Solita Health researched: Omaolo online symptom checker helps to predict national healthcare admissions related to COVID-19

In our recently published study [1], I and my Solitan colleagues Kari Antila and Vilma Jägerroos examined the possibility of predicting the burden of healthcare using machine learning methods. We used data on symptoms and past healthcare utilization collected in Finland. Our results show that COVID-19-related healthcare admissions can be predicted one week ahead with an average accuracy of 76% during the first wave of the pandemic. Similar symptom checkers could be used in other societies and for future epidemics, and they could provide an opportunity to collect data on symptom development very rapidly - and at a relatively low cost.

The rapid spread of the SARS-CoV-2 virus in March 2020 presented challenges for nationwide assessment of the progression of the COVID-19 pandemic. In Finland, Solita helped to add a COVID-19 symptom checker to a pre-existing national, CE-marked medical symptom checker service ©Omaolo. The Omaolo COVID-19 symptom checker achieved considerable popularity immediately after its release, and the city of Helsinki, for example, has estimated annual savings of 2.5 million euros from its use. Although there have been studies about how well symptom checkers perform as clinical tools, their data’s potential on predicting epidemic progression, to our knowledge, has not yet been studied.

For this purpose, Solita developed a machine learning pipeline in the Finnish Institute for Health and Welfare’s (THL) computing environment for automated model training and comparison. The models created by Solita were retrained every week using time-series nested cross-validation, allowing them to adapt to the changes in the correlation of the symptom checker answers and the healthcare burden. The pipeline makes it easy to try new models and compare the results to previous experiments. 

We decided to compare linear regression, a simple and traditional method, to XGBoost regression, a modern option with many hyperparameters that can be learned from the data. The best linear regression model and the best XGBoost model (shown in the figure) achieved mean absolute percentage error of 24% and 32%, respectively. Both models get more accurate over time, as they have more data to learn from when the pandemic progresses.

COVID-19–related admissions predicted by linear regression and XGBoost regression models, together with the true admission count during the first wave of the pandemic in 2020.

Our results show that a symptom checker is a useful tool for making short-term predictions on the health care burden due to the COVID-19 pandemic. Symptom checkers provide a cost-effective way to monitor the spread of a future epidemic nationwide and the data can be used for planning the personnel resource allocation in the coming weeks. The data collected with symptom checkers can be used to explore and verify the most significant factors (age groups of the users, severity of the symptoms) predicting the progression of the pandemic as well.

You can find more details in the publication [1]. The research was done in collaboration with University of Helsinki, Finnish Institute for Health and Welfare, Digifinland Oy, and IT Centre for Science, and we thank everyone involved.

If you have similar register data and would like to perform a similar analysis, get in touch with me or Solita Health and we can work on it together!

Joel Röntynen, Data Scientist,


[1] ​​Limingoja L, Antila K, Jormanainen V, Röntynen J, Jägerroos V, Soininen L, Nordlund H, Vepsäläinen K, Kaikkonen R, Lallukka T. Impact of a Conformité Européenne (CE) Certification–Marked Medical Software Sensor on COVID-19 Pandemic Progression Prediction: Register-Based Study Using Machine Learning Methods. JMIR Form Res 2022;6(3):e35181, doi: 10.2196/35181, PMID: 35179497

Reading the genomic language of DNA using neural networks

Neural networks are powerful tools in natural language processing (NLP). In addition, they can also learn the language of DNA and help in genome annotation. Annotated genes, in turn, play a key role in finding the causes and developing treatments for many diseases.

I have been finishing my studies while working at Solita and got the opportunity to do my master’s thesis in the research program in which Solita is participating. The topic of my thesis consisted of language, genomics and neural networks, and this is a story of how they all fit into the same picture.

When I studied Data Science at the University of Helsinki, courses in NLP were my favorites. In NLP, algorithms are taught to read, generate, and understand language, in both written and spoken forms. The task is difficult because of the characteristics of the language: words and sentences can have many interpretations depending on the context. Therefore, the language is far from accurate calculations and rules where the algorithms are good at. Of course, such challenges only make NLP more attractive!

Neural networks

This is where neural networks and deep learning come into play. When a computational network is allowed to process a large amount of text over and over again, the properties of the language will gradually settle into place, forming a language model. A good model seems to “understand” the nuances of language, although the definition of understanding can be argued, well, another time. Anyways, these language models taught with neural networks can be used for a wide variety of NLP problems. One example would be classifying movie reviews as positive or negative based on the content of the text. We will see later how the movie reviews can be used as a metaphor for genes.

In recent years, a neural network architecture called transformers has been widely used in NLP. It utilizes a method called attention, which is said to pay attention to emphases and connections of the text (see the figure below). This plays a key role in building the linguistic “understanding” for the model. Examples of famous transformers (other than Bumblebee et al.) include Google’s BERT and OpenAI’s GPT-3. Both are language models, but transformers are, well, transformable and can also be used with inputs other than natural language.

An example of how transformers self-attention “sees” the connections in a sentence. The difference of the last word completely changes what the word “it” most refers to.



And here DNA and genomes come into the picture (also literally in the picture below). You see, DNA has its own grammar, semantics, and other linguistic properties. At its simplest, genes can be thought of as positive movie reviews, and non-coding sequences between genes as negative reviews. However, because the genomes of organisms are part of nature, genes are a little more complex in reality. But this is just one more thing that language and genomics have in common: the rules do not always apply and there is room for interpretation.

Simplification of a genome and a gene. Genomic data is a long sequence of characters A, T, C, and G representing four nucleotide types. Genes are coding parts of the genome. At their simplest, they consist of start and end points and the characters between them.


Since both text and genomic data consist of letters, it is relatively straightforward to teach the transformer model with DNA sequences instead of text. Like the classification of movie reviews in an NLP-model, the DNA-model can be taught to identify different parts of the genome, such as genes. In this way, the model gains the understanding of the language of DNA.

In my thesis, I used DNABERT, a transformer model that has been pre-trained with a great amount of genomic data. I did my experiments with one of the most widely known genomes, E. coli bacterium, and fine-tuned the model to predict its gene locations.

Example of my experiments: the Receiver operating characteristic (ROC) curves helped me to find the most optimal input length for the genome data. Around 100 characters led to the highest curve and thus the best results, whilst 10 was obviously too short and 500 too long.

After finding the most optimal settings and network parameters, the results clearly showed the potential. Accuracy of 90.15% shows that the model makes “wise” decisions instead of just guessing the locations of the genes. Therefore the method has potential to assist in the basic task of bioinformatics: new genomes are sequenced at a rapid pace, but their annotation is slower and more laborious. Annotated genes are used, for example, to study the causes of diseases and to develop treatments tailored to them.

There are also other methods for finding genes and other markers in DNA sequences, but neural networks have some advantages over more traditional statistics and rule based systems. Rather than human expertise in genomics, the neural network based method relies on the knowledge gathered by the network itself, using a large amount of genomic data. This saves time and expert hours in the implementation of the neural network. The use of the pre-trained general DNA language model is also environmentally friendly. Such a model can be fine-tuned with the task-specific data and settings in just a few iterations, saving computational resources and energy. 

There is a lot of potential in further developing the link between transformer networks and DNA to study what else the genome language has to tell us about the life around us. Could this technology contribute to the understanding of genetic traits, the study of evolution, the development of medicine or vaccines? These questions are closely related to the healthcare field, in which Solita has strong expertise, including in research. If you are interested in this type of research, I and other Solita experts will be happy to tell you more!

Venla Viljamaa (Data Scientist)