Sollers College

Tag: artificial intelligence

  • Health Computing innovations in pharmacovigilance?

    Health Computing innovations in pharmacovigilance?

    Pharmacovigilance is the research and practices associated with the identification, evaluation, comprehension, and avoidance of side effects and other medication-related issues. As it assists in tracking adverse effects (AE), adverse drug reactions (ADR), and any other safety problems missed during clinical trials, it is a crucial part of the post-marketing monitoring program for pharmaceuticals and medical devices.

    Pharmacovigilance efforts provide vital safety data that helps identify any new and previously unknown dangers as well as information regarding the advantages and disadvantages of the drug in a variety of patient populations.

    Underreporting is prevalent, and it is easy to overlook crucial safety information even though most businesses use pharmacovigilance activities and have multiple systems in place.

    Data is gathered for the FDA Adverse Event Reporting Systems (FAERS) database, which is then used by the FDA to make decisions about new warning labels, withdrawal/limited use of the product in the market, and reporting by sponsors and manufacturers. These systems are part of the FDA’s voluntary reporting systems for pharmacovigilance, used by the World Health Organization (WHO).

    Pharmacovigilance is a vital aspect of the therapeutic process yet it is often overlooked by sponsors, patient advocacy organizations, and pharmaceutical companies.

    Underreporting, misreporting, missing information, and inconsistencies in safety data are widespread due to voluntary and non-standard entry into systems. If sophisticated analytical approaches aren’t used, it might be challenging to analyze trends and patterns from a vast amount of data and come to relevant conclusions. Health computing innovations facilitate the gathering and analysis of safety data and adverse events, making them valuable tools for enhancing pharmacovigilance.

    Innovations in health computing make use of software, sensors, networking, and computer platforms for medical purposes. These consist of wearable technology, telehealth and telemedicine, mobile medical apps (mHealth), artificial intelligence (AI), and machine learning (ML).

    Pharmacovigilance can benefit from health computing in two key manners

    Wearable gadgets for data collection

    Health computing wearables such as smartphones, smartwatches, fitness trackers, and mobile apps are characterized by innovations in real-time data gathering and monitoring that enable the early detection, identification, and classification of adverse occurrences. This makes it possible for producers and regulatory bodies to act quickly to reduce any possible concerns.

    Social media and internet platforms

    Patients and caregivers provide information and safety concerns to online communities and social media platforms, which act as a valuable archive. The fact that patient-provided data is directly inputted and can be used to spot patterns in safety information about specific medication classes and medical equipment makes it significant.

    Electronic health records are a valuable source of information for identifying adverse drug reactions and for facilitating cross-functional information exchange with a network of healthcare providers, facilitating the tracking and correction of safety information. EHRs have more complete data that can be useful in comprehensively analyzing adverse events. Examples of this data include the time of medication administration, the development of symptoms, and a detailed clinical history.

    Telehealth identifies and promptly communicates safety issues of patients, telehealth platforms that allow for remote visits and patient monitoring instead of in-person consultations are essential. This approach, which can be connected with EHRs, enables doctors to recognize adverse occurrences and make the necessary adjustments, such as stopping a prescription or changing the dosage.

    Machine learning (ML) and artificial intelligence (AI) for analyzing data are used to sift through vast amounts of data produced by wearables and analyze them to find patterns and trends in safety data. AI enables signal detection to extract information about possible adverse drug occurrences and processing of pharmacovigilance data.

    On data gathered from social media sites, data mining and prediction employing AI and ML are also conducted. They also aid in the creation of predictive models.

    Data integration enables the combination and analysis of data from many sources, such as AI and cloud computing, which are extremely beneficial to the field of pharmacovigilance. These technologies are also essential for maintaining data security and privacy.

    NLP allows the extraction of structured safety and adverse event data from text-based documents, including social media platforms, electronic health records, and narrative reviews.

    Big data analytics is crucial for deriving algorithms on safety issues. This data is helpful for real-world evidence studies.

    The use of health informatics is crucial for efficient pharmacovigilance. It enables the accurate management of large amounts of data, making it easier for regulatory bodies to identify safety information and safeguard patient health.

  • What strategies can pharma companies use to accelerate drug development?

    What strategies can pharma companies use to accelerate drug development?

    Clinical trial efficiency may be enhanced by artificial intelligence (AI) enabled systems that enhance patient and location acquisition. Three Strategies Artificial Intelligence Can Enhance Clinical Research Advancements.

    AI can increase clinical trial diversity. 

