We have already delivered dozens of solutions used by clinicians, researchers, nurses and healthcare providers. Trusted by the largest hospital district in Finland, as well as pharmaceutical companies, consultancies and health data providers, we guarantee a team with the experience for your success.
We provide missing expertise for research groups, in for example natural language processing (NLP) and machine learning. We deliver statistics, visualisations, and solutions for medical sciences.
We deliver customer segmentation, predictive analytics, NLP solutions built on top of well designed data platforms and data architecture. We help in creating and improving data strategies and automating customer processes with the help of artificial intelligence.
We help with cloud migrations and the modernisation of IT solutions, building data integrations to collect data into single source platforms. Modern digital solutions enable efficient use of data for analytics and machine learning.
We build data integrations between different systems, as well as data extractions and data mining. We have expert knowledge in survival and forecast models for identification of patient groups and risks. Healthcare ERP solutions help the customers to develop their own processes with data.
We have built data platforms for clinical data and offered helping hands for existing projects. We deliver data projects from proof of concepts to production installations with deep expertise in machine learning and AI.
We have build data integrations to market-leading health devices and imported the data to different types of data platforms. We create forecasting models with time series data and develop analytics based on the data collected by medical devices.
We can help with inventory management and sales forecasting with relevant experience from for example Amazon. We will build forecast models used by wholesale by importing more data from other sources.
We have helped study the effect of marketing and analysed drug effects based on real word data. We offer data science projects, where available data is used to answer for example marketing and sales questions that cannot be answered with existing BI reports.
Predicting the best response of a patient that has multiple myeloma type of cancer.
Data visualization to explore how medication advertisement affects clinicians' behaviour.
Machine learning and neural network based forecasting models for various healthcare applications.
Cloud-enabled explorer for healthcare data to use by researchers, with easy-to-use interactive dashboard.
Data models and data integrations for storing, processing, and utilizing IoT data for healthcare devices.
Hiding personal data in patient journals using neural masking.
Architecture, infrastructure, and security development for a healthcare analytics data platform.
Quantitative analysis of microscopy images of human cancer cells using image recognition.
Design and data engineering for a healthcare ERP, used to improve the use of resources for more influential interventions.
Ultra-wideband and accelerometer data transfer for rehabilitation monitoring.
Improved SQL data models for the client's healthcare products to meet the PowerBI reporting needs.
Developed an AI model to extract information about tumor size and location, consciousness, and genetic alterations.
Data is collected from a number of different source systems into a usable format.
Natural language processing tools for automatic structuring and analysis of text materials.
We did application development, Azure cloud architecture and infrastructure, design and implementation of data integrations, and security development for a healthcare analytics data platform.
We implemented ultra-wideband and accelerometer data transfer. Data is used for rehabilitation monitoring in cloud-native applications. Our team developed the signal processing components, data integrations and data visualizations.
We extracted information from several healthcare systems and built an AI model that learns to predict future risk of custody. We have implemented various healthcare applications using machine learning and neural network based forecasting.
We developed an AI model that learned to extract information about tumor size and location, level of consciousness, and genetic alterations. This information was applied to speed up cohort identification in neurological studies involving stroke and brain cancer patients.
Our specialists led service design and data engineering in the development of an ERP for healthcare domain. The product helps to better target treatments and improve the use of resources for more influential interventions.
We implemented data integrations and pipelines for gathering patient data from different source systems. The data is pushed into a single data platform, from which it can be effortlessly used in any number of applications.
We did data engineering and visualization with Plotly.JS to explore how medication advertisement affects clinicians' behaviour.
We worked with predicting the best response of a patient that has multiple myeloma type of cancer, with respect to the current treatment lines. The aim of the project was to help the clinical personnel decide whether the treatment should be changed or not.
We have developed several different natural language processing tools for automatic structuring and analysis of text materials for healthcare.
We implemented anonymization of sensitive data, such as names and addresses, in patient journals using neural masking.
We improved SQL data models for the client's healthcare products to meet the PowerBI reporting needs. Data was run through a multi-stage pipeline and validated in data graphs.
We implemented quantitative analysis of fluorescence microscopy images of human cancer cells using image recognition.
We designed and implemented a data explorer for healthcare data to use by researchers. The browser-based application had extensive filtering and data exploring capabilities within an interactive, easy-to-use user interface.
We implemented data models and data integrations for storing, processing, and utilizing IoT data for healthcare devices on a cloud platform. Our specialists also led the user interface and service design of the new product.
