Bio

Bio


Jose Posada holds a Ph.D. and master’s degree in Biomedical Informatics from the University of Pittsburgh. He is highly skilled in the use of EHR data to answer clinical and healthcare operational questions. He co-leads the development and support of the Stanford Clinical Data Warehouse STARR-OMOP where he is responsible for increasing and ensuring the data quality of the database and leading the participation of Stanford in multicentric multinational clinical studies. His current research and development efforts focus mostly on clinical text. He led the scientific design of two clinical text pipelines, one to de-identify millions of clinical notes (TiDE) and the other to extract concepts from biomedical ontologies from them. He is uniquely qualified for developing and implementing state of the art clinical natural language processing algorithms to answer clinical questions.

Current Role at Stanford


Sr. Clinical Data Scientist

Honors & Awards


  • Fulbright Fellowship for PhD Studies, Fulbright (2014)

Education & Certifications


  • PhD, University of Pittsburgh, Biomedical Informatics (2018)
  • MSc, University of Pittsburgh, Biomedical Informatics (2016)
  • MSc, Universidad del Norte, Mechanical Engineering (2009)
  • Engineer, Universidad del Norte, Electronics Engineering (2007)

Patents


  • M. E. Sanjuan, J. R. Garcia, J. D. Posada, P. J. Villalba,. "United States Patent 8,390,446 Method and apparatus for on-line estimation and forecasting of species concentration during a reaction by measuring electrical conductivity", Universidad del Norte, Jul 1, 2013
  • M. E. Sanjuan, J. R. Garcia, J. D. Posada, P. J. Villalba,. "Germany Patent EP 2350628 B1 20130717 Method and apparatus for on-line estimation and forecasting of species concentration during a reaction", Universidad del Norte, Jul 1, 2013

Professional

Professional Interests


Clinical Natural Language Processing
Artificial Intelligence in Medicine
Electronic Health Records Data Quality

Work Experience


  • Sr. Clinical Data Scientist, Stanford University (September 15, 2018 - Present)

    Location

    Redwood City

  • Graduate Student Researcher, University of Pittsburgh (August 1, 2016 - August 31, 2018)

    Location

    Pittsburgh

  • Full time professor, Universidad Autonoma del Caribe (January 1, 2011 - August 31, 2018)

    Location

    Barranquilla

  • Research and Teaching Assistant, Universidad del Norte (January 1, 2007 - December 31, 2010)

    Location

    Barranquilla

Publications

All Publications


  • Prediction of Major Depressive Disorder Following Beta-Blocker Therapy in Patients with Cardiovascular Diseases. Journal of personalized medicine Jin, S., Kostka, K., Posada, J. D., Kim, Y., Seo, S. I., Lee, D. Y., Shah, N. H., Roh, S., Lim, Y., Chae, S. G., Jin, U., Son, S. J., Reich, C., Rijnbeek, P. R., Park, R. W., You, S. C. 2020; 10 (4)

    Abstract

    Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) model predicting the onset of major depressive disorder (MDD). A model based on L1 regularized logistic regression was trained against the South Korean nationwide administrative claims database to identify risk factors for the incident MDD after beta-blocker therapy in patients with CVD. We identified 50,397 patients initiating beta-blockers for CVD, with 774 patients developing MDD within 365 days after initiating beta-blocker therapy. An area under the receiver operating characteristic curve (AUC) of 0.74 was achieved. A history of non-selective beta-blockers and factors related to anxiety disorder, sleeping problems, and other chronic diseases were the most strong predictors. AUCs of 0.62-0.71 were achieved in the external validation conducted on six independent electronic health records and claims databases in the USA and South Korea. In conclusion, an ML model that identifies patients at high-risk for incident MDD was developed. Application of ML to identify susceptible patients for adverse events of treatment may serve as an important approach for personalized medicine.

    View details for DOI 10.3390/jpm10040288

    View details for PubMedID 33352870

  • Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study. Nature communications Burn, E., You, S. C., Sena, A. G., Kostka, K., Abedtash, H., Abrahao, M. T., Alberga, A., Alghoul, H., Alser, O., Alshammari, T. M., Aragon, M., Areia, C., Banda, J. M., Cho, J., Culhane, A. C., Davydov, A., DeFalco, F. J., Duarte-Salles, T., DuVall, S., Falconer, T., Fernandez-Bertolin, S., Gao, W., Golozar, A., Hardin, J., Hripcsak, G., Huser, V., Jeon, H., Jing, Y., Jung, C. Y., Kaas-Hansen, B. S., Kaduk, D., Kent, S., Kim, Y., Kolovos, S., Lane, J. C., Lee, H., Lynch, K. E., Makadia, R., Matheny, M. E., Mehta, P. P., Morales, D. R., Natarajan, K., Nyberg, F., Ostropolets, A., Park, R. W., Park, J., Posada, J. D., Prats-Uribe, A., Rao, G., Reich, C., Rho, Y., Rijnbeek, P., Schilling, L. M., Schuemie, M., Shah, N. H., Shoaibi, A., Song, S., Spotnitz, M., Suchard, M. A., Swerdel, J. N., Vizcaya, D., Volpe, S., Wen, H., Williams, A. E., Yimer, B. B., Zhang, L., Zhuk, O., Prieto-Alhambra, D., Ryan, P. 2020; 11 (1): 5009

    Abstract

    Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.

    View details for DOI 10.1038/s41467-020-18849-z

    View details for PubMedID 33024121

  • An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza. medRxiv : the preprint server for health sciences Burn, E., You, S. C., Sena, A. G., Kostka, K., Abedtash, H., Abrahão, M. T., Alberga, A., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Banda, J. M., Cho, J., Culhane, A. C., Davydov, A., DeFalco, F. J., Duarte-Salles, T., DuVall, S., Falconer, T., Gao, W., Golozar, A., Hardin, J., Hripcsak, G., Huser, V., Jeon, H., Jing, Y., Jung, C. Y., Kaas-Hansen, B. S., Kaduk, D., Kent, S., Kim, Y., Kolovos, S., Lane, J. C., Lee, H., Lynch, K. E., Makadia, R., Matheny, M. E., Mehta, P., Morales, D. R., Natarajan, K., Nyberg, F., Ostropolets, A., Park, R. W., Park, J., Posada, J. D., Prats-Uribe, A., Rao, G., Reich, C., Rho, Y., Rijnbeek, P., Sathappan, S. M., Schilling, L. M., Schuemie, M., Shah, N. H., Shoaibi, A., Song, S., Spotnitz, M., Suchard, M. A., Swerdel, J. N., Vizcaya, D., Volpe, S., Wen, H., Williams, A. E., Yimer, B. B., Zhang, L., Zhuk, O., Prieto-Alhambra, D., Ryan, P. 2020

    Abstract

    To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza.We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014-19.6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use.Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.

    View details for DOI 10.1101/2020.04.22.20074336

    View details for PubMedID 32511443

    View details for PubMedCentralID PMC7239064

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