Polypharmacy in elderly cancer patients : challenges and the way clinical pharmacists can contribute in resource-limited settings

The aim of this study was to address the problems associated with polypharmacy in elderly cancer patients and to highlight the role of pharmacists in such cases in resource‐limited settings. A narrative review of existing literature was performed to summarize the evidence regarding the impact of polypharmacy in elderly cancer patients and the pharmaceutical strategies to manage it. This review emphasizes the significance of polypharmacy, which is often ignored in real clinical practice. Polypharmacy in the elderly cancer population is mainly due to: chemotherapy with one or more neoplastic agents for cancer treatment, treatment for adverse drug reactions due to neoplastic agents, the patient's comorbid conditions, or drug interactions. The role of the clinical pharmacist in specialized oncology hospitals or oncology departments of tertiary care hospitals is well established; however, this is not the case in many developing countries. A clinical pharmacist can contribute to solving the problems associated with polypharmacy by identifying the risks associated with polypharmacy and its management in resource‐limited settings. As in many developed countries, the involvement of a clinical pharmacist in cancer care for elderly patients may play a vital role in the recognition and management of polypharmacy‐related problems. Further research can be conducted to support this role

Social media mining for identification and exploration of health-related information from pregnant women

Widespread use of social media has led to the generation of substantial amounts of information about individuals, including health-related information. Social media provides the opportunity to study health-related information about selected population groups who may be of interest for a particular study. In this paper, we explore the possibility of utilizing social media to perform targeted data collection and analysis from a particular population group -- pregnant women. We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information. To identify potentially pregnant women, we employ simple rule-based searches that attempt to detect pregnancy announcements with moderate precision. To further filter out false positives and noise, we employ a supervised classifier using a small number of hand-annotated data. We then collect their posts over time to create longitudinal health timelines and attempt to divide the timelines into different pregnancy trimesters. Finally, we assess the usefulness of the timelines by performing a preliminary analysis to estimate drug intake patterns of our cohort at different trimesters. Our rule-based cohort identification technique collected 53,820 users over thirty months from Twitter. Our pregnancy announcement classification technique achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user timelines. Analysis of the timelines revealed that pertinent health-related information, such as drug-intake and adverse reactions can be mined from the data. Our approach to using user timelines in this fashion has produced very encouraging results and can be employed for other important tasks where cohorts, for which health-related information may not be available from other sources, are required to be followed over time to derive population-based estimates.Comment: 9 page

Leveraging FAERS and Big Data Analytics with Machine Learning for Advanced Healthcare Solutions

This research study explores the potential of leveraging the FDA Adverse Event Reporting System (FAERS), combined with big data analytics and machine learning techniques, to enhance healthcare solutions. FAERS serves as a comprehensive database maintained by the U.S. Food and Drug Administration (FDA), encompassing reports of adverse events, medication errors, and product quality issues associated with diverse drugs and therapeutic interventions.By harnessing the power of big data analytics applied to the vast information within FAERS, healthcare professionals and researchers gain valuable insights into drug safety, discover potential adverse reactions, and uncover patterns that may not have been discernible through traditional methods. Particularly, machine learning plays a pivotal role in processing and analyzing this extensive dataset, enabling the extraction of meaningful patterns and prediction of adverse events.The findings of this study demonstrate various ways in which FAERS, big data analytics, and machine learning can be leveraged to provide advanced healthcare solutions. Machine learning algorithms trained on FAERS data can effectively identify early signals of adverse events associated with specific drugs or treatments, allowing for prompt detection and appropriate actions.Big data analytics applied to FAERS data facilitate pharmacovigilance and drug safety monitoring. Machine learning models automatically classify and analyze adverse event reports, efficiently flagging potential safety concerns and identifying emerging trends.The integration of FAERS data with big data analytics and machine learning enables signal detection and causality assessment. This approach aids in the identification of signals that suggest a causal relationship between drugs and adverse events, thereby enhancing the assessment of drug safety.By analyzing FAERS data in conjunction with patient-specific information, machine learning models can assist in identifying patient subgroups that are more susceptible to adverse events. This information is instrumental in personalizing treatment plans and optimizing medication choices, ultimately leading to improved patient outcomes.The combination of FAERS data with other biomedical information offers insights into potential new uses or indications for existing drugs. Machine learning algorithms analyze the integrated data, identifying patterns and making predictions about the efficacy and safety of repurposing existing drugs for new applications.The implementation of FAERS, big data analytics, and machine learning in advanced healthcare solutions necessitates meticulous consideration of data privacy, security, and ethical implications. Safeguarding patient privacy and ensuring responsible data use through anonymization techniques and appropriate data governance are paramount.The integration of FAERS, big data analytics, and machine learning holds immense potential in advancing healthcare solutions, enhancing patient safety, and optimizing medical interventions. The findings of this study demonstrate the multifaceted benefits that can be derived from leveraging these technologies, paving the way for a more efficient and effective healthcare ecosystem

