Hasso-Plattner-Institut
Prof. Dr. Erwin Böttinger
 

Chair Digital Health - Personalized Medicine

2022

  • 1.
    Kelly, T.N., Sun, X., He, K.Y., Brown, M.R., Taliun, S.A.G., Hellwege, J.N., Irvin, M.R., Mi, X., Brody, J.A., Franceschini, N., Guo, X., Hwang, S.-J., de Vries, P.S., Gao, Y., Moscati, A., Nadkarni, G.N., Yanek, L.R., Elfassy, T., Smith, J.A., Chung, R.-H., Beitelshees, A.L., Patki, A., Aslibekyan, S., Blobner, B.M., Peralta, J.M., Assimes, T.L., Palmas, W.R., Liu, C., Bress, A.P., Huang, Z., Becker, L.C., Hwa, C.-M., O’Connell, J.R., Carlson, J.C., Warren, H.R., Das, S., Giri, A., Martin, L.W., Johnson, W.C., Fox, E.R., Bottinger, E.P., Razavi, A.C., Vaidya, D., Chuang, L.-M., Chang, Y.-P.C., Naseri, T., Jain, D., Kang, H.M., Hung, A.M., Srinivasasainagendra, V., Snively, B.M., Gu, D., Montasser, M.E., Reupena, M.S., Heavner, B.D., LeFaive, J., Hixson, J.E., Rice, K.M., Wang, F.F., Nielsen, J.B., Huang, J., Khan, A.T., Zhou, W., Nierenberg, J.L., Laurie, C.C., Armstrong, N.D., Shi, M., Pan, Y., Stilp, A.M., Emery, L., Wong, Q., Hawley, N.L., Minster, R.L., Curran, J.E., Munroe, P.B., Weeks, D.E., North, K.E., Tracy, R.P., Kenny, E.E., Shimbo, D., Chakravarti, A., Rich, S.S., Reiner, A.P., Blangero, J., Redline, S., Mitchell, B.D., Rao, D.C., Chen, Y.-D.I., Kardia, S.L., Kaplan, R.C., Mathias, R.A., He, J., Psaty, B.M., Fornage, M., Loos, R.J., Correa, A., Boerwinkle, E., Rotter, J.I., Kooperberg, C., Edwards, T.L., Abecasis, G.R., Zhu, X., Levy, D., Arnett, D.K., Morrison, A.C.: Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension. Hypertension. 79, 1656–1667 (2022).
     
  • 2.
    Borchert, F., Lohr, C., Modersohn, L., Witt, J., Langer, T., Follmann, M., Gietzelt, M., Arnrich, B., Hahn, U., Schapranow, M.-P.: GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers. Proceedings of the Language Resources and Evaluation Conference. pp. 3650–3660. European Language Resources Association, Marseille, France (2022).
     
  • 3.
    Nadkarni, G.N., Fei, K., Ramos, M.A., Hauser, D., Bagiella, E., Ellis, S.B., Sanderson, S., Scott, S.A., Sabin, T., Madden, E., Cooper, R., Pollak, M., Calman, N., Bottinger, E.P., Horowitz, C.R.: Effects of Testing and Disclosing Ancestry-Specific Genetic Risk for Kidney Failure on Patients and Health Care Professionals. JAMA Network Open. 5, e221048 (2022).
     
  • 4.
    Borchert, F., Schapranow, M.-P.: HPI-DHC @ BioASQ DisTEMIST: Spanish Biomedical Entity Linking with Pre-trained Transformers and Cross-lingual Candidate Retrieval. Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. pp. 244–258. , Bologna, Italy (2022).
     
  • 5.
    Norden, M., Hofmann, A.G., Meier, M., Balzer, F., Wolf, O.T., Bottinger, E., Drimalla, H.: Inducing and Recording Acute Stress Responses on a Large Scale With the Digital Stress Test (DST): Development and Evaluation Study. Journal of Medical Internet Research. 24, e32280 (2022).
     

2021

  • 1.
    Dellepiane, S., Vaid, A., Jaladanki, S.K., Coca, S., Fayad, Z.A., Charney, A.W., Bottinger, E.P., He, J.C., Glicksberg, B.S., Chan, L., Nadkarni, G.: Acute Kidney Injury in Patients Hospitalized With COVID-19 in New York City: Temporal Trends From March 2020 to April 2021. Kidney Medicine. (2021).
     
