Category Archives: Apps

#nCoVStats: An Interactive Data Analysis Tool for COVID-19 (Coronavirus SARS-CoV-2 2019-nCoV)

Launch “#nCoVStats Data Analysis Tool by HPI”
password: 2019-nCoV

Latest News

#nCoVStats @HPI_DE Daily #COVID19 update: Worldwide #infections exceeded 543.2M, about 553k new #cases reported within the past 24hrs. Good to know that 349.1M (64%) people are counted as #recovered, but also 6.3M+ (1%) as #deceased.

#nCoVStats @HPI_DE Daily #COVID19 update: Worldwide #infections reached 542.7M, about 741k new #cases reported within the past 24hrs. About 4.2M new infections in the past 7 days, slightly showing upward #trend ↗️.

#nCoVStats @HPI_DE Daily #COVID19 update: Worldwide #infections reached 542.0M, about 740k new cases reported in the past 24hrs.
Focus #Germany 🇩🇪: Back to 477k new infections within the past 7d; similar to early May, but now with upward #trend ↗️; total #cases reached 27.6M.

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Global Health Challenge: 2019-nCoV Coronavirus outbreak

Just a couple of days ago, the year 2020 started when a new global health challenge approached: the outbreak of 2019-nCoV Coronavirus in China end of 2019. As seen in other pandemic situations, the global news coverage deals with the topic on a daily basis. With the provided data analysis tool, we want to contribute to an informed news coverage about the situational development.

Background: Graphical Data Exploration using In-Memory Database Technology

Screenshot of the HPI 2019-nCoV Data Analyzer ToolResearchers of the Hasso Plattner Institute designed an interactive graphical data exploration tool. It support unbiased and informed news coverage about the outbreak by exploring latest Coronavirus case reports provided by the Robert Koch Institute in Berlin and DXY in China. Therefore, you can use the tool to explore latest available case report data and to assess the developments of the past days in different graphical perspectives. The tool builds on the latest in-memory database technology. The same technology is the centerpiece of the cloud platform for precision medicine.


Dr. Matthieu-P. Schapranow
Group Leader and Scientific Manager Digital Health Innovations
Digital Health Center @ Hasso Plattner Institute
Phone: +49 331 5509 -1331
Mail: please use the contact form
14482 Potsdam, Germany

Archive (no longer updated)

  • Feb 20, 2020:

  • Feb 19, 2020:

  • Feb 18, 2020:

  • Feb 17, 2020:

  • Feb 16, 2020:

  • Feb 15, 2020:

  • Feb 14, 2020: Situation in Japan unchanged.

  • Feb 13, 2020: A spike in reported cases in Hubei region was reported. However, this might be the result of unconfirmed cases for days reported just now as confirmed and does not necessary indicate a super spreading event.

  • Feb 12, 2020:

  • Feb 11, 2020: As of today, we also included the reported number of cured cases to provide additional insights. Furthermore, WHO decided to label the lung disease as COVID-19 from today on.
  • Feb 10, 2020: We switched data source from RKI to DXY to have more accurate and up-to-date numbers.

  • Feb 9, 2020:

  • Feb 7, 2020


How AI Provides Medical Assistant System for Oncology

We are happy to support the work of the Plattform Lernende Systeme. Together with subject-matter experts from medicine, oncology, and clinical sciences, we created a demonstrator for the 2018 National Digital Summit. Building on our Medical Knowledge Cockpit, we enhanced it by selected machine learning and artificial intelligence algorithms.

As a result, our demonstrator shows how an integrated medical assistant system supports physicians and clinicians in their daily work. Thus, latest medical knowledge is always at hand, interactive analysis of data from similar cases provide evidence-based insights, and adherence to latest clinical guidelines are supported.

Clinical Trial Matching

Today, the pharmaceutical industry faces a time challenge since approval of innovative new drugs requires successful completion of clinical trials. Identifying relevant candidates for clinical trials is time-consuming and not targeted, e.g. by using TV and newspaper advertisements. Benefits for participants of clinical trials remain hidden during their participation, which results in relatively high dropout rates exposing many clinical trials at risk to fail before their completion.

Together with experts from the pharmaceutical industry, we created the Clinical Trial Matching app: an innovative data-driven way to identify and contact relevant candidates for clinical trials within seconds. We incorporated latest in-memory database technology to test inclusion and exclusion criteria for registered users. Search results are clustered accordingly to identified similarities and ranked compared to perfect matching candidate. Ultimately, selected candidate can be contacted directly to highlight the benefits from participating in the given clinical trial.