"Even though we have extensive expertise in genomics and data analysis, trying to do everything ourselves is not always advisable. Veracell’s expertise in NLP and electronic health records accelerated our research by structurizing and modeling the data for the project's purposes.”
- Professor Matti Nykter, Tampere University
"It has been quite beneficial working with Veracell in our NLP project. Veracell engineers helped us get started from the beginning and has been consistently responsive with discussions and ideas for improving our EFI model."
- Jake Lin, PhD, Tampere University
"Innovative technologies reach their full potential when developed in collaboration with domain experts and guided by thoughtful design."
- Antti Kangas, CTO, Nightingale Health
“Veracell was able to effectively plan and carry out our data model development project, providing us useful competence to develop our reporting platform. Communication with Veracell was continuous and smooth throughout the project.”
"Veracell is our long-term trusted partner and enables us to deliver more demanding projects that require data and artificial intelligence expertise."
- Ari Rantanen, Partner at AI Roots
"From Veracell we found a professional team that we had been looking for to advance our lifecycle modeling. The results of the project are promising and lead us towards a production solution."
- Tomi Krogerus, Senior Manager of Analytics and AI
“Veracell handled the data and AI strategy sprint professionally and efficiently, which resulted in concrete plans for choosing the direction of AI development.”
- Johannes Karjula, CEO of Trustmary
"Long-term cooperation with Veracell has yielded good results. Veracell is a reliable partner for us in the development of our data-centric solutions."
- Hannu Tissari, Head of Product Development, Tietoevry
"Whether you need to develop natural language processing (NLP) or analysis algorithms, customize the interface, or find an experienced backend developer, Veracell delivers."
- Dr. Heli Holttinen, CEO, Cambri
Exploratory analysis on a raw dataset of patients with multiple myeloma and comparing the efficacy of recorded available medical treatment lines, using logistic regression and random forest. Predictive analysis on patients with glioblastoma using deep learning.
Implemented data pipelines to collect, clean, standardize and store electric health records, with data modeling, pipeline management and monitoring. Built models for forecasting demand of certain medications.
Working in data analytics enabling more effective use of resources in social and psychiatric care. Interested in combining machine learning models and health technology for patient care and decision-making assistants.
Studying biosignal processing and health informatics during future Master’s studies. Aims to help healthcare professionals in their work by providing them with decision support based on the analysis of healthcare data.
Holds an MSc in Biomedical Engineering, majored in Health Technology and Informatics. Has created an algorithm for detecting falls in a home or hospital setting and worked in a healthcare data analytics customer project handling data loading and transformation.
Worked at Tietoevry to build a modern healthcare analytics platform, responsible of application development, Azure cloud architecture and infrastructure, data integrations, and security.
Experienced in text analytics and natural language processing. Has built predictive models for predicting mortality in traumatic brain injury patients, and data pipelines for EMR data to be used for research purposes.
Data engineer working with healthcare-related data integrations, analytics and visualisation. Has been developing an ERP system for targeting resources in psychiatric care.
Markus Räsänen is part of Veracell's expert network, which helps us acquire specialized expertise in various fields for projects if necessary. As a top expert in his field, Markus strengthens our expertise in the field of healthcare and medicine.
Implementing data integrations for Withings devices to enable social and health care professionals to monitor individuals' well-being in real time. Currently uses Azure functions, combined with full-stack development.
Worked on data visualization on how drug advertisement affects clinicians' behavior, EKG analysis with Python for decision support in healthcare, and analysis of MRI images in Matlab. Also knowledgeable about standards and regulations in healthcare (MDR).
Built integrations of health monitors and visualization of health-related time-series data. Proficient in user-management in multi-tenant applications, API design for data-retrieval and stream-analytics to enable real-time analytics.
Years of experience in bioinformatics and statistical and data analyses in medical research. Has worked in several projects with EHR data processing and analysing as well as developed data pipelines for clinical patient record systems in cloud environments.
Analyzed complex biological datasets in academia and a CRO. Developed neural network models for healthcare NLP and predictive analytics. Built data platforms and applications for healthcare data.
Data scientist developing predictive modelling, segmentation, and natural language processing (NLP) algorithms and data pipelines for medical research. Strong understanding of statistical modelling and hypothesis testing frameworks using frequentist and Bayesian approaches.
Lead designer for many healthcare-related products, including application for tumor board work, explorer for clinical data, and multiple electronic patient record systems. Expert in data visualisation and user-centered design for healthcare professionals.