    The population’s lack of access to basic medical treatment is alarming. The patient population encounters noteworthy obstacles when it comes to engaging in clinical trials, primarily transportation, time, and financial constraints to trial locations.

    Clinical trials can also result in a partial or skewed understanding of the safety and effectiveness of drugs in certain populations, which could have detrimental effects on the general public’s health.

    To solve these problems, significant efforts must be made to guarantee that clinical trials are inclusive and representative of the diverse patient population, as well as to expand access to healthcare for marginalized communities.

    Researchers and physicians must consider the influence of genetic variation on medication metabolism and treatment results. This is because varying patient populations may require customized doses and treatment regimens. A high-quality healthcare system for all patients is reflected in clinical research.

    Numerous clinical trials study initiations were postponed, and remote monitoring became more prevalent. Many industry stakeholders are pushing for the continued use of digital technology to reach a wider range of patients.

    Clinical trials are becoming more diverse and decentralized with the help of AI-enabled technology.

    Listed below are some instances. 

    • AI-powered patient IDs based on clinical characteristics quickly find possible subjects for clinical and observational research.
    • Remote patient monitoring using AI capabilities decreases the frequency of visits to the trial site, which increases participant retention while gathering objective, real-world health data.
    • Collaboration powered by AI improves clinical data exchange and reduces the time to diagnosis by gathering and analyzing vast amounts of data from hospital hubs and spoke networks.

    AI can boost monitoring accuracy and speed.

    • Most research teams employ manual techniques in the screening process for clinical trial volunteers, which increases the likelihood of human error and delays the process.
    • The challenge of reaching this goal is exacerbated by the tremendous problems sponsors have been having persuading their usual research sites to take part in clinical trials. This is partially explained by the growing need for clinical trials.
    • Efficiency in recruiting participants for clinical trials can be increased by using AI to perform real-time automated eligibility checks.

    AI has the potential to bridge the gaps between clinical care and trial research.

    • Artificial Intelligence is expediting the per-screening of clinical trial candidates by automating the analysis of hospital imaging at sites and referral facilities.

    Once a candidate has been found, AI software can use a communication system to spread their information. Subsequently, the enrollment procedure can be optimized and made more efficient.

  • Safeguarding drug safety with technological innovations?

    Safeguarding drug safety with technological innovations?

    Artificial intelligence generates a more accurate reporting system for increased drug safety by processing large amounts of diverse data in an organized way.

    The drug safety data monitoring and reporting process can be made easier with artificial intelligence.

    As safety technology advances, AI is increasingly used in case processing for intake, validation, and coding. This is to support case processors or automatically process cases.

     Data entry can be automated, results can be produced quickly, errors can be reduced, and clinical documentation can be understood and classified using AI.In order to comply with regulations, pharmaceutical sponsors are responsible for collecting and reporting safety data.

     By using natural language processing (NLP) to automate case intake, AI can help extract and aggregate large data sets. Businesses need faster case capture to report problems and implement preventative changes. 

    In addition to reducing the data entry costs associated with case intake, these AI technologies also do so significantly.

    • AI tools can instantly analyze both structured and unstructured data. 
    • NLP tools analyze intricate descriptions, including medical charts, social networking posts, documents, and other unstructured data. 
    • Pharmacovigilance query tools automate case documentation submission and handling. 
    • AI-driven automation speeds up the process by supplementing or replacing manual tasks, thus completing reviews more thoroughly than human reviewers under time constraints.

    Artificial Intelligence in drug discovery

    AI’s ability to run numerous analytical techniques in real-time and evaluate data from various perspectives demonstrates AI’s significance in drug development. 

    AI has many uses in the clinical, administrative, and research spheres for safety assessments in pharmaceutical development. There are difficulties when using AI.

     AI-enabled products may produce inaccurate, even harmful, treatment recommendations. 

    Machine learning software can analyze data generated from clinical trials faster and more accurately, producing results that, again, are checked. These errors can be caused by unexpected sources of bias in the information used to build or train the AI. 

    In addition, they can be caused by the inappropriate weight given to certain data points.

    Artificial intelligence-supported data analysis allows pharmaceutical companies to reroute funds to create and distribute better drugs. 

    Image recognition and natural language processing can be used to enhance drug study data quality. 

    With recent advancements in big data analytics and cloud-based pharmacovigilance platforms, it will be possible to analyze large datasets from real-world experiments more sophisticatedly. 

    As well as reducing human error, AI can help identify trends and patterns, as well as speed up risk assessment processes.

    Post-marketing AI 


    Based on safety data gathered after approval to safeguard patients, AI can help pharmaceutical companies research, learn, and forecast the changes to already-available products. 

