Pharmaceuticals and Artificial Intelligence
In various fields like Telecommunication, Healthcare, Insurance, Manufacturing, and others,
we process Petabytes of scale data to give an idea. Likewise, the Pharmaceutical industry
is also being aided by it for effective functioning. Petabytes of scale data help to solve
multiple business processes and improve efficiency across the board.
Research and development
Discovery from drug to real life use
Identify sources of clinical data
Integrate data into big data
Link datasets for research
Gain input about various drugs
Collect info about genetic details, personality
traits, disease status
Analyze if patient is fit for clinical trial
Perform shorter and cheap trials
Use predictive modelling for drug discovery
Easily predict drug allergies, toxicity or
Use sentiment analysis to read drug reactions
Use natural language processing
Gather information about any reactions
Simplify drug reactions
Enable precise medicine
Diagnosis and treatment of orders
Develop personalized medicinessuitablefor an
Predict susceptibility to certain disorders and
enhance disorder detection
Sales and Marketing
Analyze the geographical locations with
maximum promoted medicines
Make key decisions in marketing and sales
Analyze about customer behavior
Analyze ad campaigns and customer retention
Perform predictive analysis for industry trends
How Sparkflows aids in Pharma
PRE PROCESSING DATA
The process of cleaning data consumes an enormous time manually but Sparkflows makes data preparation and cleaning fast and easy.
STABLE MACHINE AND DEEP LEARNING TECHNOLOGIES
Sparkflows takes help from common open source libraries and toolkits to provide strong and dependable understanding of machine and deep learning resources.
After the project is ready, Sparkflows helps users to test the accuracy of prediction by using cross validation that divides data into two subsets- Training and Testing.
PRODUCE MODELS THAT DRIVE VALUE
Sparkflows provides models with real data that reflects the actual population where the drug would be used.
Data Analytics and AI for improved Drug Developement
Diaognosis & Identification of diseases
Sparkflows helps solve the biggest challenge of diagnosis and identification of diseases by machine learning development.
Sparkflows uses machine learning and predictive analytics in customizing treatment to a person's unique medical history.
Drug discovery and manufacture
Sparkflows uses machine learning for early drug discovery like new drug compounds, discovery technology, next generation sequencing and more.
Sparkflows makes this process very easy by using predictive analysis on a wide range of data to target populations more quickly.
Electronic Health Records
Sparkflows supports vector machines and optical character recognition as essential components of machine learning systems for document classification.
Specific Use Cases of Sparkflows
Accurately identifying patients for clinical trials
Data science, machine learning, and AI can help to quickly and precisely identify patients who would be fit for a particular trial via advanced analysis of medical records through natural language processing (NLP) or by exploring geographically. These techniques can examine the interactions of potential trial members’ specific biomarkers and current medication to predict the drug’s interactions and side effects, avoiding any potential complications.
The Future of Computational Biochemistry
Computational biochemistry allows drug-makers to cut down a great portion of the test tube experiments. Instead, a computer is used for the protein and tests all of its atomic interactions. Such an analysis will allow researchers to take to the next stage of testing with a smaller list of leads. Deep learning models drastically save the expenses of Stage 1 trials.
Supply chain and Manufacturing.
Pharmaceutical companies can better forecast demand and distribute products more efficiently through the use of Data science, machine learning, and AI techniques. For manufacturing, pharmaceuticals can use machine learning to control the cost of equipment maintenance and make the way for self-maintenance through artificial intelligence. Predictive maintenance is widely used in any business with high-capital assets.
An easy option for pharmaceutical companies can be to leverage machine learning techniques to cut down through literature and journal publications using NLP and also to pre-screen for the most effective potential compounds to prioritize their time.