@article{AartsAndersonAndersonetal.2015, author = {Aarts, Alexander A. and Anderson, Joanna E. and Anderson, Christopher J. and Attridge, Peter R. and Attwood, Angela and Axt, Jordan and Babel, Molly and Bahnik, Stepan and Baranski, Erica and Barnett-Cowan, Michael and Bartmess, Elizabeth and Beer, Jennifer and Bell, Raoul and Bentley, Heather and Beyan, Leah and Binion, Grace and Borsboom, Denny and Bosch, Annick and Bosco, Frank A. and Bowman, Sara D. and Brandt, Mark J. and Braswell, Erin and Brohmer, Hilmar and Brown, Benjamin T. and Brown, Kristina and Bruening, Jovita and Calhoun-Sauls, Ann and Callahan, Shannon P. and Chagnon, Elizabeth and Chandler, Jesse and Chartier, Christopher R. and Cheung, Felix and Christopherson, Cody D. and Cillessen, Linda and Clay, Russ and Cleary, Hayley and Cloud, Mark D. and Cohn, Michael and Cohoon, Johanna and Columbus, Simon and Cordes, Andreas and Costantini, Giulio and Alvarez, Leslie D. Cramblet and Cremata, Ed and Crusius, Jan and DeCoster, Jamie and DeGaetano, Michelle A. and Della Penna, Nicolas and den Bezemer, Bobby and Deserno, Marie K. and Devitt, Olivia and Dewitte, Laura and Dobolyi, David G. and Dodson, Geneva T. and Donnellan, M. Brent and Donohue, Ryan and Dore, Rebecca A. and Dorrough, Angela and Dreber, Anna and Dugas, Michelle and Dunn, Elizabeth W. and Easey, Kayleigh and Eboigbe, Sylvia and Eggleston, Casey and Embley, Jo and Epskamp, Sacha and Errington, Timothy M. and Estel, Vivien and Farach, Frank J. and Feather, Jenelle and Fedor, Anna and Fernandez-Castilla, Belen and Fiedler, Susann and Field, James G. and Fitneva, Stanka A. and Flagan, Taru and Forest, Amanda L. and Forsell, Eskil and Foster, Joshua D. and Frank, Michael C. and Frazier, Rebecca S. and Fuchs, Heather and Gable, Philip and Galak, Jeff and Galliani, Elisa Maria and Gampa, Anup and Garcia, Sara and Gazarian, Douglas and Gilbert, Elizabeth and Giner-Sorolla, Roger and Gl{\"o}ckner, Andreas and G{\"o}llner, Lars and Goh, Jin X. and Goldberg, Rebecca and Goodbourn, Patrick T. and Gordon-McKeon, Shauna and Gorges, Bryan and Gorges, Jessie and Goss, Justin and Graham, Jesse and Grange, James A. and Gray, Jeremy and Hartgerink, Chris and Hartshorne, Joshua and Hasselman, Fred and Hayes, Timothy and Heikensten, Emma and Henninger, Felix and Hodsoll, John and Holubar, Taylor and Hoogendoorn, Gea and Humphries, Denise J. and Hung, Cathy O. -Y. and Immelman, Nathali and Irsik, Vanessa C. and Jahn, Georg and Jaekel, Frank and Jekel, Marc and Johannesson, Magnus and Johnson, Larissa G. and Johnson, David J. and Johnson, Kate M. and Johnston, William J. and Jonas, Kai and Joy-Gaba, Jennifer A. and Kappes, Heather Barry and Kelso, Kim and Kidwell, Mallory C. and Kim, Seung Kyung and Kirkhart, Matthew and Kleinberg, Bennett and Knezevic, Goran and Kolorz, Franziska Maria and Kossakowski, Jolanda J. and Krause, Robert Wilhelm and Krijnen, Job and Kuhlmann, Tim and Kunkels, Yoram K. and Kyc, Megan M. and Lai, Calvin K. and Laique, Aamir and Lakens, Daniel and Lane, Kristin A. and Lassetter, Bethany and Lazarevic, Ljiljana B. and LeBel, Etienne P. and Lee, Key Jung and Lee, Minha and Lemm, Kristi and Levitan, Carmel A. and Lewis, Melissa and Lin, Lin and Lin, Stephanie and Lippold, Matthias and Loureiro, Darren and Luteijn, Ilse and Mackinnon, Sean and Mainard, Heather N. and Marigold, Denise C. and Martin, Daniel P. and Martinez, Tylar and Masicampo, E. J. and