Unstructured data analysis is a key talking point when it comes to the life sciences industry. The need for better life sciences data management has grown rapidly in recent years, with the help of better data integration and advanced technologies like machine learning, big data analytics, data visualisation, and natural language processing (NLP).
Data scientists usually classify data as semi-structured, structured, and unstructured. Unstructured data represents information that has not been organised into any uniform format and hence is difficult to operate. It may include images, text, video, and audio materials.
This data may come with semantic tags but may suffer from inconsistencies or the lack of standardisation.
Unstructured data analysis cannot be neglected, since this data type is vital. This is usually extracted from human languages via natural language processing (NLP) and gained via sensors, scraped from the web or databases, and so on. This data has vast benefits in terms of generating helpful insights for life sciences companies.
Machine learning for identifying patterns & trends in unstructured data:
Gartner has forecasted how the life sciences and healthcare segment will keep surpassing average growth in IT expenditure.
This investment will be majorly targeted towards cloud transitions, digital care delivery transformations, data and analytics, virtual care solutions, and more. Here are some key points worth noting in this regard:
The life sciences industry churns out a huge amount of unstructured information on a daily basis. This comes from various sources including patient records, clinical notes, and research papers.
This data comes with invaluable insights which may lead to path-breaking insights and discoveries along with better patient care and superior decision-making.
Unstructured data analysis through machine learning is even more essential since there is a need to extract actionable and meaningful insights from the unstructured information. The sheer complexity, volume, and non-standardisation of this data make data integration and analysis vital.
AI-backed technologies that tap machine learning, natural language processing (NLP), and deep learning to extract, analyse, and categorise insights from unstructured information will be vital in this regard. It will help in automatic classification for addressing diverse challenges. It will also help identify trends and patterns in unstructured information.
Common types of files include genome sequencing, clinical images, instrument data, and research documentation. These types do not function well with other conventional tools for data analytics. Life sciences companies are shifting research data to the cloud for tapping analytics solutions.
Life sciences companies often grapple with challenges in terms of data management, including lack of visibility and silos. Other issues include data losses, improper practices, poor internal collaborations, and more.
ML (machine learning) algorithms can be trained to identify data patterns and generate predictions or decisions on the basis of the same. These may be applied for categorising and classifying unstructured data. This makes it easier to analyse and manage the same.
Deep learning also deploys artificial neural networks for modeling complicated data patterns. Auto-classification and image and speech recognition is possible with advanced technologies today.
Auto-classification will help researchers swiftly find emerging patterns/trends along with zeroing in on key findings and addressing potential gaps in research in scientific literature.
This will help in analysing patient records and finding patterns which may establish clinical research or enable the optimisation of care plans for individuals.
It may help in detecting any adverse events in terms of drug safety reports as well. This enables the identification of potential risks while helping tackle them in a prompt manner.
Natural language processing (NLP) is the cornerstone of extracting insights from vital text data. Here’s learning more about the same.
Here are some points relating to natural language processing (NLP) which enables machines to interpret, understand, and generate human languages. Here are some points that should be taken into account:
NLP enables better text categorisation and classification. This may be used throughout diverse NLP applications.
These include language identification, readability assessment, information filtering, and web searching.
These will simplify and automate for applications while enabling the classification of large textual data. This will enable integration and standardisation for unstructured life sciences.
It will make searching more relevant and easier, while improving overall experiences in terms of navigation.
Text summarisation is also made possible by NLP especially for large chunks of unstructured data. NLP also offers named entity recognition systems. Named entities are terms indicating organisations, names, values, and locations. The technology annotates texts, indicating the type of named entities and where they occur in the same.
This will simplify further data usage, enabling easier categorisation. Patterns and trends can be readily identified alongside.
NLP also enables optical character recognition with proper algorithms. They can identify and recognise number and latter shapes, while returning the same through text that may be analysed further with other processing techniques.
The third step in the process is data visualisation. Here’s learning more about the same below.
Data visualisation for communicating the insights from unstructured data to stakeholders
Data visualisation is also a vital step for unstructured data analysis. It indicates data representation via the usage of various displays and graphics for communicating complex relationships and insights to stakeholders. Here are some aspects that should be noted in this regard:
Data visualisation may be leveraged for several purposes. It may help in denoting relationships amongst data points while also delineating insights for stakeholders.
Organisational and data hierarchy can be easily conveyed while it will cover aspects like visual discovery, idea illustration, and idea generation.
Visualisation enables better alignment and design thinking while illustration and communication is done with various data structures and processes.
Visual discovery will be in sync with teams with improved storytelling and better insights. Visualisation enables teams to efficiently convey inputs to decision-makers and colleagues among other stakeholders.
Some mechanisms include pie charts, stacked bar charts, tables, line charts, area charts, scatter plots, histograms, tree maps, and heat maps.
Auto-classification also enables accurate and swifter decision-making through enabling access to relevant insights and data from unstructured information.
Unstructured data analysis will be automated with auto-classification and this will scale up R&D processes, leading to higher innovation and discoveries.
Thus, automatic classification technologies driven by ML, NLP, visualisation, and other tools will enable the identification of trends and patterns throughout unstructured data. This will lead to better insights, usage, and decision-making throughout product development, patient care, safety, logistics, and various other aspects.
FAQs
1,What is unstructured data in the context of life sciences?
Unstructured data for the life sciences industry is a form of data that is not uniform and may be hard to understand. It may have inconsistencies and may be hard to integrate or standardise.
2.What tools and technologies are available for handling unstructured data in life sciences?
There are several technologies and tools used to take care of unstructured data in the life sciences industry. These include machine learning (ML), NLP (natural language processing), data visualisation, and artificial intelligence.
3. What are the potential benefits of analysing unstructured data in life sciences?
There are several advantages of analysing unstructured life sciences data. These include identification of patterns and trends, generation of easy-to-understand actionable insights and faster decision-making as a result.
4. What are the challenges associated with managing and unstructured data in life sciences?
Some of the challenges linked to the analysis and management of unstructured life sciences data include data silos, issues with visibility, collaboration throughout teams, data export and access issues, lack of data organisation and integration, and problems with its retrieval and classification.