  • 2.
    Zenner, A.M., Bottinger, E., Konigorski, S.: StudyMe: A New Mobile App for User-Centric N-of-1 Trials, (2021).
     
  • 3.
    Henkenjohann, R.: Role of Individual Motivations and Privacy Concerns in the Adoption of German Electronic Patient Record Apps—A Mixed-Methods Study. International Journal of Environmental Research and Public Health. 18, 31 (2021).
     
  • 4.
    Oliveira-Ciabati, L., Santos, L.L., Hsiou, A.S., Sasso, A.M., Castro, M., Souza, J.P.: Scientific sexism: the gender bias in the scientific production of the Universidade de São Paulo. Revista de Saúde Pública. 55, 46 (2021).
     
  • 5.
    Klessascheck, F., Lichtenstein, T., Meier, M., Remy, S., Sachs, J.P., Pufahl, L., Miotto, R., Bottinger, E., Weske, M.: Domain-Specific Event Abstraction. Business Information Systems. 117–126 (2021).
     
  • 6.
    Hackl, M., Datta, S., Miotto, R., Bottinger, E.: Unsupervised Learning to Subphenotype Heart Failure Patients from Electronic Health Records. Artificial Intelligence in Medicine. pp. 219–228. Springer International Publishing (2021).
     
  • 7.
    Rasheed, A., Borchert, F., Kohlmeyer, L., Henkenjohann, R., Schapranow, M.-P.: A Comparison of Concept Embeddings for German Clinical Corpora. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 2314–2321 (2021).
     
  • 8.
    Ruther, D.F., Sebode, M., Lohse, A.W., Wernicke, S., Bottinger, E., Casar, C., Braun, F., Schramm, C.: Mobile app requirements for patients with rare liver diseases: A single center survey for the ERN RARE-LIVER‬‬‬. Clinics and Research in Hepatology and Gastroenterology. 45, 101760 (2021).
     
  • 9.
    Freitas, J.K.D., Johnson, K.W., Golden, E., Nadkarni, G.N., Dudley, J.T., Bottinger, E.P., Glicksberg, B.S., Miotto, R.: Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records. Patterns. 2, 100337 (2021).
     
  • 10.
    Nadkarni, G., Gottesman, O., Ellis, S.B., Bottinger, E.: Electronic phenotyping technique for diagnosing chronic kidney disease, (2021).
     
  • 11.
    Hirten, R.P., Tomalin, L., Danieletto, M., Golden, E., Zweig, M., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Bottinger, E.P., Keefer, L., Charney, D., Nadkarni, G.N., Suarez-Farinas, M., Fayad, Z.A.: Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers. (2021).
     
  • 12.
    Cope, J.L., Baukmann, H.A., Klinger, J.E., Ravarani, C.N.J., Bottinger, E.P., Konigorski, S., Schmidt, M.F.: Interaction-Based Feature Selection Algorithm Outperforms Polygenic Risk Score in Predicting Parkinson’s Disease Status. Frontiers in Genetics. 12, (2021).
     
  • 13.
    da Cruz, H.F., Pfahringer, B., Martensen, T., Schneider, F., Meyer, A., Bottinger, E., Schapranow, M.-P.: Using interpretability approaches to update textquotedblleftblack-boxtextquotedblright clinical prediction models: an external validation study in nephrology. Artificial Intelligence in Medicine. 111, 101982 (2021).
     
  • 14.
    Borchert, F., Meister, L., Langer, T., Follmann, M., Arnrich, B., Schapranow, M.-P.: Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence. AMIA Annual Symposium Proceedings. pp. 237–246. American Medical Informatics Association (2021).
     
  • 15.
    Datta, S., Sachs, J.P., Cruz, H.F., Martensen, T., Bode, P., Sasso, A.M., Glicksberg, B.S., Bottinger, E.: FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling. JAMIA Open. 4, (2021).
     
  • 16.
    Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Calcogna, C., Freeman, R., Sands, B.E., Charney, D., Bottinger, E.P., Murrough, J.W., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data (Preprint). Journal of Medical Internet Research. (2021).
     
  • 17.
    Ruther, D.F., Sebode, M., Lohse, A.W., Wernicke, S., Boetinger, E., Casar, C., Braun, F., Schramm, C.: Mobile app requirements for patients with rare liver diseases: A single center survey for the ERN RARE-LIVER‬‬‬. Clinics and Research in Hepatology and Gastroenterology. 45, 101760 (2021).
     
