You can find my papers listed by category below, or you can view them on my Google Scholar profile.

Probability (9) 🔗

Theory 🔗

[pdf] Hierarchical self-organization in fixed-magnetization particle systems. With Shunhao Oh and Dana Randall. Preprint.

[pdf] A local–global principle for nonequilibrium steady states. With Dana Randall. PNAS, 2024.

[pdf] Shunhao Oh, Joseph Briones, Jacob Calvert, Noah Egan, Dana Randall, and Andréa Richa. Single bridge formation in self-organizing particle systems. DISC 2024.

[pdf] Critical numerosity in collective behavior. Preprint.

[pdf] Existence of a phase transition in harmonic activation and transport. Electronic Journal of Probability, 2023.

[pdf] Collapse and diffusion in harmonic activation and transport. With Shirshendu Ganguly and Alan Hammond. Forum of Mathematics, Sigma, 2023.

[pdf] Brownian structure in the KPZ fixed point. With Alan Hammond and Milind Hegde. Astérisque, 2023.

Applications 🔗

[pdf] Fernando Lejarza, Jacob Calvert, Misty Attwood, Daniel Evans, and Qingqing Mao. Optimal discharge of patients from intensive care via a data-driven policy learning framework. Operations Research for Health Care, 2023.

[pdf] Jacob Calvert, Márton Balázs, and Katerina Michaelides. Unifying particle-based and continuum models of hillslope evolution with a probabilistic scaling technique. Journal of Geophysical Research: Earth Surface, 123(12):3124–3146, 2018.

Clinical data science (42) 🔗

COVID-19 🔗

[pdf] Carson Lam, Anna Siefkas, Nicole S. Zelin, Gina Barnes, R. Phillip Dellinger, Jean-Louis Vincent, Gregory Braden, Hoyt Burdick, Jana Hoffman, Jacob Calvert, Qingqing Mao, and Ritankar Das. Machine learning as a precision-medicine approach to prescribing COVID-19 pharmacotherapy with remdesivir or corticosteroids. Clinical Therapeutics, 43(5):871–885, 2022/04/25 2021.

[full text] Carson Lam, Jacob Calvert, Anna Siefkas, Gina Barnes, Emily Pellegrini, Abigail Green-Saxena, Jana Hoffman, Qingqing Mao, and Ritankar Das. Personalized stratification of hospitalization risk amidst COVID-19: A machine learning approach. Health Policy and Technology, 10(3):100554, 2021.

[pdf] Jenish Maharjan, Jacob Calvert, Emily Pellegrini, Abigail Green-Saxena, Jana Hoffman, Andrea McCoy, Qingqing Mao, and Ritankar Das. Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays. Clinical Imaging, 80:268–273, 2021.

[full text] Hoyt Burdick, Carson Lam, Samson Mataraso, Anna Siefkas, Gregory Braden, R. Phillip Dellinger, Andrea McCoy, Jean-Louis Vincent, Abigail Green-Saxena, Gina Barnes, Jana Hoffman, Jacob Calvert, Emily Pellegrini, and Ritankar Das. Is machine learning a better way to identify COVID-19 patients who might benefit from hydroxychloroquine treatment?—the IDENTIFY trial. Journal of Clinical Medicine, 9(12), 2020.

[full text] Hoyt Burdick, Carson Lam, Samson Mataraso, Anna Siefkas, Gregory Braden, R. Phillip Dellinger, Andrea McCoy, Jean-Louis Vincent, Abigail Green-Saxena, Gina Barnes, Jana Hoffman, Jacob Calvert, Emily Pellegrini, and Ritankar Das. Prediction of respiratory decompensation in COVID-19 patients using machine learning: The READY trial. Computers in Biology and Medicine, 124:103949, 2020.

Sepsis 🔗

[pdf] Jenish Maharjan, Rahul Thapa, Jacob Calvert, Misty Attwood, Sepideh Shokouhi, Satish Casie Chetty, Zohora Iqbal, Navan Singh, Rome Arnold, Jana Hoffman, Samson Mataraso, Anurag Garikipati, Carson Lam, and Qingqing Mao. A new standard for sepsis prediction algorithms: using time-dependent analysis for earlier clinically relevant alerts. Preprint available at http://dx.doi.org/10.2139/ssrn.4130480.

[full text] Sidney Le, Jana Hoffman, Christopher Barton, Julie C. Fitzgerald, Angier Allen, Emily Pellegrini, Jacob Calvert, and Ritankar Das. Pediatric severe sepsis prediction using machine learning. Frontiers in Pediatrics, 7, 2019.