    The results may indicate previously unidentified effects of long-term medication use and may motivate adjustments to dosage or patient education.

    AI and machine learning help drug sponsors gather information and create practical solutions to adverse events in post-marketing safety data. 

    NLP techniques also use AI and computational linguistics methodologies. To categorize events as meaningful or not, qualitative models use expert judgment. Because they aid in determining the underlying cause of events and whether they result in significant events, such as side effects, causal models may be a better fit for post-approval changes.

    A machine- or AI-run causal analysis that examines all post-approval events may spot issues.

    AI offers higher-quality data to regulatory bodies. This enables easy transmission of clean data to internal teams in an easy format. This allows them to concentrate on analysis rather than data collection and extraction. 

    AI can accelerate reporting by using AI to identify potential signals earlier, giving analysis teams more time to make the right decision.

    New AI developments

    Optical character recognition (OCR) transforms handwritten and typed text into machine-readable text. Other AI applications employed in pharmacovigilance include RPA, autonomous software, desktop automation, NLP, speech-to-text conversion, and natural language understanding (NLU). 

    They are used to collect data on adverse drug reactions (ADRs), boost efficiency, speed, and scalability, and cut costs. FastText, the long-short-term memory recurrent neural network (LSTM), and the convolutional neural network (CNN) are a few of the neural networks and deep learning models used to produce real-world data from ADRs.

     There is potential to standardize and streamline the entry of ICSR [individual case safety report] data into a pharmacovigilance system by utilizing various combinations and integrations of these currently available technologies.

    The automation of pharmacovigilance tasks using technologies like blockchain, rule-based robotic process automation (RPA), cognitive machine learning, and chatbots. 

    Both authorities and the life sciences industry are on an education path to determine appropriate use cases, GxP validation, and quality assurance. This is in a highly regulated environment. Pharmacovigilance teams can filter the information using most AI technologies currently on the market to spot trends and send signals.

    Although AI in drug safety operations is still developing, conducting risk-benefit analyses, and using AI to analyze substantial data can help sponsors identify drug-event associations and predict positive or negative effects.

     The intelligence offered by these signals is invaluable in the real world and cannot be obtained by data mining from controlled clinical trials. NLG technologies can produce aggregate reports or their basic framework, freeing up human experts to conduct additional analysis and finalization.

    Pharmacovigilance uses NLP applications to comprehend and categorize information about post-marketing adverse events from various sources, including patients, healthcare professionals, and clinical trials.

    Unstructured clinical notes on patients can be analyzed by NLP systems. This provides amazing insight into how to assess quality, improve procedures, and enhance patient outcomes. 

    Natural language and image classification modeling have undergone some amazing improvements. These advancements may help pharmaceutical development and safety. Additionally, causal, and qualitative models provide a lot of value, and they continue to improve.

    With Sollers, you are guaranteed to learn the necessary skills, competencies, and other qualities needed for a career.

  • Optimize signal management with artificial intelligence and machine learning

    Optimize signal management with artificial intelligence and machine learning

    • The life sciences industry is constantly growing and searching for novel ways to improve pharmaceutical efficacy and security.
    •  Safety signals require appropriate actions to reduce drug safety risks. From a patient safety and regulatory standpoint, the benefit-risk profile of a drug can be established through signaling and risk management activities.
    • Signal detection and risk management can be transformed with artificial intelligence and machine learning by streamlining the process. This will enable quick, automated signal detection and better quality control and collaboration.
    • In the past, manual and semi-automated methods based on statistical analysis have been used to detect PV signals. 
    • Due to their limitations of quantitative data and conventional statistical tools, which cannot capture all types of patterns in enormous amounts of complex, heterogeneous data, these methods are not always successful at identifying signals that require further attention. They are effective at generating signals, but they do not often succeed in identifying signals that require further attention.

    An evaluation of AI-powered and conventional signal management techniques

    By combining algorithms to generate statistical scores on many safety cases and usability needed for modern web applications, we can transform the signal and risk management of drugs, cosmetics, vaccines, and medical devices. 

    Using line listings and existing knowledge, we can identify qualitative signals with a domain-centric approach.

    mSignal AI makes predictions based on a composite signal score using Bayesian and frequentist statistics. Regulatory agencies, clinical trials, and literature sources are examined for relevant clinical flags for product-event combinations to produce this score.

    The shift to active surveillance: why drug event combinations must be monitored and gathering information on potential safety risks will be a significant task for signal management in the future. 

    With “Data as a Service” for the most thorough active surveillance, mSignal AI is at the forefront of this change, regularly updating data from publicly accessible sources like FAERS, EMA, and others. 

