Pawel Kaplanski

Clinical registers are needed to perform research studies and thus to increase medical knowledge that finds its way into new and improved guidelines. Adherence to clinical practice guidelines is mandatory to increase the effectiveness of treatments and to eliminate the negative consequences of medical decisions.

We organized available data into the knowledge of the diagnostic process, based on many sources like studies, publications, recommendations, so it supports doctors decisions. We also developed a central registry for collecting patient’s clinical data from over 70 oncological institutions in Poland. In production since 2016.

The results were published in Expert Systems With Applications that is currently ranked number 1 in the Google Scholar h-index listed under the top publications of artificial intelligence.

Sales analysts are responsible for providing the promotion plan for the new quarter in most of the big FMCG enterprises. Currently, these plans are created manually, mostly using conventional tools like Excel that try to answer typical TPO (trade promotion optimization) questions like:
- Can we lower overall costs by optimizing products volume sales and its promotion strategy by anticipating a promotion calendar for a given period?
- Can we predict using key indicators when and which sales pattern is the most effective and can be used to increase volume sales?
- Can we set up a useful promotion calendar for “slow-moving products”?
- Can we optimize budget KPIs when planning the next sales period?

In our case, the mis-forecasting (avg. the error was around 20%) led to budget reduction (across multiple stages within a whole supply chain). To solve the problem, we combined business knowledge of subject matter experts with historical sales data that we received. We also took into account their anomalies and outliers.

The solution allowed the company to increase its accuracy in prediction by up to 10% of volume planning.

The tax-fraud detection system was based on natural language rules enabling decision makers and specialists to manage a tax fraud knowledge base. Reasoning with AI agents is used to recognize elaborate fraud and non-compliance patterns. A stream-based reasoner allows discovering fraudulent activities in the stream of 5 million invoices per day.

In order to sign a contract, the CEO has to analyze business situations and implement a good strategy, especially about $10+ million contracts. CEO makes highly contextual and time-sensitive decisions that have to factor in priorities, such as risk aversion or profitability. To amplify the gut feelings of the CEO, we have developed the automated decision-making system. The core of the system is based on the effective computing AI models, which are adapted to combine both expert knowledge and intuitions, in order to calculate the quality score of the deal/opportunity. In order to solve a complex problem in business, a manager needs to take into account multiple, conflicting objectives and we observed that the solution must consist of the AI models wrapped in the user-friendly UI, with drag and drop editor for tuning the expert knowledge consumed by the models. Having this done, a visualization of the results can be finally presented on a custom dashboard to the CEO.

Processing of large corpora of legal documents, for finding potentially abusive clauses is very resource-prone and usually requires hiring a team of lawyers. Reduction in this process can be achieved using modern NLP methods. I developed a Python classification based on the NLTK library, that was capable of automating the daily work of the client. The pipeline was constructed using classical approach, based on feature-extraction techniques like n-grams, POS-Tagging, noun-phrase extraction, collocations and NER (named entity recognition), followed by Tf/Idf, feature reduction and finally the classification with Passive-Aggressive, scalable classifier.

Developed a tool, that is allowing encoding the knowledge of cyber-security expert for a cyber-security solution provider of a strong, policy-driven security architecture that can be enforced through defined security domains and controls, and implemented through set standards, guidelines and procedures.

The tool is allowing customers to perform guided cyber-security health check, and after the health-check is completed, the detailed report (diagnosis) is generated allowing the customer to understand the current state of the company’s cybersecurity maturity level and understand the weak points. The estimation of the potential cost of the Problem is also provided.