[full text] Christopher Barton, Uli Chettipally, Yifan Zhou, Zirui Jiang, Anna Lynn-Palevsky, Sidney Le, Jacob Calvert, and Ritankar Das. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs. Computers in Biology and Medicine, 109:79–84, 2019.

[full text] Jacob Calvert, Nicholas Saber, Jana Hoffman, and Ritankar Das. Machine-learning-based laboratory developed test for the diagnosis of sepsis in high-risk patients. Diagnostics, 9(1), 2019.

[pdf] Qingqing Mao, Melissa Jay, Jana L Hoffman, Jacob Calvert, Christopher Barton, David Shimabukuro, Lisa Shieh, Uli Chettipally, Grant Fletcher, Yaniv Kerem, Yifan Zhou, and Ritankar Das. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open, 8(1), 2018.

[pdf] Jacob Calvert, Jana Hoffman, Christopher Barton, David Shimabukuro, Michael Ries, Uli Chettipally, Yaniv Kerem, Melissa Jay, Samson Mataraso, and Ritankar Das. Cost and mortality impact of an algorithm-driven sepsis prediction system. Journal of Medical Economics, 20(6):646–651, 2017. PMID: 28294646.

[pdf] Jacob Calvert, Daniel A. Price, Uli K. Chettipally, Christopher W. Barton, Mitchell D. Feldman, Jana L. Hoffman, Melissa Jay, and Ritankar Das. A computational approach to early sepsis detection. Computers in Biology and Medicine, 74:69–73, 2016.

[full text] Thomas Desautels, Jacob Calvert, Jana Hoffman, Melissa Jay, Yaniv Kerem, Lisa Shieh, David Shimabukuro, Uli Chettipally, Mitchell D Feldman, Christopher Barton, David J Wales, and Ritankar Das. Prediction of sepsis in the intensive care unit with minimal electronic health record data: A machine learning approach. JMIR Med Inform, 4(3):e28, Sep 2016.

[full text] Jacob Calvert, Thomas Desautels, Uli Chettipally, Christopher Barton, Jana Hoffman, Melissa Jay, Qingqing Mao, Hamid Mohamadlou, and Ritankar Das. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Annals of Medicine and Surgery, 8:50–55, 2016.

Acute kidney injury 🔗

[full text] Sidney Le, Angier Allen, Jacob Calvert, Paul M. Palevsky, Gregory Braden, Sharad Patel, Emily Pellegrini, Abigail Green-Saxena, Jana Hoffman, and Ritankar Das. Convolutional neural network model for intensive care unit acute kidney injury prediction. Kidney International Reports, 6(5):1289–1298, 2021.

[full text] Sidney Le, Emily Pellegrini, Abigail Green-Saxena, Charlotte Summers, Jana Hoffman, Jacob Calvert, and Ritankar Das. Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS). Journal of Critical Care, 60:96–102, 2020.

[pdf] Hamid Mohamadlou, Anna Lynn-Palevsky, Christopher Barton, Uli Chettipally, Lisa Shieh, Jacob Calvert, Nicholas R. Saber, and Ritankar Das. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Canadian Journal of Kidney Health and Disease, 5:2054358118776326, 2018. PMID: 30094049.

Infection 🔗

[full text] Maxime Faucher, Satish Casie Chetty, Sepideh Shokouhi, Gina Barnes, Keyvan Rahmani, Jacob Calvert, Qingqing Mao. Early Prediction of Ventilator-Associated Pneumonia in ICU Patients Using An Interpretable Machine Learning Algorithm . Preprints 2022060149 (doi: 10.20944/preprints202206.0149.v1), 2022.

[full text] Saarang Panchavati, Nicole S. Zelin, Anurag Garikipati, Emily Pellegrini, Zohora Iqbal, Gina Barnes, Jana Hoffman, Jacob Calvert, Qingqing Mao, and Ritankar Das. A comparative analysis of machine learning approaches to predict C. difficile infection in hospitalized patients. American Journal of Infection Control, 50(3):250–257, 2022.

[full text] Keyvan Rahmani, Anurag Garikipati, Gina Barnes, Jana Hoffman, Jacob Calvert, Qingqing Mao, and Ritankar Das. Early prediction of central line associated bloodstream infection using machine learning. American Journal of Infection Control, 50(4):440–445, 2022.