Matacotta, Josh and Mathur, Maya and May, Michael and Mechin, Nicole and Mehta, Pranjal and Meixner, Johannes and Melinger, Alissa and Miller, Jeremy K. and Miller, Mallorie and Moore, Katherine and M{\"o}schl, Marcus and Motyl, Matt and M{\"u}ller, Stephanie M. and Munafo, Marcus and Neijenhuijs, Koen I. and Nervi, Taylor and Nicolas, Gandalf and Nilsonne, Gustav and Nosek, Brian A. and Nuijten, Michele B. and Olsson, Catherine and Osborne, Colleen and Ostkamp, Lutz and Pavel, Misha and Penton-Voak, Ian S. and Perna, Olivia and Pernet, Cyril and Perugini, Marco and Pipitone, R. Nathan and Pitts, Michael and Plessow, Franziska and Prenoveau, Jason M. and Rahal, Rima-Maria and Ratliff, Kate A. and Reinhard, David and Renkewitz, Frank and Ricker, Ashley A. and Rigney, Anastasia and Rivers, Andrew M. and Roebke, Mark and Rutchick, Abraham M. and Ryan, Robert S. and Sahin, Onur and Saide, Anondah and Sandstrom, Gillian M. and Santos, David and Saxe, Rebecca and Schlegelmilch, Rene and Schmidt, Kathleen and Scholz, Sabine and Seibel, Larissa and Selterman, Dylan Faulkner and Shaki, Samuel and Simpson, William B. and Sinclair, H. Colleen and Skorinko, Jeanine L. M. and Slowik, Agnieszka and Snyder, Joel S. and Soderberg, Courtney and Sonnleitner, Carina and Spencer, Nick and Spies, Jeffrey R. and Steegen, Sara and Stieger, Stefan and Strohminger, Nina and Sullivan, Gavin B. and Talhelm, Thomas and Tapia, Megan and te Dorsthorst, Anniek and Thomae, Manuela and Thomas, Sarah L. and Tio, Pia and Traets, Frits and Tsang, Steve and Tuerlinckx, Francis and Turchan, Paul and Valasek, Milan and Van Aert, Robbie and van Assen, Marcel and van Bork, Riet and van de Ven, Mathijs and van den Bergh, Don and van der Hulst, Marije and van Dooren, Roel and van Doorn, Johnny and van Renswoude, Daan R. and van Rijn, Hedderik and Vanpaemel, Wolf and Echeverria, Alejandro Vasquez and Vazquez, Melissa and Velez, Natalia and Vermue, Marieke and Verschoor, Mark and Vianello, Michelangelo and Voracek, Martin and Vuu, Gina and Wagenmakers, Eric-Jan and Weerdmeester, Joanneke and Welsh, Ashlee and Westgate, Erin C. and Wissink, Joeri and Wood, Michael and Woods, Andy and Wright, Emily and Wu, Sining and Zeelenberg, Marcel and Zuni, Kellylynn}, title = {Estimating the reproducibility of psychological science}, series = {Science}, volume = {349}, journal = {Science}, number = {6251}, publisher = {American Assoc. for the Advancement of Science}, address = {Washington}, organization = {Open Sci Collaboration}, issn = {1095-9203}, doi = {10.1126/science.aac4716}, pages = {8}, year = {2015}, abstract = {Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47\% of original effect sizes were in the 95\% confidence interval of the replication effect size; 39\% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68\% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.}, language = {en} } @article{VaidSomaniRussaketal.2020, author = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.}, title = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation}, series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, volume = {22}, journal = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, number = {11}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1439-4456}, doi = {10.2196/24018}, pages = {19}, year = {2020}, abstract = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.}, language = {en} }