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Welcome to OHDSI! - Please introduce yourself

Colleagues in non-experimental research data erroneously termed observational since all data, experimental or non-experimental are observational:

I am Larry Holmes, Jr, a quantitative epidemiologist with focus on pediatric cancer and health disparities. My focus is on socio-epigenomic modifications as exposure functions of health disparities in pediatric cancer incidence, progression and survival, implying extra-genetic-environment interaction as potential explanation of racial, sex and ethnic variances in pediatric cancer prognosis and survival. .As a strong advocate of team science one firmly believes that we can improve patient outcome and public health by collective scientific effort in risk isolation and intervention mapping to address such etiologies and risk adapted treatment.

Hi!
My name is Lei Wang. I am a Web Applications Architect at Stanford Children’s Health. I have worked with various databases before, such as SQL Server, Oracle, Postgress… I would like to develop analytical solutions using OMOP and OHDSI tools.

Hi, my name is Stan Robertson, and I am working on what we call the “Health Data Platform” for the Ministry of Health, Government of British Columbia, Canada.

We have not standardized on the OMOP model and OHDSI platform, but it is intriguing as a solution for our needs. We are issuing a Request for Proposals (RFP) in April, so the selected proposal will help determine the platform.

I am impressed with the OHDSI community and what it has accomplished. Our challenges with using the model includes mapping Canadian codes (e.g. ICD-10-CA and ICD-9-CA), and other source codes to OMOP Concepts, and we are also planning to implement a “Federated” system - not storing all data in one location, except for extracted data specific to research projects.

I welcome any suggestions anyone has to help us on the right path, and I hope to contribute in return if we continue along this journey.

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Hello, I’m Nam Nguyen, Director of Health Data Management, Medical Informatics at Practice Fusion (Allscripts). My team will soon be converting our data into OMOP. I am excited to work with and get help from the OHDSI community in this endeavor.

Hi, my name is Henrik John. I recently started as a PhD student at Erasmus University Medical Center in Rotterdam.

I am working in the group of Peter Rijnbeek on the Patient-Level Prediction package and will concentrate on large-scale prediction and deep learning. Currently, I am looking into learning curves and how they can be compared across databases, prediction algorithms, class distributions, etc…

Looking forward to meeting some of you during the OHDSI European Symposium.

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Hello,
My name is Jihoon Yoon. I graduated with a Master’s Degree in Biostatistics at Washington University in St. Louis.

I am a researcher at Seoul National University Hospital working with various data and I am excited to be part of the OHDSI community.

We hope to learn and contribute along this journey.
Sincerely,
Jihoon Yoon

Hi Everyone,

My name is Chris Sindoni and I am Chief Data Officer at Practice Fusion in San Francisco. We are a cloud based EHR provider recently acquired by Allscripts. Allscripts now has three commercially available de-identified EHR patient data sets, as well as a de-identified claims data set. Each of these has tens of millions of individual patients.

We are making a commitment to convert these data sets to OMOP 5. Members of the team will be joining the OHDSI community, and I expect we will contribute to the Github Common Data Model project and will participate in working groups.

We also have a RWE team and a RWE analytics platform that uses some code from OHDSI projects. I expect these team members will be active in the community and open source projects as well.

Sincerely,
Chris Sindoni

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Dear All,

I am Sreekanth V K, Research Scholar at Indian Institute of Technology Kharagpur (IIT Kharagpur), India. I am working on Decision Modeling in Emergency Medical Services (EMS) as my thesis topic. In our school, we are in the process of setting up Healthcare Analytics Lab for developing data-driven solution healthcare operations management problems. Currently, our team works in different areas such as EMS, insurance, and preventive care.

As a want-to-be data scientist, I understand the difficulty in getting data in proper format. I really appreciate this initiative and I hope it would help the researchers and academicians across the world. I hope the shared data in this forum would help us in setting up the direction for healthcare analytics lab.

We really want to join this journey and spearhead this movement in India. I would really appreciate if somebody could let us know the formal method of collaboration and moving forward.

Sincerely,
Sreekanth V K

Hello everyone! My name is David Vizcaya and I have been an epidemiomogist for the last ten years. Currently I work for Bayer as a senior epidemiologist and I am based in Barcelona, Spain.
I strongly believe that progress comes from research, and there is no research possible without collaboration. That’s why I am an OHDSI “believer”! Especially after attending the fantastic european symposium a few hours ago! :slight_smile:

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Hi all, general forum introduction.
Senior postdoc researcher in clinical data science and knowledge engineering @ Maastricht University NL and Maastro (Radiation oncology) Clinic. Specializations in Semantic Web, OPEN FAIR data, distributed machine learning and shared decision making at point of consult. Hoping to encourage (and sometimes enforce) OMOP-CDM as preferred data model for clinical (non-radiology, non-imaging) data. Project lead/co-PI for several EU projects such as STRaTegy, eDECIDEand BIONIC, in addition to several smaller scale feasibilties for NLP and Narrow AI.
Looking forward to learning and contributing via EU OHDSI.
Groetjes,

Hello OHDSI community,

I’m very excited to be here after the great experience of the European symposium.

My name is Iannis Drakos, a data scientist, working in the Danish region of Zealand in the field of medical research and personalized medicine.

We will soon make a decision about a favorable data-model that will be used for the region’s prospective and retrospective data, going back as far as 4 decades.

After traveling around the planet and evaluating the available OHDSI-like solutions, my personal preference is to suggest and promote the OMOP CDM as the most efficient solution.

