Technology
Our healthcare AI platform turns massive data sources into valuable insights
There are new kinds of data that are specific to the healthcare and pharmaceutical industries (such as electronic health records) as well as data science tools that allow us to extract valuable knowledge from that data.
Konplik Health™ extracts value from unstructured data in Healthcare. It makes the most of our proprietary software and innovative self-developed AI-based data processing tools and algorithms to help you discover new purposeful and actionable sources of revenue in healthcare.
With Konplik it is possible to identify the costs of medical treatments, their efficiency (cost, benefits, and risks), references to drugs, side effects, or long-term results.
Examples of projects that offer a valuable wealth of information include search, and evaluation for drug discovery, patient experience, dynamic pricing, revenue optimization, competitor monitoring, or compliance checking.
Our Platform connects and extracts valuable insights from a wide range of external data Sets:
- Patent filings
- Scientific Research
- Electronic Health Records
- Clinical Trials
- Tendering Intelligence
- Intelligence on Shortages
- Competitive Analysis
- Sales Data
- Social Media Interactions
Natural language processing (NLP) has practically achieved human quality (or even better) in many different tasks, mainly based on advances in machine learning/deep learning techniques, which allow making use of large sets of training data to build language models, but also due to the improvement in core text processing engines and the availability of semantic knowledge databases.
Machine learning plus linguistic
engineering
Deep learning is, in general, the best choice for text categorization where a large volume of training data is available. When training data is scarce, other more classical machine learning techniques such as decision trees or SVMs, in general, provide better results with a smaller computational cost.
Hybrid solutions combining machine learning (the machine’s opinion) with a rule-based post-filtering (a human-like correction) provide the best results in terms of precision, and they have to become popular in the near future.
Additionally, some machine/deep learning techniques are becoming helpful for supporting humans in the process of building/improving models:
Rule induction techniques for generating a first draft rule model.
Semantic expansion techniques (such as word/sentence embeddings) for improving rule recall.
Text analytics projects are often dependent on Internet-based sources such as the world wide web. These projects usually begin by extracting data from a variety of websites. We call this process “web scraping” (or “web harvesting”). While users can handle web scraping manually, the term often refers to automated methods executed utilizing a web crawler.
The platform step by step
A text analytics project typically consists of four steps:
1) We gather unstructured information.
2) We leverage NLP to convert the raw source into a structured or semi-structured format.
3) We then perform complex and custom transformations – including custom filtering, fuzzy product matching, and fuzzy de-duplication on large sets of data.
4) Finally, we apply any standard predictive analytics or data mining techniques to extract insights.We also use Artificial Intelligence-based algorithms to predict and to optimize revenue and margins, as well as to expand the discovery of new business opportunities.
Ready to leverage unstructured data?
About Us
Konplik Health™ makes the most of cutting edge technologies including proprietary software and innovative self-developed data processing tools and algorithms to extract value from unstructured data in healthcare. Konplik Health™ will help you discover new purposeful and actionable sources of revenue
Contact Info
Wilmington, DE 19801
Recent Posts
New microsite: Voice of the Patient
We just released a new microsite for the voice of the patient at https://konplik.health/voice-of-the-patient/ It includes a video showcasing the experience of a European pharmaceutical company that created a project aimed at collecting and analyzing the voice of women...