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Revisitation of precision medicine. a Precision medicine moves from a genetics-centered personalization of treatment on to a dynamic risk assessment and optimization of current and future health status through immutable (e.g. genetics) and actionable factors (e.g. behavior). b Disease phenotypes are reclassified on the basis of new system-level evidence, identifying pathophysiological endotypes associated with common, known phenotypes. The logos are trademarks of their respective companies and institutions, and their uses do not represent affiliation or endorsement
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Background
Nowadays, trendy research in biomedical sciences juxtaposes the term ‘precision’ to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media post...
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Background
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Citations
... Future research should address these challenges, exploring methods to refine safety constraints further, Quality & safety in the literature integrating more clinical considerations into the AI learning process and evaluating the impact of AI-based recommendations on patient outcomes. 25 This future research is vital for moving beyond theoretical safety assurances to practical, measurable improvements in patient care, crucial for bridging the gap between research and practice. 25 26 Implementing and assessing AI-based recommendations in live clinical environments will be crucial in ensuring that AI technologies can safely and effectively support healthcare providers. ...
... To achieve their goals, comprehensive real-time data are needed to learn about the health status of the public. In other words, they need a large variety of accessible data and sequencing genomics on a population level [36,40]. Ultimately, big data in precision medicine is key, emphasizing public data sharing to achieve precision public health and speed-accuracy-equity decisions made by the government for the sake of the public [39]. ...
... Thus, through precision medicine, we will eventually have less wastage of ineffective and costly therapies that patients must undergo to receive treatment. Furthermore, prediction modelling and forecasting can be used to determine the patient's exact diagnosis and optimum treatment, allowing healthcare providers to order only the required and curated medicines [40]. As a result, healthcare institutions are likely to change their way of dealing with pharmaceutical companies. ...
Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.
... Effective precision medicine depends on ensuring the data's correctness, security, and interoperability. 85 It remains challenging to distinguish between bacterial and viral infections, which frequently leads to inappropriate antibiotic use. 77 One of the biggest challenges to incorporating precision medicine into standard clinical treatment is the lack of advanced technologies. ...
Antimicrobial resistance (AMR) poses a significant threat to global health. It makes treating bacterial infections increasingly difficult. AMR arises from various mechanisms of antibiotic resistance including enzymatic inactivation, target alteration, efflux pumps, and decreased permeability. The limited and often ineffective treatments relying on antibiotics and their combinations result in increased morbidity and mortality. Therefore, it is essential to explore alternative methods for combating the challenge of AMR. In recent years, there has been a notable shift towards precision medicine in the battle against AMR. Precision medicine, characterized by its focus on individualized treatment tailored to patients’ specific genetic makeup, offers a paradigm shift in addressing AMR challenges. By pinpointing molecular targets responsible for infection, precision medicine enables more targeted and effective therapies, minimizing the risk of antimicrobial resistance development. Precision medicine can provide an alternative option to combat AMR by focusing on targets responsible for the infection. Bacteriophages and antimicrobial peptides (AMPs) are groups of antimicrobials that can serve as novel alternatives to antibiotics for combating the global antibiotic resistance challenge. They have the potential to be used as targeted therapy. Despite challenges such as limited host range, which refers to the specific bacteria they can infect, and regulatory concerns related to their approval and usage, bacteriophages have proven effective against bacteria causing infections. Meanwhile, AMPs provide a potential treatment approach against antibiotic-resistant bacteria due to their low molecular weight and broad-spectrum antimicrobial activity. AMPs can serve as a first line of defense against microorganisms. When used alone or combined with other biomaterials to increase therapeutic action, they can serve as a first line of defense against microorganisms. This review article aims to provide a comprehensive overview of the current understanding and clinical potential of bacteriophages and AMPs as alternatives to conventional antibiotics in addressing the pressing challenge of AMR.
... In conclusion, the integration of Big Data and multiomics approaches is revolutionizing health care by providing a more comprehensive understanding of the molecular mechanisms underlying various diseases. The integration of various omics data, clinical, and phenotypic information may aid in the development of personalized and effective diagnostic and treatment strategies [29,30]. ...
Big Data has become part of our everyday lives. Whether you’re using a smartphone, a fitness tracking device, or social media, almost everything in our life generates data points that are being stored, analyzed, and evaluated often without us even thinking about it. The amount of data produced and stored in the Global Datasphere grows vastly every year (Fig. 5.1). Big Data has become a buzzword in modern society. Its meaning keeps evolving and adapting to technological and societal change. The beginning of the use of the term was back in the 1990s most likely in connection with data visualization [1,2]. At first, the term only described large and complex data sets that could not be analyzed, stored, or processed with the then conventional techniques. Since then, the concept of the term has broadened to include more than just the data set, extending to the methods and techniques used to produce the data as well as the approaches used to store, analyze, and process the data [3].
... Since racial/ethnic disparities in AD are the consequence of social and structural inequities, there is a need to look beyond biological and genetic factors into other multidomain factors. [26][27][28] Several recent research investigations started the practice of employing data-driven psychosocial-behavioral phenotyping methods that incorporate multidomain data pertaining to health behaviors, social determinants of health, environmental resources, and psychological functioning. [29][30][31][32][33] These studies have revealed that (1) heterogeneity that shape or protect against pathologic aging outcomes of racially/ethnically diverse older adults. ...
INTRODUCTION
Identification of psychosocial‐behavioral phenotypes to understand within‐group heterogeneity in risk and resiliency to Alzheimer's disease (AD) within Black/African American and Hispanic/Latino older adults is essential for the implementation of precision health approaches.