  • 18.
    Dellepiane, S., Vaid, A., Jaladanki, S.K., Paranjpe, I., Coca, S., Fayad, Z.A., Charney, A.W., Bottinger, E.P., He, J.C., Glicksberg, B.S., Chan, L., Nadkarni, G.: Temporal Trends in COVID-19 associated AKI from March to December 2020 in New York City. (2021).
     
  • 19.
    Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J.K.D., Wanyan, T., Johnson, K.W., Bicak, M., Klang, E., Kwon, Y.J., Costa, A., Zhao, S., Miotto, R., Charney, A.W., Böttinger, E., Fayad, Z.A., Nadkarni, G.N., Wang, F., Glicksberg, B.S.: Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach. JMIR Medical Informatics. 9, e24207 (2021).
     
  • 20.
    Paranjpe, I., Chaudhary, K., Johnson, K.W., Jaladanki, S.K., Zhao, S., Freitas, J.K.D., Pujdas, E., Chaudhry, F., Bottinger, E.P., Levin, M.A., Fayad, Z.A., Charney, A.W., Houldsworth, J., Cordon-Cardo, C., Glicksberg, B.S., Nadkarni, G.N.: Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study. PLOS ONE. 16, e0247366 (2021).
     
  • 21.
    Vaid, A., Chan, L., Chaudhary, K., Jaladanki, S.K., Paranjpe, I., Russak, A., Kia, A., Timsina, P., Levin, M.A., He, J.C., Bottinger, E.P., Charney, A.W., Fayad, Z.A., Coca, S.G., Glicksberg, B.S., Nadkarni, G.N.: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clinical Journal of the American Society of Nephrology. 16, 1158–1168 (2021).
     
  • 22.
    Borchert, F., Mock, A., Tomczak, A., Hügel, J., Alkarkoukly, S., Knurr, A., Volckmar, A.-L., Stenzinger, A., Schirmacher, P., Debus, J., Jäger, D., Longerich, T., Fröhling, S., Eils, R., Bougatf, N., Sax, U., Schapranow, M.-P.: Knowledge bases and software support for variant interpretation in precision oncology. Briefings in Bioinformatics. 22, (2021).
     
  • 23.
    Vaid, A., Chan, L., Chaudhary, K., Jaladanki, S.K., Paranjpe, I., Russak, A., Kia, A., Timsina, P., Levin, M.A., He, J.C., Boettinger, E.P., Charney, A.W., Fayad, Z.A., Coca, S.G., Glicksberg, B.S., Nadkarni, G.N.: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clinical Journal of the American Society of Nephrology. 16, 1158–1168 (2021).
     
  • 24.
    Kappattanavar, A.M., Steckhan, N., Sachs, J.P., da Cruz, H.F., Bottinger, E., Arnrich, B.: Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review. JMIR Biomedical Engineering. 6, e21105 (2021).
     
  • 25.
    Gu, X., Yang, H., Sheng, X., Ko, Y.-A., Qiu, C., Park, J., Huang, S., Kember, R., Judy, R.L., Park, J., Damrauer, S.M., Nadkarni, G., Loos, R.J.F., My, V.T.H., Chaudhary, K., Bottinger, E.P., Paranjpe, I., Saha, A., Brown, C., Akilesh, S., Hung, A.M., Palmer, M., Baras, A., Overton, J.D., Reid, J., Ritchie, M., Rader, D.J., Susztak, K.: Kidney disease genetic risk variants alter lysosomal beta-mannosidase (MANBA) expression and disease severity. Science Translational Medicine. 13, eaaz1458 (2021).
     
  • 26.
    Belbin, G.M., Cullina, S., Wenric, S., Soper, E.R., Glicksberg, B.S., Torre, D., Moscati, A., Wojcik, G.L., Shemirani, R., Beckmann, N.D., Cohain, A., Sorokin, E.P., Park, D.S., Ambite, J.-L., Ellis, S., Auton, A., Bottinger, E.P., Cho, J.H., Loos, R.J., Abul-Husn, N.S., Zaitlen, N.A., Gignoux, C.R., Kenny, E.E.: Toward a fine-scale population health monitoring system. Cell. 184, 2068–2083.e11 (2021).
     
  • 27.
    Golden, E.A., Zweig, M., Danieletto, M., Landell, K., Nadkarni, G., Bottinger, E., Katz, L., Somarriba, R., Sharma, V., Katz, C.L., Marin, D.B., DePierro, J., Charney, D.S.: A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation. JMIR Formative Research. 5, e26590 (2021).
     
