcare cost, however, the researchers think it can create race, income, and educational disparity.…”

An Intelligent System Prototype to support and sharing diagnoses of maligned tumours, based on personalized medicine philosophy

“…Unlike to EBM, the data sources regard to personalized medicine can be classified in three groups [8], [10]: (i) genomic data about particular information of an individual, (ii) clinical history of patients or similar cases captured from sources like sensors (e.g. Electronic Health Recorder -EHR) [11], [12], and (iii) public or private big biomedical data bases; they are able to contribute with enough evidences to apply intelligent and specialized algorithms in personalized treatments. Personalized medicine is related to the development of strategies to generate tailored treatments for strengthening traditional medicine by adding the patient's genetics profile and heterogeneous information available in repositories as indicated in (iii).…”

“…This implies adequate means of storage, being Big Data the most suitable for this need. When Big Data is personalized-medicine oriented, three factors are important [10], [11], [12]: (i) volume, which means the size of the datasets, (ii) speed, which means how much fast patients' data, treatment data, diagnoses and/or advice are generating; and (iii) variety, which means diversity and heterogeneity of data obtained from several sources and formats (e.g. texts, images, plots, etc.).…”

Patient-specific workup of adrenal incidentalomas

“…Personalized medicine will help produce more efficient and effective diagnoses and treatment, and will lead to better prognoses for patients at both the individual and population level. The availability of patient- and disease-related data in today’s healthcare workforce is a significant resource in the development and application of this individual-based approach [12] . Therefore, by using these data preventive or therapeutic interventions can be concentrated on the patients who will benefit, and at the same time sparing expense and side effects for those who will not [13] .…”