METHODS
A cluster analysis was performed on baseline measures of socioeconomic resources (annual income, social support, occupational complexity) and psychiatric distress (chronic stress, depression, anxiety) for 1220 racially/ethnically minoritized adults enrolled in the Health and Aging Brain Study‐Health Disparities (HABS‐HD). Analyses of covariance adjusting for sociodemographic factors examined phenotype differences in cognition and plasma AD biomarkers.
RESULTS
The cluster analysis identified (1) Low Resource/High Distress (n = 256); (2) High Resource/Low Distress (n = 485); and (3) Low Resource/Low Distress (n = 479) phenotypes. The Low Resource/High Distress phenotype displayed poorer cognition and higher plasma neurofilament light chain; differences between the High Resource/Low Distress and Low Resource/Low Distress phenotypes were minimal.
DISCUSSION
The identification of psychosocial‐behavioral phenotypes within racially/ethnically minoritized older adults is crucial to the development of targeted AD prevention and intervention efforts.
... We will then apply logistic regression methods with a LASSO (least absolute shrinkage and selection operator) feature selector and machine learning approaches including naive Bayesian classification, decision trees, and support vector machines. Interpretability and clinical significance are critical to understand the impact of different attributes on the overall prediction and clinical translation and will be carefully considered in choosing the best fitting model [44]. The performance of the prediction models will be evaluated by calculating sensitivity, specificity, and area under the curve using 10-fold cross-validation. ...
... In addition, we will be able to compare our expert-informed traditional logistic regression model with models developed using machine learning methods. Even though models developed using machine learning techniques may be more accurate, they are also more difficult to interpret [44]. We will attempt to discern whether gain in performance is appreciable and if it outweighs the loss in interpretability. ...
Background
Hospital-induced delirium is one of the most common and costly iatrogenic conditions, and its incidence is predicted to increase as the population of the United States ages. An academic and clinical interdisciplinary systems approach is needed to reduce the frequency and impact of hospital-induced delirium.
Objective
The long-term goal of our research is to enhance the safety of hospitalized older adults by reducing iatrogenic conditions through an effective learning health system. In this study, we will develop models for predicting hospital-induced delirium. In order to accomplish this objective, we will create a computable phenotype for our outcome (hospital-induced delirium), design an expert-based traditional logistic regression model, leverage machine learning techniques to generate a model using structured data, and use machine learning and natural language processing to produce an integrated model with components from both structured data and text data.
Methods
This study will explore text-based data, such as nursing notes, to improve the predictive capability of prognostic models for hospital-induced delirium. By using supervised and unsupervised text mining in addition to structured data, we will examine multiple types of information in electronic health record data to predict medical-surgical patient risk of developing delirium. Development and validation will be compliant to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.
Results
Work on this project will take place through March 2024. For this study, we will use data from approximately 332,230 encounters that occurred between January 2012 to May 2021. Findings from this project will be disseminated at scientific conferences and in peer-reviewed journals.
Conclusions
Success in this study will yield a durable, high-performing research-data infrastructure that will process, extract, and analyze clinical text data in near real time. This model has the potential to be integrated into the electronic health record and provide point-of-care decision support to prevent harm and improve quality of care.
International Registered Report Identifier (IRRID)
DERR1-10.2196/48521
... This challenge is particularly important in sub-Saharan Africa (SSA) as the representation of individuals of population groups in teaching datasets may be skewed towards those of European ancestry since the large bulk of studies into the molecular patterns in breast cancer have been performed in this population group. Such a development is known as data bias, while societal bias may occur when older datasets or databases are used that classify data using outdated terms or classifications [24]. Societal bias can result in individuals that fall outside of certain classifications being ignored, resulting in misdiagnoses and poor treatment selection [25]. ...
The formidable impact of breast cancer extends globally, with South Africa facing pronounced challenges, including significant disparities in breast cancer screening, treatment and survival along ethnic and socioeconomic lines. Over the last two decades, breast cancer incidence has increased and now accounts for a substantial portion of cancers in women. Ethnic disparities in terms of screening, incidence and survival exacerbate the issue, leading to delayed diagnosis among Black patients and highlighting healthcare inequities. These concerning trends underscore the urgency of enhancing breast cancer screening while mitigating treatment delays, although obstacles within the health-care system impede progress. The intersection of breast cancer and human immunodeficiency virus (HIV) further complicates matters and particularly affects the Black population. Tackling the aforementioned disparities in breast cancer in South Africa mandates a multifaceted strategy. Robust screening efforts, particularly those targeting marginalised communities, are crucial for early detection. Concurrently, expe- dited treatment initiation is imperative. Addressing HIV-related complexities requires tailored interventions to ensure effective care. These multifaceted disparities require pan Afri- can research and cooperation as well as tailored interventions to enhance breast cancer care within the African region.
... The core problem to be solved by the use of AI for predictive modeling, encompassing disease forecasting, risk prediction, and spatial modeling, is the enhancement of accuracy, efficiency, and actionable insights in public health decision-making (33,34). Traditional methods in these domains often face limitations in handling the complexity of data, identifying patterns, and making accurate predictions. ...
Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.
... Many conditions or diagnoses cannot be clearly defined, leading to imprecise phenotyping of diseases and their treatments [45]. There is also substantial variability between experts who evaluate data, and this variability may result in biased labels. ...
Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.
... Clinical studies of diseases often suffer from biases due to demographic, social, genetic, and ethnic factors, leading to the underrepresentation of specific population groups (Prosperi et al., 2018). This underrepresentation hampers the generalizability of conclusions to a larger population, hindering the development of effective treatments (Kessler et al., 2016;Popejoy and Fullerton, 2016;Popejoy et al., 2018;Gurdasani et al., 2019). ...
This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.