This is based on two factors.
The first is the wide adoption of the CDM and the functionality of the available tools.
The second, and of equal importance, is the feeling that I left Rotterdam with and makes me think that the OHDSI community is too awesome to miss.

I want to thank everyone for presenting your work and helping us to better understand OHDSI and the community. And I would like to ask for your assistance in accurately describing OHDSI to the local stakeholders.
So please share cases, documentation or demos that you may have and can be used as part of an OHDSI introductory presentation to an audience without special IT/data science/bioinformatics skills.

I’m looking forward to becoming an active community member and getting to know you and the projects in more detail.

Best,
Iannis.

Hello everyone,
I am Yanshan Wang, a research associate at Mayo Clinic. My research interests are but not limited to biomedical information retrieval, natural language processing, and machine learning applications. Currently I am working on a patient-level information retrieval system by leveraging both structured and unstructured EHRs. I am using free text information retrieval techniques and adopting OHDSI CDM.

Looking forward to collaboration opportunities from an exciting OHDSI community.

Sincerely,
Yanshan Wang

Hello everyone,

I’m Anna May, a researcher and sleep physician at Case Western Reserve University and the Cleveland VA. My research is in clinical and epidemiological cohorts to investigate sleep disorders, particularly sleep apnea. I have an interest in cross-platform integration of medical records to improve research capability. I’m looking forward to learning and collaborating with others through OHDSI.

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Hi,
My name is Oezlem, working as HEOR-Manager at Janssen, Germany.
Inspired by the fabulous OHDSI meeting in Rotterdam and lots of fruitful discussions with the OHDSI members I am happy to join the OHDSI journey today.
Looking very much forward to collaborating with you.

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Greetings! I officially joined the forum today, although my team at the VA Information Resource Center (VIReC) has been working with OMOP for over a year now. I am now based at the Portland VA Healthcare System and Oregon State University. Our VIReC team based at the Hines VA Hospital is transforming Medicare data files we have merged with VA data to the OMOP CDM. We are collaborating with the VA Informatics and Computing Infrastructure (VINCI) team led by Michael Matheny, and with dissemination planning with Mary Whooley at the San Francisco VA It is a long process and we hope it will be useful to many within and outside the VA when we have a working product!
Denise Hynes, Portland VA & Oregon State University

Hello Everyone,

my name is Lianne Ippel and I just started a postdoc at Maastricht University at the institute for Data Science (the Netherlands). I joined the symposium in Rotterdam a couple weeks ago and learned a great deal! I will be working on learning health care systems and I believe the OHDSI tools will make my life a lot easier while doing so, once I learned how to work with them. You will probably hear from me during that process.

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Hi Bob,

I am assuming you were the one who volunteered to join the Oncology WG to work on the NAACCR vocabulary during the last WG meeting. I realized that you were not on either the mailing list or among the meeting invitees. If it was not you, I apologize. If it was you, could you please send me your email address?

Thank you,
Rimma

Hi everyone,

I’m Qi Tang, an associate director in the Translational Informatics group of Sanofi, Bridgewater, NJ. I have been working on EHR analytics since 2017. What excites me most is the promising future of precision medicine based on EHR data analytics. A patient can simply share his/her EHR with a health practitioner and obtain a report of different choices of treatment options including their effectiveness, safety and cost.

I would be very interseted in working on predicting patient disease trajectory and treatment effectiveness and safety based on EHR data analysis using machine learning methods, especially, deep learning.

Qi

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Hi. My name is John Welsh. I work at the Vaccine Research Institute of San Diego as CEO and chief bottle washer. My research has focused lately on how gene expression changes when cancer cells first encounter tissue to which they metastasize. In one study, we identified genes that become up-regulated when breast cancer cells are cultured in vivo on brain tissue in a mouse model (https://doi.org/10.1371/journal.pone.0101160). Interestingly, several of the genes that are temporarily up-regulated as breast cancer cells adapt to brain tissue can be suppressed with commonly used drugs, including ibuprofen, dramamine, and MAOA inhibitors, which are antidepressants. I am wondering whether observational data on patients with breast cancer who routinely use one of these drugs show decreased incidence or delay of brain metastasis. I’d be interested to hear back whether this seems like an approachable problem for OHDSI.

By the way, my PhD is from Columbia University, officially in chemistry, but I studied molecular and structural biology. I loved every minute of it, and I love NYC. If OHDSI provides me with even a shadow of an excuse to visit in person, I will jump at it!

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Hi Everyone,

My name is Aldo Saavedra and I’m a data scientist specialising in eMR data at the Sydney University, Australia. I’m currently working on the development of a data sharing analytics platform that will facilitate studies on the standardised datasets and propagation of insights back to stakeholders such as clinicians working at the coal face and administrators.

Our proof of concept is associated with patients presenting to ED with chest pains and studying the patient journey using the data stored in the eMR. The main aim is to determine whether the pathway has been optimised and what are the contributing factors behind unwarranted clinical variations with the aim of improving patient outcomes and overall system performance. Our first study will involve STEMI patients.
With the extracted data I’m hoping to develop statistical bayesian models and machine learning algorithms to answer our questions and develop decision support rules.

It has been great to find a lively collaboration in OHDSI.
At the moment I’m reading the OHDSI documentation how it can help us and where we can contribute.

I’m happy to hear from people that have undergone through a similar journey.

Cheers,
Aldo

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