Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS) von Le, Sidney, Pellegrini, Emily, Green-Saxena, Abigail, Summers, Charlotte, Hoffman, Jana, Calvert, Jacob, Das, Ritankar

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Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data von Mohamadlou, Hamid, Lynn-Palevsky, Anna, Barton, Christopher, Chettipally, Uli, Shieh, Lisa, Calvert, Jacob, Saber, Nicholas R., Das, Ritankar

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A Computational Approach to Early Sepsis Detection von Calvert, Jacob S, Price, Daniel A, Chettipally, Uli K, Barton, Christopher W, Feldman, Mitchell D, Hoffman, Jana L, Jay, Melissa, Das, Ritankar

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A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients von Ryan, Logan, Mataraso, Samson, Siefkas, Anna, Pellegrini, Emily, Barnes, Gina, Green-Saxena, Abigail, Hoffman, Jana, Calvert, Jacob, Das, Ritankar

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Energy landscapes for a machine learning application to series data von Ballard, Andrew J., Stevenson, Jacob D., Das, Ritankar, Wales, David J.

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OpenMedLM: prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models von Maharjan, Jenish, Garikipati, Anurag, Singh, Navan Preet, Cyrus, Leo, Sharma, Mayank, Ciobanu, Madalina, Barnes, Gina, Thapa, Rahul, Mao, Qingqing, Das, Ritankar

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Pediatric Severe Sepsis Prediction Using Machine Learning von Le, Sidney, Hoffman, Jana, Barton, Christopher, Fitzgerald, Julie C, Allen, Angier, Pellegrini, Emily, Calvert, Jacob, Das, Ritankar

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Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients von Calvert, Jacob, Saber, Nicholas, Hoffman, Jana, Das, Ritankar

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Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction von Mohamadlou, Hamid, Panchavati, Saarang, Calvert, Jacob, Lynn-Palevsky, Anna, Le, Sidney, Allen, Angier, Pellegrini, Emily, Green-Saxena, Abigail, Barton, Christopher, Fletcher, Grant, Shieh, Lisa, Stark, Philip B, Chettipally, Uli, Shimabukuro, David, Feldman, Mitchell, Das, Ritankar

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A machine learning approach to predicting risk of myelodysplastic syndrome von Radhachandran, Ashwath, Garikipati, Anurag, Iqbal, Zohora, Siefkas, Anna, Barnes, Gina, Hoffman, Jana, Mao, Qingqing, Das, Ritankar

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Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus von Allen, Angier, Iqbal, Zohora, Green-Saxena, Abigail, Hurtado, Myrna, Hoffman, Jana, Mao, Qingqing, Das, Ritankar

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Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461... von Burdick, Hoyt, Pino, Eduardo, Gabel-Comeau, Denise, Gu, Carol, Roberts, Jonathan, Le, Sidney, Slote, Joseph, Saber, Nicholas, Pellegrini, Emily, Green-Saxena, Abigail, Hoffman, Jana, Das, Ritankar

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Machine learning determination of applied behavioral analysis treatment plan type von Maharjan, Jenish, Garikipati, Anurag, Dinenno, Frank A., Ciobanu, Madalina, Barnes, Gina, Browning, Ella, DeCurzio, Jenna, Mao, Qingqing, Das, Ritankar

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A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients von Allen, Angier, Siefkas, Anna, Pellegrini, Emily, Burdick, Hoyt, Barnes, Gina, Calvert, Jacob, Mao, Qingqing, Das, Ritankar

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Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment von Garikipati, Anurag, Ciobanu, Madalina, Singh, Navan Preet, Barnes, Gina, Decurzio, Jenna, Mao, Qingqing, Das, Ritankar

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Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from... von Burdick, Hoyt, Pino, Eduardo, Gabel-Comeau, Denise, McCoy, Andrea, Gu, Carol, Roberts, Jonathan, Le, Sidney, Slote, Joseph, Pellegrini, Emily, Green-Saxena, Abigail, Hoffman, Jana, Das, Ritankar

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COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients von Stewart, Mark, Rodriguez-Watson, Carla, Albayrak, Adem, Asubonteng, Julius, Belli, Andrew, Brown, Thomas, Cho, Kelly, Das, Ritankar, Eldridge, Elizabeth, Gatto, Nicolle, Gelman, Alice, Gerlovin, Hanna, Goldberg, Stuart L., Hansen, Eric, Hirsch, Jonathan, Ho, Yuk-Lam, Ip, Andrew, Izano, Monika, Jones, Jason, Justice, Amy C., Klesh, Reyna, Kuranz, Seth, Lam, Carson, Mao, Qingqing, Mataraso, Samson, Mera, Robertino, Posner, Daniel C., Rassen, Jeremy A., Siefkas, Anna, Schrag, Andrew, Tourassi, Georgia, Weckstein, Andrew, Wolf, Frank, Bhat, Amar, Winckler, Susan, Sigal, Ellen, Allen, Jeff

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Predicting Falls in Long-term Care Facilities: Machine Learning Study von Thapa, Rahul, Garikipati, Anurag, Shokouhi, Sepideh, Hurtado, Myrna, Barnes, Gina, Hoffman, Jana, Calvert, Jacob, Katzmann, Lynne, Mao, Qingqing, Das, Ritankar

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Using electronic health record collected clinical variables to predict medical intensive care unit mortality von Calvert, Jacob, Mao, Qingqing, Hoffman, Jana L, Jay, Melissa, Desautels, Thomas, Mohamadlou, Hamid, Chettipally, Uli, Das, Ritankar

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Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data von Radhachandran, Ashwath, Garikipati, Anurag, Zelin, Nicole S., Pellegrini, Emily, Ghandian, Sina, Calvert, Jacob, Hoffman, Jana, Mao, Qingqing, Das, Ritankar

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