    To monitor the purpose of monitoring Drug Event Combination (DEC), mSignal AI provides both real-time (data-on-the-go) and passive surveillance. 

    The system includes a drag-and-drop user interface that makes it simple to customize views, alerts, and notifications. As a result, signal AI is a flexible and effective tool for signal management. It also integrates variables from various databases.

    Artificial intelligence will grow as the life sciences sector expands and changes. 

    Tracking and Classification

    Safety alert severity levels can be accurately identified and classified with AI-based methods. A significant amount of historical and real-world data on safety issues can be obtained by machine learning algorithms. Advanced analytical techniques allow for better grouping of data and almost perfect identification of associations.

    With AI, signal analysts can make wise observations using data-driven algorithms. Signal selection and prioritization can be predicted by multivariable logistic regression analysis. Data patterns can be spotted by AI, thereby improving timeliness and completeness.

    Using artificial intelligence for signal detection is an effective method because safety experts are likely to receive fewer trial data than in the past and more real-world data.

    Improved assessment of the risk-benefit ratio


    Risk evaluators are expanding the number and variety of datasets with which they conduct signal detection to produce better risk-benefit profiles and shorten the time needed to produce such profiles. They, therefore, require ML algorithms to comprehend signal score variations across various datasets, including the technique for summing them.

     In comparison to a single case, aggregated case data offers a more comprehensive picture of the signals. This aggregated data enables signal discovery—the search for patterns that point to unknown risks.

    More advanced statistical analysis

    In recent research, multiple data streams have been combined to get a more comprehensive picture of specific safety issues that have been discovered in the same or related databases.

    Data inputs are constantly increasing, which causes analytics capabilities to advance to utilize these new data sources. Reviewers can locate cases of interest by prioritizing those that have been flagged as high-risk and by using analytics that is quicker and more precise.

    Sollers College employs industry-experienced faculty to teach its signal management program.

    Using the latest technology and gaining valuable hands-on experience, this program is an excellent way to quickly gain a skill that is in high demand by using the latest technologies and building on practical experience.

  • Why Machine Learning Is the Next Big Thing

    Why Machine Learning Is the Next Big Thing

    With the help of artificial intelligence (AI), computer programs may continuously improve their performance by learning from their experiences with data. By employing algorithms that learn incrementally, it automates analytics. Rather than relying on rule-based programming, it uses a basic self-learning approach instead. So this technology has become a part of our daily lives, whether it’s helping us find our way across town or advising us on how to make the most significant investments. As a result, machine learning is essential since it affects how easy it is to live or make decisions. It has become so ingrained in our everyday routines that you may not even realize it. 

    What are the benefits of using machine learning in a business?

    Machine learning may be used to solve problems and bring value to a company. Personalized suggestions may be made more effective by using this information in marketing and segmentation techniques. Conversion rates may be predicted with more accuracy using machine learning models built on various marketing indicators. 

    By grouping products, the unsupervised learning approach of a machine learning algorithm finds more purchase patterns. 

    A wide range of sectors and applications benefit from machine learning, which improves customer experience and increases the value of investments (ROI). ML may reduce downtime by creating predictions in online searches that provide sensible results.  

    The application of NLP algorithms in AI chatbots enables them to serve as very effective self-learning customer service representatives. In this way, resources may be used more efficiently, and a new channel for consumer analytics can be opened up.

    Trends and potential for a future dominated by machine learning

    Data flywheel, algorithm marketplace, and cloud-hosted intelligence are three themes that are likely to affect the future of machine learning. An algorithm marketplace has been formed by scaling up machine learning algorithms to profit from shared algorithmic knowledge. 

    Machine learning platforms deliver pre-trained models as a subscription service because of the economies of scale.

    Machine learning is projected to be widely used in marketing, finance, and healthcare in the following years.  Predictive suggestions and demand forecasting are made more accessible with this marketing information. Fraud detection and risk management will be made more accessible with the help of machine learning in the financial sector. Machine learning will find its most major use in healthcare, where the outcomes can alter people’s lives.

    Using Machine Learning and Data Science, make data-driven decisions. A certification from Sollers in data science and machine learning will provide you with the skills you need to succeed in any organization’s data science endeavors.

  • How to become a Machine Learning Expert?

    The emergence of Machine Learning is going to be very bright in the coming years. As the dimensions of technology change day by day, a new revolution is taking over the world, which will be the future of ML.

    There is one significant reason why data scientists need machine learning? For High-value forecasts, oversee better decisions and smart actions in real-time without human intervention.

