HAIDi bloguje

I had the pleasure of attending the 73rd Annual Conference of the German Society for Hygiene and Microbiology e. V.. To begin I’d like to offer some kudos to the organizers as it was a well-organized virtual event - full of presentations and insights about the changes due to COVID-19 and overall, an inspiring experience.

A few days ago we received a strange bug report from one of our customers...

Hospitals around the world are fighting the COVID-19 pandemic. Their efforts are led by hospital epidemiologists, experts trained in infection prevention and control. The hospital epidemiologists are responsible for making tough decisions including complete visitor restrictions, universal masking despite lack of sufficient personal protective equipment and quarantining of employees despite lack of a sufficient work force [1].

The cases of antibiotic resistant bacteria continue to grow worldwide. While antibiotic resistance can occur naturally, misuse of antibiotics has accelerated the process. As more and more bacteria become resistant to antibiotic treatment, traditional methods of destroying bacterial infections become less effective and in some cases obsolete.

When a computer makes an error, people tend to judge it harshly. No matter the complexity of the task, we usually start complaining about the software program or stop trusting the algorithm at all. But do we assess our mistakes in the same way? Can we even realize how many mistakes we make?

Listening to the following words may become a little tiresome: "I've heard you have a solution which can read unstructured texts. Can you tell me which keywords do you use?" It was a reason why we added a slide describing why keywords are not sufficient for text mining into our presentations and now we decided to publish it online.

Results of an European survey on healthcare-associated infections (HAIs) has been published recently. In 2016 and 2017, the European Centre for Disease Prevention and Control (ECDC) conducted two surveys on HAIs, which offer the most exhaustive insights into the current European HAI situation.

We have prepared a fresh new website! It is simple, reflects our current activities in healthcare analytics, and introduces our product HAIDi, which brings NLP and machine learning into surveillance of healthcare associated infections.

Have you ever thought about the amounts of data, which are produced every day in each hospital? All those anamneses, daily diagnoses, laboratory reports, nursing records, X-ray reports, patient’s educational protocols and agreements, discharge summaries and many more. Majority of these files are written in plain texts, they are meant to be put into archives and never read again when the treatment is over. However, that’s about to change as we have been intensively working on our platform for processing medical records during the past year.

Common statistical or data mining methods cannot cope with unstructured data (e.g., texts, images or videos) and the business analyses are often based on the structured information only. The unstructured sources are thus omitted even though they usually contain important insights. The DATLOWE solution can help to process the text data, so that they can be analyzed by classic statistical algorithms.

One of the biggest obstacles can be the first step in our analysis pipeline - to prepare data for analysis. The data of our customers is stored in different document formats, e.g. machine readable PDFs, scans, MS office documents like words, excels and others. There are two scenarios how to deploy our pipeline. We can run the pipeline in the cloud or in the environment of our customers. A setup of the pipeline should be as simple as possible and without any additional license costs for our customers.