  • 28.
    Vaid, A., Chan, L., Chaudhary, K., Jaladanki, S., Paranjpe, I., Russak, A., Kia, A., Timsina, P., Levin, M., He, J., Bottinger, E., Charney, A., Fayad, Z., Coca, S., Glicksberg, B., Nadkarni, G.: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clinical Journal of the American Society of Nephrology. CJN.17311120 (2021).
     
  • 29.
    Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Charney, A., Miotto, R., Glicksberg, B.S., Levin, M., Nabeel, I., Aberg, J., Reich, D., Charney, D., Bottinger, E.P., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study. Journal of Medical Internet Research. 23, e26107 (2021).
     
  • 30.
    Sasso, A., Morgenstern, J., Musmann, F., Arnrich, B.: Devicely: A Python package for reading, timeshifting and writing sensor data. Journal of Open Source Software. 6, 3679 (2021).
     

2020

  • 1.
    Sigel, K., Swartz, T., Golden, E., Paranjpe, I., Somani, S., Richter, F., Freitas, J.K.D., Miotto, R., Zhao, S., Polak, P., Mutetwa, T., Factor, S., Mehandru, S., Mullen, M., Cossarini, F., Bottinger, E., Fayad, Z., Merad, M., Gnjatic, S., Aberg, J., Charney, A., Nadkarni, G., Glicksberg, B.S.: Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City. Clinical Infectious Diseases. (2020).
     
  • 2.
    Vaid, A., Somani, S., Russak, A.J., Freitas, J.K.D., Chaudhry, F.F., Paranjpe, I., Johnson, K.W., Lee, S.J., Miotto, R., Richter, F., Zhao, S., Beckmann, N.D., Naik, N., Kia, A., Timsina, P., Lala, A., Paranjpe, M., Golden, E., Danieletto, M., Singh, M., Meyer, D., OtextquotesingleReilly, P.F., Huckins, L., Kovatch, P., Finkelstein, J., Freeman, R.M., Argulian, E., Kasarskis, A., Percha, B., Aberg, J.A., Bagiella, E., Horowitz, C.R., Murphy, B., Nestler, E.J., Schadt, E.E., Cho, J.H., Cordon-Cardo, C., Fuster, V., Charney, D.S., Reich, D.L., Bottinger, E.P., Levin, M.A., Narula, J., Fayad, Z.A., Just, A.C., Charney, A.W., Nadkarni, G.N., Glicksberg, B.S.: Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal of Medical Internet Research. 22, e24018 (2020).
     
  • 3.
    Borchert, F., Lohr, C., Modersohn, L., Langer, T., Follmann, M., Sachs, J.P., Hahn, U., Schapranow, M.-P.: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines. Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. pp. 38–48. Association for Computational Linguistics, Online (2020).
     
  • 4.
    Kappattanavar, A.M., da Cruz, H.F., Arnrich, B., Böttinger, E.: Position Matters: Sensor Placement for SittingPosture Classification. ICHI ’20: Proceedings of the 2020 IEEE International Conference on Healthcare Informatics. IEEE (2020).
     
  • 5.
    Konak, O., Wegner, P., Arnrich, B.: IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition. Sensors. 20, 7179 (2020).
     
  • 6.
    Hirten, R.P., Danieletto, M., Tomalin, L., Choi, K.H., Zweig, M., Golden, E., Kaur, S., Helmus, D., Biello, A., Pyzik, R., Charney, A., Miotto, R., Glicksberg, B.S., Levin, M., Nabeel, I., Aberg, J., Reich, D., Charney, D., Bottinger, E.P., Keefer, L., Suarez-Farinas, M., Nadkarni, G.N., Fayad, Z.A.: Physiological Data from a Wearable Device Identifies SARS-CoV-2 Infection and Symptoms and Predicts COVID-19 Diagnosis: Observational Study (Preprint). Journal of Medical Internet Research. (2020).
     
  • 7.
    Freitas, J.K.D., Johnson, K.W., Golden, E., Nadkarni, G.N., Dudley, J.T., Bottinger, E.P., Glicksberg, B.S., Miotto, R.: Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records. (2020).
     
