Co-author: Chetan Kamath

Big data refers to data that is so large and complex, it is difficult to handle using traditional methods. However, with the advent of advanced computing techniques, the handling of huge amounts of data has become much easier and a separate branch of study called Big Data Analytics has been assigned for this purpose. With its ubiquitous usage today, businesses have expanded with the development of new products and smart decision making. As dealing with size is a major human constraint, the automation of processing torrents of data has proved to be a viable option against human intervention. It reduces operational costs, improves operational efficiencies, enhances self-service modules, and increases the scalability of the business – bringing down weeks of analyses to mere hours.

Some industries, typically healthcare, has been slower to adopt these technologies. However, things are changing of late and organizations outsource their data to firms specializing in data.

There are numerous areas in the life science industry that uses Artificial Intelligence (AI) effectively today.

In this blog we discuss how two prominent facets of recent advancements in computing pave the way for the benefits of life sciences reach a far greater audience. Complementing data in modern technology is a plethora of automation tools that have come up, to save monotonous labor and its associated costs. It frees up human resources to engage in more productive decision making and innovative efforts. Right from making data entries to developing modules that simplify long repetitive processing tasks, entire labor-intensive jobs can be automated with the use of robots, sensors, and transducers. A single breakthrough in a research effort can be the starting point of mass production of a drug, making it a highly worthwhile investment.

Challenges faced currently

Practically speaking, there isn’t an ideal model that can be built out of big data that brings about a perfect understanding of where things are headed or what overall sentiment the data represents.

• There is inevitably a trade-off between the goodness of fit the model achieves and the accuracy with which it can predict the turn of events, or describe what gives the best results.
• Models often suffer from overfitting, not having an optimized algorithm, or not taking the right mix of available data, to begin with – more so in the hands of inexperienced professionals.
• A number of events and analyses are not deterministic as a model would imply, but probabilistic.
• True events will inevitably occur with an erring on side or the other without ever an ‘exact match’ coming out in reality. While most risk-bearing decisions turn out to be for the best, a few failures among the lot are inevitable.
• Although AI has boomed and completed its inception stage, it remains at an explorative stage where different aspects of the technologies are still being tested. It can replace human involvement ordinarily requiring intelligence such as visual perception, speech recognition, decision making, and language translation.

Solutions to overcome the challenges:

While the idea of AI may seem complex and imperfect, its end goal is actually to make life simpler.

• In the life sciences industry, data encompasses all spheres of activity, be it in molecular biology or the responsiveness of patients to drugs. Data serves to find the best combinations workable in each case, to filter out defects, and to lay the foundation for best practices. We have well-tested tools now in the market for creating this intelligence and processing of big data – and minimize risks and costly mistakes.
• Greater computing power means greater access to data. With this comes the ability to glean meaningful patterns on major trends, the popularity of human preferences, root causes of problems, and so on, as well as to make intelligent predictions on future figures.
• The choices made in using the model and data, along with aligning the same to the business strategy and market, will be crucial in determining the effectiveness of the model.

The facets of automation tools and DevOps, combined with data science, achieved far-reaching results.

1. Advanced techniques in diagnostics

   With technological advancements in histopathological image analysis and automated diagnosis of complete histology slides, AI enhances microscopic magnification. The combination of AI models and pattern recognition with complex algorithms has enabled the pathologist to oversee analyses and focus on more difficult cases.

2. R&D of new products

   The use of new sophisticated learning algorithms to extract real-world structured and unstructured data leads to new insights into the mechanism of diseases and molecular reactions, thus proving helpful in preclinical experiments. The information is extracted in real-time from commercial, scientific, and regulatory literature to help researches identify the research gaps, eliminate blind spots, and discover disease similarities.

3. Drug development acceleration

   The product development timeline usually ranges from seven to ten years from development to launch. However, with the advancements in AI and machine learning, the time taken to develop, manufacture, and launch patient therapies has drastically come down resulting in the reduction of overall product development timelines. The integration of research data, lab data, and clinical data in combination with the data acquired from social media platforms and wearables in real time with the help of AI and machine learning, has created a holistic approach to the drug development process.

4. Clinical trial transparency compliance

   Companies are required to comply with certain regulatory policies that sometimes require them to anonymize or redact patient information in clinical submissions. Though generalist automation tools are available, many do not meet the expected accuracy. The emergence of Natural Language Processing (NLP) that incorporates scientific, specific taxonomies, and text mining models identifies keywords, phrases, and data patterns to achieve redaction or anonymization.

5. Improved methodology for clinical site selection and patient identification

   The failure of clinical trials to meet their patient enrolment deadlines has necessitated the use of advanced AI models to incorporate unanalyzed historical structured and unstructured data to highlight high probability targets and thus, improve clinical sites and patient selection. The use of AI models during active clinical trials enables real-time adjustments and course corrections.

6. Using machine learning/predictive analysis for optimizing submission dates 

   Life sciences companies need to publish data on the safety of their products. In cases like artwork printing, production run dates must update when an update to the reference label is made. The corresponding health authority would expect these submissions complying with safety standards. To meet stringent timeline requirements, combining and connecting, the appropriate data points with AI and machine learning determines an optimal submission date.

Given a robust model, built by experienced data scientists, we can expect highly satisfactory responses from an AI tool serving any function in the life science industry. In the long run, it will undoubtedly prove beneficial to all stakeholders in the industry.

Conclusion:

Ultimately the cost-effectiveness of mass production and proven cutting edge methods of manufacturing and quality control – make the output of efforts in the entire life science industry reach out to an ever-broadening base of consumers. This complements the decision making process of AI and big data. Put together, big data and AI pave the way for greater progress in society. Needless to say, early adopters of AI and big data reap the benefits and gain competitive edge over others in marketing their products, improving patient outcomes and care, and driving cost efficiencies.