[full text] Christine Giang, Jacob Calvert, Keyvan Rahmani, Gina Barnes, Anna Siefkas, Abigail Green-Saxena, Jana Hoffman, Qingqing Mao, and Ritankar Das. Predicting ventilator-associated pneumonia with machine learning. Medicine, 100(23), 2021.

Mortality 🔗

[full text] Jenish Maharjan, Sidney Le, Abigail Green-Saxena, Manan Khattar, Jacob Calvert, Emily Pellegrini, Jana Hoffman, and Ritankar Das. Mortality, disease progression, and disease burden of acute kidney injury in alcohol use disorder subpopulation. The American Journal of the Medical Sciences, 2022.

[pdf] Ashwath Radhachandran, Anurag Garikipati, Nicole S. Zelin, Emily Pellegrini, Sina Ghandian, Jacob Calvert, Jana Hoffman, Qingqing Mao, and Ritankar Das. Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Mining, 14(1):23, 2021.

[pdf] Hamid Mohamadlou, Saarang Panchavati, Jacob Calvert, Anna Lynn-Palevsky, Sidney Le, Angier Allen, Emily Pellegrini, Abigail Green-Saxena, Christopher Barton, Grant Fletcher, Lisa Shieh, Philip B Stark, Uli Chettipally, David Shimabukuro, Mitchell Feldman, and Ritankar Das. Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction. Health Informatics Journal, 26(3):1912–1925, 2020. PMID: 31884847.

[full text] Angier Allen, Samson Mataraso, Anna Siefkas, Hoyt Burdick, Gregory Braden, R Phillip Dellinger, Andrea McCoy, Emily Pellegrini, Jana Hoffman, Abigail Green-Saxena, Gina Barnes, Jacob Calvert, and Ritankar Das. A racially unbiased, machine learning approach to prediction of mortality: Algorithm development study. JMIR Public Health Surveill, 6(4):e22400, Oct 2020.

[pdf] Thomas Desautels, Jacob Calvert, Jana Hoffman, Qingqing Mao, Melissa Jay, Grant Fletcher, Chris Barton, Uli Chettipally, Yaniv Kerem, and Ritankar Das. Using transfer learning for improved mortality prediction in a data-scarce hospital setting. Biomedical Informatics Insights, 9:1178222617712994, 2017. PMID: 28638239.

[full text] Jacob Calvert, Qingqing Mao, Jana L. Hoffman, Melissa Jay, Thomas Desautels, Hamid Mohamadlou, Uli Chettipally, and Ritankar Das. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Annals of Medicine and Surgery, 11:52–57, 2016.

[pdf] Jacob Calvert, Qingqing Mao, Angela J. Rogers, Christopher Barton, Melissa Jay, Thomas Desautels, Hamid Mohamadlou, Jasmine Jan, and Ritankar Das. A computational approach to mortality prediction of alcohol use disorder inpatients. Computers in Biology and Medicine, 75:74–79, 2016.

Pulmonary embolism and myocardial infarction 🔗

[full text] Jieru Shen, Satish Casie Chetty, Sepideh Shokouhi, Jenish Maharjan, Yevheniy Chuba, Jacob Calvert, and Qingqing Mao. Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients. Thrombosis Research, 216:14–21, 2022.

[pdf] Logan Ryan, Jenish Maharjan, Samson Mataraso, Gina Barnes, Jana Hoffman, Qingqing Mao, Jacob Calvert, and Ritankar Das. Predicting pulmonary embolism among hospitalized patients with machine learning algorithms. Pulmonary Circulation, 12(1):e12013, 2022.

[pdf] Saarang Panchavati, Carson Lam, Nicole S. Zelin, Emily Pellegrini, Gina Barnes, Jana Hoffman, Anurag Garikipati, Jacob Calvert, Qingqing Mao, and Ritankar Das. Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification. Healthcare Technology Letters, 8(6):139–147, 2021.

[pdf] Logan Ryan, Samson Mataraso, Anna Siefkas, Emily Pellegrini, Gina Barnes, Abigail Green-Saxena, Jana Hoffman, Jacob Calvert, and Ritankar Das. A machine learning approach to predict deep venous thrombosis among hospitalized patients. Clinical and Applied Thrombosis/Hemostasis, 27:1076029621991185, 2021. PMID: 33625875.