During the whole process, the personal identity of candidates is kept private. After conducting an informed consent interview with the personal medical doctor, she or he contacts the clinical research organization to acquire further details. As a result, identifying clinical trials participants can be performed for the first time within seconds saving a tremendous amount of preparation time. Furthermore, participants in clinical trials may benefit from access to latest medical innovations, i.e. access to an improved way of healthcare long before it is released to public access.

Data Donation Pass

The data donation pass is an innovative way for citizens taking care of their personal health. It enables easy sharing of personal health and fitness data with known third-parties, such as family members, physicians, and selected research projects, whilst keeping full control of  personal data.

Users of the data donation pass app define their health interests in their personal online profile. Based on these interests, relevant information and projects are carefully compiled and forward to the users keeping their identity private.

Based on their personal interests, citizens can chose to participate and support individually selected projects and start by a single click. After indicating their interest to donate data for a specific use case or project, all relevant medical details are forwarded to their trusted medical doctor, e.g. the general practitioner, who takes care of the informed consent. Thus, the user can raise any open questions and understand eventual implication of her/his participation.

Participants in projects receive regular updates about how and how often their donated data was used for what purpose. Thus, they can better understand how their donation contributed to a concrete project. Nonetheless, users can redraw their personal consent to donate data at any point in time. As a result, participants keep full control of the use of data.

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Cloud Services for Analysis of Genome Data

Alignment screenshotGenome data can be used to identify individual roots of certain diseases and to derive specific treatment decision. However, clinicians and medical experts only rarely incorporate genomic data due the required technical knowledge nowadays. We focus on providing tools and services for non-IT experts that enable them to process and analyze medical data, e.g. genome data, by themselves. Our services are provided as Software-as-a-Service (SaaS) cloud applications eliminating the need for local hardware resources. Test-drive our Cloud Services for Analysis of Genome Data today.


The user logs into the personal account, which protects all personal data. After submitting raw genome sequence data, e.g. as FASTQ file, the algorithm for alignment and the reference genome are configured. The high-throughput processing of data is performed asynchronously, i.e. multiple samples can be submitted in parallel. After processing, results can be explored interactively. Thus, medical results from international research databases are combined to identify relevant mutations and diseases. Identified mutation sites of individual study participants are listed and sorted accordingly to their relevance for certain diseases. In addition, each mutation site can be investigated in a detailed way on various levels, e.g. nucleotide or amino acid base, using our Genome Browser. The comparison of genomic data from multiple patients or samples, is supported by our Cohort Analysis.

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Apps for Researchers

The following apps are designed together with researchers for primary use by researchers.

Please also have a look at our apps for clinicians and apps for patients.

Medical Knowledge Cockpit for Patients

Medical Knowledge Cockpit for PatientsWe are happy to announce that our latest application the “Medical Knowledge Cockpit for Patients” is online available. It enables you to securely store your personal information, such as biomarkers and diagnoses, within your private electronic health record. Thus, you can perform iterative searches without the need to provide all your medical details each time.

Drug Response Analysis

Drug Response Analysis (Poster)


Medical doctors can chose from a variety of available medical drugs for specific types of cancer. However, the American Society of Cancer published in 2012 that three out of four applied chemotherapies do not work as expected. The challenge for medical experts in course of personalized medicine is to select the combination of medical drugs, which works best for an individual patient.

To predict the drug’s effect for a concrete tumor and patient, it is possible to extract the tumor and test various therapies in parallel in laboratories and document the outcome. Today, this is a time-consuming process requiring excessive wet-lab work and time-consuming manual data analysis.

With the help of our in-memory technology, we were able to improve the analysis process from weeks of manual data analysis to minutes of interactive data exploration. With a growing library of experiment results for a drugs applied to a certain type of cancer, we are now able to predict the drug response for new tumors minimizing the drugs to test. Furthermore, we enable researchers to discover correlations between genetic variants and drug response interactively. Thus, researchers are able to verify hypothesis in a couple of minutes for the first time and deriving new indicators to select a concrete drug and therapy combination per patient.

You can find a detailed overview of the process and our findings in the poster attached to this webpage and selected application screenshots in the gallery below.

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