Improving Patient Care with Machine Learning: A Game-Changer for Healthcare

Machine learning has revolutionized the field of healthcare by offering tremendous potential to improve patient care across various domains. This research study aimed to explore the impact of machine learning in healthcare and identify key findings in several areas.Machine learning algorithms demonstrated the ability to detect diseases at an early stage and facilitate accurate diagnoses by analyzing extensive medical data, including patient records, lab results, imaging scans, and genetic information. This capability holds the potential to improve patient outcomes and increase survival rates.The study highlighted that machine learning can generate personalized treatment plans by analyzing individual patient data, considering factors such as medical history, genetic information, and treatment outcomes. This personalized approach enhances treatment effectiveness, reduces adverse events, and contributes to improved patient outcomes.Predictive analytics utilizing machine learning techniques showed promise in patient monitoring by leveraging real-time data such as vital signs, physiological information, and electronic health records. By providing early warnings, healthcare providers can proactively intervene, preventing adverse events and enhancing patient safety.Machine learning played a significant role in precision medicine and drug discovery. By analyzing vast biomedical datasets, including genomics, proteomics, and clinical trial information, machine learning algorithms identified novel drug targets, predicted drug efficacy and toxicity, and optimized treatment regimens. This accelerated drug discovery process holds the potential to provide more effective and personalized treatment options.The study also emphasized the value of machine learning in pharmacovigilance and adverse event detection. By analyzing the FDA Adverse Event Reporting System (FAERS) big data, machine learning algorithms uncovered hidden associations between drugs, medical products, and adverse events, aiding in early detection and monitoring of drug-related safety issues. This finding contributes to improved patient safety and reduced occurrences of adverse events.The research demonstrated the remarkable potential of machine learning in medical imaging analysis. Deep learning algorithms trained on large datasets were able to detect abnormalities in various medical images, facilitating faster and more accurate diagnoses. This technology reduces human error and ultimately leads to improved patient outcomes.While machine learning offers immense benefits, ethical considerations such as patient privacy, algorithm bias, and transparency must be addressed for responsible implementation. Healthcare professionals should remain central to decision-making processes, utilizing machine learning as a tool to enhance their expertise rather than replace it. This study showcases the transformative potential of machine learning in revolutionizing healthcare and improving patient care

Pharmacovigilance: A practical approach for reshaping patient safety

Ensuring patient safety and efficacy of medicines is one of the top challenges in healthcare today. Numbers of adverse effects, drug-interactions and risk factors have been reported later in the years of drug release and hence post-marketing surveillance is today's need. Pharmacovigilance is the important and integral part of clinical research dedicated to reduce the risk of drug-related harms to patient. Pharmacovigilance encompasses the science and practice related to the detection, assessment, understanding and prevention of adverse effects of drugs or any other possible drug-related problems. It has been regarded as a type of continual monitoring of unwanted effects, adverse drug interactions and other safety-related aspects of drugs, which are already placed in markets. The objective of the present article is to provide a succinct review on the importance of Pharmacovigilance practice in establishing and maintenance of rational use of drugs within the ambit of pharmacotherapy. The promotion of systematic and rational use of drugs requires the reporting of adverse events of medicine and proper implementation from everyone in healthcare sector. Thus, in summary this review attempts to stress that systematic pharmacovigilance is essential to build up reliable information network on the safety of medicines to boost confidence about their safety.The scientists, clinicians, pharmaceutical manufacturers, drug developers, regulators, public policy makers, patients and the general public all have their own complementary roles in achieving what is envisaged

Automation in pharmaceutical sector by implementation of artificial intelligence platform: a way forward

Worldwide, there are technological advances that swift automation in several aspects of the pharmaceutical industry such as pharmacovigilance, clinical research, medical affairs, and marketing. Innovative technology like artificial intelligence (AI) emphasizes the massive use of the internet for drug development, drug safety, data analytics, communication marketing, and customer engagement to achieve the goal of pharmaceuticals and patient-centric healthcare. Presently, escalating the number of individual case safety reports (ICSRs) necessitate the support of AI in the transformation of drug safety professional. AI can be transformed and evolve the clinical trial process from the conventional method alongside benefited the cutting cost, enhancing the trial quality, and alleviate trial time by almost half. Today, AI may be efficiently implemented to lower the cost of medical information requests, besides the online chatbots to communicate with health care professionals (HCPs) and consumers. There are numerous forthcoming uses of AI which need to be executed for renovation in the field of pharmaceuticals