  • 8.
    Whiffin, N., Armean, and I.M., Kleinman, A., Marshall, J.L., Minikel, E.V., Goodrich, J.K., Quaife, N.M., Cole, J.B., Wang, Q., Karczewski, K.J., Cummings, B.B., Francioli, L., Laricchia, K., Guan, A., Alipanahi, B., Morrison, P., Baptista, M.A.S., Merchant, K.M., Ware, J.S., Havulinna, A.S., Iliadou, B., Lee, J.-J., Nadkarni, G.N., Whiteman, C., Daly, M., Esko, T., Hultman, C., Loos, R.J.F., Milani, L., Palotie, A., Pato, C., Pato, M., Saleheen, D., Sullivan, P.F., Alföldi, J., Cannon, P., MacArthur, D.G., and: The effect of LRRK2 loss-of-function variants in humans. Nature Medicine. 26, 869–877 (2020).
     
  • 9.
    Chan, L., Jaladanki, S.K., Somani, S., Paranjpe, I., Kumar, A., Zhao, S., Kaufman, L., Leisman, S., Sharma, S., He, J.C., Murphy, B., Fayad, Z.A., Levin, M.A., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19. Clinical Journal of the American Society of Nephrology. 16, 452–455 (2020).
     
  • 10.
    Wenric, S., Jeff, J.M., Joseph, T., Yee, M.-C., Belbin, G.M., Obeng, A.O., Ellis, S.B., Bottinger, E.P., Gottesman, O., Levin, M.A., Kenny, E.E.: Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry. The Pharmacogenomics Journal. 21, 174–189 (2020).
     
  • 11.
    da Cruz, H.F., Pfahringer, B., Martensen, T., Schneider, F., Meyer, A., Bottinger, E., Schapranow, M.-P.: Using Interpretability Approaches to Update Black-Box Clinical Prediction Models: an External Validation Study in Nephrology. Artificial Intelligence in Medicine. 101982 (2020).
     
  • 12.
    Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., Lala, A., Kia, A., Timsina, P., Li, L., Freeman, R., Chen, R., Narula, J., Just, A.C., Horowitz, C., Fayad, Z., Cordon-Cardo, C., Schadt, E., Levin, M.A., Reich, D.L., Fuster, V., Murphy, B., He, J.C., Charney, A.W., Bottinger, E.P., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: AKI in Hospitalized Patients with COVID-19. Journal of the American Society of Nephrology. 32, 151–160 (2020).
     
  • 13.
    Musmann, F., Sasso, A., Arnrich, B.: ALPS: A Web Platform for Analysing Multimodal Sensor Data in the Context of Digital Health. 2020 IEEE International Conference on Healthcare Informatics (ICHI). pp. 1–12. IEEE Computer Society, Los Alamitos, CA, USA (2020).
     
  • 14.
    Somani, S.S., Richter, and F., Fuster, V., Freitas, J.K.D., Naik, N., Sigel, K., Bottinger, E.P., Levin, M.A., Fayad, Z., Just, A.C., Charney, A.W., Zhao, S., Glicksberg, B.S., Lala, A., Nadkarni, G.N.: Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19. Journal of General Internal Medicine. 35, 2838–2844 (2020).
     
  • 15.
    Chaudhary, K., Vaid, A., Duffy, Áine, Paranjpe, I., Jaladanki, S., Paranjpe, M., Johnson, K., Gokhale, A., Pattharanitima, P., Chauhan, K., O’Hagan, R., Vleck, T.V., Coca, S.G., Cooper, R., Glicksberg, B., Bottinger, E.P., Chan, L., Nadkarni, G.N.: Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury. Clinical Journal of the American Society of Nephrology. CJN.09330819 (2020).
     
  • 16.
    Paranjpe, I., Chaudhary, K., Paranjpe, M., O’Hagan, R., Manna, S., Jaladanki, S., Kapoor, A., Horowitz, C., DeFelice, N., Cooper, R., Glicksberg, B., Bottinger, E.P., Just, A.C., Nadkarni, G.N.: Association of APOL1 Risk Genotype and Air Pollution for Kidney Disease. Clinical Journal of the American Society of Nephrology. 15, 401–403 (2020).
     
  • 17.
    Drimalla, H., Scheffer, T., Landwehr, N., Baskow, I., Roepke, S., Behnia, B., Dziobek, I.: Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT). npj digital medicine. 3, (2020).
     
  • 18.
    Remy, S., Pufahl, L., Sachs, J.P., Böttinger, E., Weske, M.: Event Log Generation in a Health System: A Case Study. Lecture Notes in Computer Science. pp. 505–522. Springer International Publishing (2020).
     
