The birth of the computer has led to a host of technological advancements across every industry from engineering and architecture to education and health. Computers have played a key role in data collection and information storage and management in health care. Examples include patient medical histories, medicine inventories, diagnostic tools such as MRI radiation technology, computer tomography (CT), and more that were created to assist and simplify health care measures. Even the pharmaceutical sector has been revolutionized by computer technology.
Machine learning is a branch of artificial intelligence that allows systems to automatically learn and improve from experience without being programmed to do so. ML develops computer programs capable of accessing data and learning from it. The ML lifecycle delineates the role of each data science initiative and takes each project from inception to completion to produce a high-level perspective of how a data science project should be structured to achieve true business value.
ML algorithms are typically categorized as supervised or unsupervised algorithms. Supervised ML algorithms apply previous knowledge to new data using labeled examples to make predictions. This training model uses a training dataset to produce an inferred function and make predictions about the output values. Unsupervised ML algorithms use non-labeled datasets and study how systems can infer a function to describe hidden structures.
A core function of machine-learning engineering is machine-learning operations, or MLOPs. This collaborative function comprises data scientists, DevOps engineers, and IT and focuses on streamlining the process of taking machine-learning models to production where they’re maintained and monitored. PhData presents a realistic look at each position within what MLOps does and contributes and how it functions. Adopting an MLOPs approach increases the speed of model development and production by implementing continuous integration and deployment practices with monitoring, validation, and governance of ML models.
AI and the Pharmaceutical Industry
Research within the health care industry is carried out daily to discover new information about incurable diseases and conditions, to increase safety profiles of prescription drugs, to overcome drug resistance and therapeutic failure, and more. Continuous research results in a vast amount and variety of biomedical datasets necessary for drug design and discovery. This continuous accumulation of data has contributed to the advancement of ML in the pharmaceutical industry, specifically in drug design, data processing, and predicting treatment outcomes. ML can determine the cause-and-effect relationships between different data types that are normally overlooked by direct data evaluation. Deep learning takes a statistical approach to structure-based drug design and discovery, which aids in lead optimization and prediction of toxicity with precision in a matter of weeks.
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AI and Hospital and Retail Pharmacy
Artificial intelligence can reduce errors in disease diagnosis and drug therapy, predict and evaluate the risks of therapy, and predict therapy outcomes while reducing health costs and treatment time. AI is used in clinical pharmacy to predict treatment outcomes and risks. AI can use algorithms based on patient electronic medication history to predict overdose risk scores and minimize the risk of unintentional overdose.
AI in pharmacy has several use cases and applications in the pharmaceutical industry as well as in retail pharmacy. An example of an AI application is the delivery of personalized health care to patients via e-commerce portals, promotions, personalized communications via email, and more. ML models allow for the fast and accurate personalization of emails while chatbots increase the efficiency of service delivery.