    The nearly infinite quantity of available data, affordable data storage, and the growth of small, expensive, and more powerful processing have propelled machine learning growth. 

    Many enterprises are developing more strong machine learning models to analyze bigger and more complex data while achieving faster, more accurate results on vast scales. Machine learning tools allow organizations to identify profitable opportunities and potential risks more quickly.

    It uses specific statistical algorithms to perform computers’ work in a certain way without being explicitly programmed. The algorithms take an input value and predict an output for this by using certain statistical methods. The main purpose of machine learning is to build intelligent machines which can think and work like human beings.

    The practical significance of machine learning drives business results which can dramatically affect a company’s bottom line. New techniques in the field were growing rapidly and extended machine learning to nearly limitless opportunities. Industries depend on vast quantities of data and need a system to analyze it accurately and efficiently, and have embraced machine learning as the best way to build models, strategize, and plan.

     Traditional statistical explications are more focused on static analysis confined to analyzing samples that are frozen in time. Enough, this could result in inaccurate conclusions.

    Machine Learning is coming as a solution to all this chaos. Proposing smart alternatives to analyze vast data volumes ML is a leap forward from statistics, computer science, and other emerging applications in the industry. Machine learning can produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for this data’s real-time processing.

    How to become a Machine Learning Expert?

    To become a specialist in Machine Learning, every Data Scientist must have the below 4 skills. In several ways, a machine learning engineer is the same as a programmer. The prime difference is the machine learning expert requires to generate programs that allow machines to self-learn and deliver results without human interruption. In general, there are a variety of positions that a machine learning engineer might play.

    • Expertise and thorough knowledge in computer fundamentals such as system architecture and layers, application software, and computer organization.
    • Detailed understanding of probability is fundamental because Data Scientists’ work includes a lot of estimation. 
    • Analyzing statistics is a different area that they need to concentrate on.
    • Data modeling for analyzing multiple data objects and how they associate with each other.
    • Programming skills and profound knowledge of programming languages like python and R. 
    • A crusade for learning new database languages like NoSQL apart from Oracle and traditional SQL.

    Sollers College aims to help those who want to begin their career in the IT and Life Science sectors. A certification in Machine Learning with Python Training can maximize your career opportunities.  So why late? Start your journey with Sollers now.

  • Industrial Trends of Artificial Intelligence

    Industrial Trends of Artificial Intelligence

    Before the global pandemic struck, the world was turned on its head towards artificial intelligence (AI), especially the branch of AI known as machine learning (ML), which was already producing extensive disruption in almost every industry.

    AI unquestionably resides a key trend in choosing the technologies that will transform how we live, work, and play shortly. During recent years, there have been several discoveries in machine learning and AI.

    The AI-ML industry is developing quickly and gives sufficient advancement scope to companies to bring necessary development. According to Gartner, about 37% of all companies analyzed are utilizing some ML in their business. It is anticipated that about 80% of modern improvements will be founded on AI and ML by 2022.

    With the rush in demand and interest in these technologies, various new patterns are rising during this space. Solely if you’re a tech proficient or related to innovation in some capacity, it’s exciting to see what’s next inside the area of machine learning.  Here’s a summary of what we can anticipate during what will be a year of refurbishing our lives and rethinking business strategies and preferences

    Machine Learning In Hyper Automation

    Hyper Automation, an IT mega-trend recognized by Gartner, is the incident that almost anything inside a company that can be automated–, for example, legacy business methods – should be automated. The pandemic has increased the theory’s adoption, which is otherwise called digital process industrialization” and “intelligent process automation.”

    Business Forecasting and Analysis

    The time series investigation has been mainstream for recent years and is a hot model for the current year. With this approach, experts gather and screen a set of data over a period that is then examined and utilized to make smart decisions. The ML networks can give conjectures with efficiency as high as around 95% whenever trained using diverse data sets.

    Automation

    The year 2021 will achieve new patterns in technology, and hence the failure to establish reasons for enhanced technology debt for companies. Enterprise budgets will keep on seeing action from IT to more critical business operations. The center of software development & data tech spending will be on the implementation of AI. 

    The Intersection of ML and IoT

    The Internet of Things has recently been a fast-developing segment. Economic analyst Transforma Insights forecasts that the worldwide IoT market will develop to 24.1 billion devices in 2030, producing $1.5 trillion in income.

    To be in the competitive race accelerate your career with this acclaimed SAAS certification. Gain in-demand skills to open doors to your future with our Academic Specialization and get your digital badge created. Arm yourself with the latest tools and technologies from Sollers

×