  • 19.
    Slosarek, T., Wohlbrandt, A., Böttinger, E.: Using CEF Digital Service Infrastructures in the Smart4Health Project for the Exchange of Electronic Health Records. arXiv preprint arXiv:2001.01477. (2020).
     
  • 20.
    Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., Lala, A., Kia, A., Timsina, P., Li, L., Freeman, R., Chen, R., Narula, J., Just, A.C., Horowitz, C., Fayad, Z., Cordon-Cardo, C., Schadt, E., Levin, M.A., Reich, D.L., Fuster, V., Murphy, B., He, J.C., Charney, A.W., Böttinger, E.P., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N., Li, L.: AKI in Hospitalized Patients with COVID-19. Journal of the American Society of Nephrology. ASN.2020050615 (2020).
     
  • 21.
    Paranjpe, I., Russak, A., Freitas, J.K.D., Lala, A., Miotto, R., Vaid, A., Johnson, K.W., Danieletto, M., Golden, E., Meyer, D., Singh, M., Somani, S., Manna, S., Nangia, U., Kapoor, A., OtextquotesingleHagan, R., OtextquotesingleReilly, P.F., Huckins, L.M., Glowe, P., Kia, A., Timsina, P., Freeman, R.M., Levin, M.A., Jhang, J., Firpo, A., Kovatch, P., Finkelstein, J., Aberg, J.A., Bagiella, E., Horowitz, C.R., Murphy, B., Fayad, Z.A., Narula, J., Nestler, E.J., Fuster, V., Cordon-Cardo, C., Charney, D.S., Reich, D.L., Just, A.C., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Nadkarni, G.: Clinical Characteristics of Hospitalized Covid-19 Patients in New York City. medRxiv. Version 1 (April 23, 2020 – 03:18) (2020).
     
  • 22.
    Chan, L., Jaladanki, S.K., Somani, S., Paranjpe, I., Kumar, A., Zhao, S., Kaufman, L., Leisman, S., Sharma, S., He, J.C., Murphy, B., Fayad, Z.A., Levin, M.A., Bottinger, E.P., Charney, A.W., Glicksberg, B.S., Coca, S.G., Nadkarni, G.N.: Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19. Clinical Journal of the American Society of Nephrology. CJN.12360720 (2020).
     
  • 23.
    Sasso, A.M., Datta, S., Jeitler, M., Steckhan, N., Kessler, C.S., Michalsen, A., Arnrich, B., Boettinger, E.: HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population. International Conference on Artificial Intelligence in Medicine. pp. 325–335. Springer (2020).
     
  • 24.
    Kraus, M., Mathew Stephen, M., Schapranow, M.-P.: Eatomics: Shiny exploration of quantitative proteomics data. Journal of Proteome Research. (2020).
     
  • 25.
    Schmitt, F., Sundermeier, J., Bohn, N., Morassi Sasso, A.: Spotlight on Women in Tech: Fostering an Inclusive Workforce when Exploring and Exploiting Digital Innovation Potentials. ICIS Proceedings (2020).
     

2019

  • 1.
    Slosarek, T., Kraus, M., Schapranow, M.-P., Bottinger, E.: Qualitative Comparison of Selected Indel Detection Methods for RNA-Seq Data. International Work-Conference on Bioinformatics and Biomedical Engineering. pp. 166–177. Springer (2019).
     
  • 2.
    Freitas da Cruz, H., Pfahringer, B., Schneider, F., Meyer, A., Schapranow, M.-P.: External Validation of a Black-Box Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences?. Proceedings of 17th Conference on Artificial Intelligence in Medicine. pp. 191–201 (2019).
     
  • 3.
    Konak, O., Freitas Da Cruz, H., Thiele, M., Golla, D., Schapranow, M.-P.: An Information and Communication Platform Supporting Analytics for Elderly Care. 5th International Conference on Information for Ageing Well, Communication Technologies e Health (2019).
     
  • 4.
    Freitas da Cruz, H., Schneider, F., Schapranow, M.-P.: Prediction of Acute Kidney Injury in Cardiac Surgery Patients: Interpretation using Local Interpretable Model-agnostic Explanations. Proceedings of the 12th International Conference on Biomedical Engineering Systems and Technologies. pp. 380–387. , Prague, Czech Republic (2019).
     