Acute coronary syndrome 🔗

[pdf] Cecilia Zeng, Wang Xiang, Angier Allen, Sepideh Shokouhi, Satish Casie Chetty, Gina Barnes, Zohora Iqbal, Peiling Tsou, Navan Singh, Jacob Calvert, Myrna Hurtado, Jana Hoffman, Qingqing Mao. A machine learning approach for unbiased diagnosis of acute coronary syndrome in the emergency department. Preprint available at Research Square https://doi.org/10.21203/rs.3.rs-1743328/v1, 14 June 2022.

[pdf] Cecilia Zeng, Wang Xiang, Angier Allen, Sepideh Shokouhi, Satish Casie Chetty, Gina Barnes, Zohora Iqbal, Peiling Tsou, Jacob Calvert, Jana Hoffman, Qingqing Mao. Assessment of sex and racial biases in electronic health records of emergency department patients with acute coronary syndrome. Preprint available at Research Square https://doi.org/10.21203/rs.3.rs-1735655/v1, 08 June 2022.

Stroke 🔗

[full text] Jenish Maharjan, Yasha Ektefaie, Logan Ryan, Samson Mataraso, Gina Barnes, Sepideh Shokouhi, Abigail Green-Saxena, Jacob Calvert, Qingqing Mao, and Ritankar Das. Enriching the study population for ischemic stroke therapeutic trials using a machine learning algorithm. Frontiers in Neurology, 12, 2022.

[full text] Angier Allen, Anna Siefkas, Emily Pellegrini, Hoyt Burdick, Gina Barnes, Jacob Calvert, Qingqing Mao, and Ritankar Das. A digital twins machine learning model for forecasting disease progression in stroke patients. Applied Sciences, 11(12), 2021.

Other 🔗

[full text] Chak Foon Tso, Carson Lam, Jacob Calvert, Qingqing Mao. Machine Learning Early Prediction of Respiratory Syncytial Virus in Pediatric Hospitalized Patients. Frontiers in Pediatrics, 10, 2022.

[full text] Rahul Thapa, Anurag Garikipati, Sepideh Shokouhi, Myrna Hurtado, Gina Barnes, Jana Hoffman, Jacob Calvert, Lynne Katzmann, Qingqing Mao, and Ritankar Das. Predicting falls in long-term care facilities: Machine learning study. JMIR Aging, 5(2):e35373, Apr 2022.

[pdf] Sina Ghandian, Rahul Thapa, Anurag Garikipati, Gina Barnes, Abigail Green-Saxena, Jacob Calvert, Qingqing Mao, and Ritankar Das. Machine learning to predict progression of non-alcoholic fatty liver to non-alcoholic steatohepatitis or fibrosis. JGH Open, 6(3):196–204, 2022.

[full text] Carson Lam, Chak Foon Tso, Abigail Green-Saxena, Emily Pellegrini, Zohora Iqbal, Daniel Evans, Jana Hoffman, Jacob Calvert, Qingqing Mao, and Ritankar Das. Semisupervised deep learning techniques for predicting acute respiratory distress syndrome from time-series clinical data: Model development and validation study. JMIR formative research, 5(9):e28028, September 2021.

[full text] Thomas Desautels, Ritankar Das, Jacob Calvert, Monica Trivedi, Charlotte Summers, David J Wales, and Ari Ercole. Prediction of early unplanned intensive care unit readmission in a uk tertiary care hospital: a cross-sectional machine learning approach. BMJ Open, 7(9), 2017.

[full text] Jacob Calvert, Daniel A Price, Christopher W Barton, Uli K Chettipally, and Ritankar Das. Discharge recommendation based on a novel technique of homeostatic analysis. Journal of the American Medical Informatics Association, 24(1):24–29, 03 2016.

Structural biology (3) 🔗

[full text] Minji Kim, Alex Kreig, Chun-Ying Lee, H. Tomas Rube, Jacob Calvert, Jun S. Song, and Sua Myong. Quantitative analysis and prediction of G-quadruplex forming sequences in double-stranded DNA. Nucleic Acids Research, 44(10):4807–4817, 04 2016.

[full text] Alex Kreig, Jacob Calvert, Janet Sanoica, Emily Cullum, Ramreddy Tipanna, and Sua Myong. G-quadruplex formation in double strand DNA probed by NMM and CV fluorescence. Nucleic Acids Research, 43(16):7961–7970, 07 2015.

[full text] Helen Hwang, Alex Kreig, Jacob Calvert, Justin Lormand, Yongho Kwon, James M. Daley, Patrick Sung, Patricia L. Opresko, and Sua Myong. Telomeric overhang length determines structural dynamics and accessibility to telomerase and alt-associated proteins. Structure, 22(6):842–853, 2014.