  • 5.
    Drimalla, H., Baskow, I., Roepke, S., Behnia, B., Dziobek, I.: Imitation und Erkennung von Emotionen bei Autismus-Spektrum-Störungen - eine computerbasierte Analyse des fazialen Emotionsausdrucks. 12. Wissenschaftliche Tagung Autismus-Spektrum. (2019).
     
  • 6.
    Freitas da Cruz, H., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., Schapranow, M.-P.: MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research. Proceedings of the 19th IEEE International Conference on Bioinformatics and Biomedicine. , Athens, Greece (2019).
     
  • 7.
    Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Knowledge Distillation from Machine Learning Models for Prediction of Hemodialysis Outcomes. International Journal On Advances in Life Sciences. 11, 33–43 (2019).
     
  • 8.
    Datta, S., Schraplau, A., da Cruz, H.F., Sachs, J.P., Mayer, F., Böttinger, E.: A Machine Learning Approach for Non-Invasive Diagnosis of Metabolic Syndrome. 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). pp. 933–940. IEEE (2019).
     
  • 9.
    Wojcik, G.L., Graff, M., Nishimura, K.K., Tao, R., Haessler, J., Gignoux, C.R., Highland, H.M., Patel, Y.M., Sorokin, E.P., Avery, C.L., Belbin, G.M., Bien, S.A., Cheng, I., Cullina, S., Hodonsky, C.J., Hu, Y., Huckins, L.M., Jeff, J., Justice, A.E., Kocarnik, J.M., Lim, U., Lin, B.M., Lu, Y., Nelson, S.C., Park, S.-S.L., Poisner, H., Preuss, M.H., Richard, M.A., Schurmann, C., Setiawan, V.W., Sockell, A., Vahi, K., Verbanck, M., Vishnu, A., Walker, R.W., Young, K.L., Zubair, N., Acuna-Alonso, V., Ambite, J.L., Barnes, K.C., Boerwinkle, E., Bottinger, E.P., Bustamante, C.D., Caberto, C., Canizales-Quinteros, S., Conomos, M.P., Deelman, E., Do, R., Doheny, K., Fernandez-Rhodes, L., Fornage, M., Hailu, B., Heiss, G., Henn, B.M., Hindorff, L.A., Jackson, R.D., Laurie, C.A., Laurie, C.C., Li, Y., Lin, D.-Y., Moreno-Estrada, A., Nadkarni, G., Norman, P.J., Pooler, L.C., Reiner, A.P., Romm, J., Sabatti, C., Sandoval, K., Sheng, X., Stahl, E.A., Stram, D.O., Thornton, T.A., Wassel, C.L., Wilkens, L.R., Winkler, C.A., Yoneyama, S., Buyske, S., Haiman, C.A., Kooperberg, C., Le Marchand, L., Loos, R.J.F., Matise, T.C., North, K.E., Peters, U., Kenny, E.E., Carlson, C.S.: Genetic analyses of diverse populations improves discovery for complex traits. Nature. 570, 514–518 (2019).
     
  • 10.
    Drimalla, H., Landwehr, N., Hess, U., Dziobek, I.: From face to face: the contribution of facial mimicry to cognitive and emotional empathy. Cognition and Emotion. 33, 1672–1686 (2019).
     
  • 11.
    Belbin, G.M., Wenric, S., Cullina, S., Glicksberg, B.S., Moscati, A., Wojcik, G.L., Shemirani, R., Beckmann, N.D., Cohain, A., Sorokin, E.P., Park, D.S., Ambite, J.-L., Ellis, S., Auton, A., Bottinger, E.P., Cho, J.H., Loos, R.J., Abul husn, N.S., Zaitlen, N.A., Gignoux, C.R., Kenny, E.E., and: Towards a fine-scale population health monitoring system. (2019).
     

2018

  • 1.
    Kraus, M., Hesse, G., Slosarek, T., Danner, M., Kesar, A., Bhushan, A., Schapranow, M.-P.: DEAME-Differential Expression Analysis Made Easy. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. pp. 162–174. Springer (2018).
     
  • 2.
    Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Prediction of Patient Outcomes after Renal Replacement Therapy in Intensive Care. Proceedings of the 3rd International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing (2018).
     
  • 3.
    Freitas da Cruz, H., Gebhardt, M., Becher, F., Schapranow, M.-P.: Interactive Data Exploration Supporting Elderly Care Planning. Proceedings of the 10th International Conference on eHealth, Telemedicine, and Social Medicine (2018).