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- 2017 - Cheng, L. F., Darnell, G., Chivers, C., Draugelis, M. E., Li, K., & Engelhardt, B. E. (2017). Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction. arXiv preprint arXiv:1703.09112.
<li>2017 - Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E Engelhardt. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units, Proceedings of Uncertainty in Artificial Intelligence (UAI), 2017.</li>
<li>2017 - Giannini, H. M., Chivers, C., Draugelis, M., Hanish, A., Fuchs, B., Donnelly, P., & Mikkelsen, M. E. (2017). Development and Implementation of a Machine-Learning Algorithm for Early Identification of Sepsis in A Multi-Hospital Academic Healthcare System. American Journal of Respiratory and Critical Care Medicine. 195.</li>
<li>2017 - C Chivers, V Panchanadam, M Draugelis, D Do, A Huffenberger, M Stepanik, S Gudowski, A Hanish, MB Fuchs, M Frazer Cereda, M Pierce, C Maguire, M Olson, A Gabrielli, WC Hanson, P Sullivan, PJ Brennan. (2017) Accuracy and Opportunity of An Automated Ventilator and Sedation Weaning Alert Program. Am J Respir Crit Care Med. 195.</li>
<li>2017 - AMIA Panel, The Good, The Bad, and The Ugly of Deploying and Adopting Machine Learning-Based Models in Clinical Practice, Yin Aphinyanaphongs ; David Holmes ; Berkman Sahiner ; Parsa Mirhaji ; Michael Draugelis</li>
<li>2018 - BJ Anderson, D Do, C Chivers, K Choi, E Gitelman, M Draugelis, S Mehta, BD Fuchs. (2018) Clinical Impact of an Electronic Dashboard and Alert System That Promotes Sedation Minimization and Ventilator Liberation. Am J Respir Crit Care Med. 197.</li>
<li>2018 - Steven W Gudowski, Margarete Pierce, Barry Fuchs and Michael Frazer. Rapid Liberation From Mechanical Ventilation, the ICU and Hospital by Using an ICU Dashboard and Alert Program, Respiratory Care October 2018, 63 (Suppl 10) 3025698</li>
<li>2019 - Courtright, K.R, Chivers, C., Becker, M., Regli, S.H., Pepper, L.C, Draugelis, M.E. (in prep) A pilot study of targeted palliative care using an electronic health record mortality prediction model. Pre-Publication Link </li>
<li>2019 - Ginestra JC, Giannini HM, Schweickert WD, Meadows L, Lynch MJ, Pavan K, Chivers CJ, Draugelis M, Donnelly PJ, Fuchs BD, Umscheid CA. Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock. Crit Care Med. 2019 May 24. doi: 10.1097/CCM.0000000000003803. [Epub ahead of print] PMID: 31135500</li>
<li>2019 - Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang, Corey Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt. (2019). Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes. arXiv preprint arXiv:1906.00226.</li>
<li>2019 - Ravi Parikh, Christopher Manz 2019 October 21, Supportive Care in Oncology Symposium, Oral Abstract Session A, Abstract Title: Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer.</li>
<li>2019 - AMIA Panel, Translating, Implementing, Deploying, and Evaluating Clinical Interventions Using Machine Learning-Based Predictive Models: Illustrative Case Studies on November 17, 2019. Y. Aphinyanaphongs, NYU Langone Health; J. Wilt, Oschner Health System; C. Chivers, Penn Medicine; M. Sendak, Duke Institute for Health Innovation</li>
<li>2019 - Parikh RB, Manz C, Chivers C, et al. Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997</li>
<li>2019 - Beaulieu-Jones B, Finlayson SG, Chivers C, et al. Trends and Focus of Machine Learning Applications for Health Research. JAMA Netw Open. Published online October 25, 20192(10):e1914051. doi:10.1001/jamanetworkopen.2019.14051</li>
<li>2019 - Parikh RB, Teeple S, Navathe AS. Addressing Bias in Artificial Intelligence in Health Care. JAMA. Published online November 22, 2019. doi: <a href="https://doi.org/10.1001/jama.2019.18058" target="_blank">https://doi.org/10.1001/jama.2019.18058</a> | <a href="https://jamanetwork.com/journals/jama/fullarticle/2756196" target="_blank">https://jamanetwork.com/journals/jama/fullarticle/2756196</a></li>
<li>2020 - Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer. RB Parikh, C Manz, C Chivers, SB Regli, J Braun, JA Jones, R Mamtani, ... Journal of Clinical Oncology 37 (31_suppl), 131-131</li>
<li>2020 - Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge … CR Manz, RB Parikh, CN Evans, C Chivers, SH Regli, JE Bekelman, ..., Contemporary Clinical Trials 90, 105951</li>
<li>2020 - Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic, GE Weissman, A Crane-Droesch, C Chivers, TB Luong, A Hanish, M. Becker Annals of internal medicine </li>
<li>2020 - Modeling Epidemics With Compartmental Models., Tolles J, Luong T., _JAMA._ Published online May 27, 2020. doi:10.1001/jama.2020.8420 </li>
<li>2020 - Manz CR, Chen J, Liu M, et al. Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer. _JAMA Oncol._ Published online September 24, 2020. doi:10.1001/jamaoncol.2020.4331</li>
<li>2020 - Manz CR, Parikh RB, Small DS, et al. Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial. _JAMA Oncol._ Published online October 15, 2020. doi:10.1001/jamaoncol.2020.4759</li>
- 2015 – Operational sepsis predictive system https://www.usnews.com/news/articles/2016-09-23/penn-medicines-michael-draugelis-predictive-analytics-is-promising-health-care-tool
<li>2015 - Penn Medicine Taps Big Data To Save Lives <a target="_blank" href="https://www.informationweek.com/healthcare/analytics/penn-medicine-taps-big-data-to-save-lives/d/d-id/1322498">https://www.informationweek.com/healthcare/analytics/penn-medicine-taps-big-data-to-save-lives/d/d-id/1322498</a></li>
<li>2016 – Operational Vent Weaning screening <a target="_blank" href="https://www.cio.com/article/3336486/penn-medicine-shortens-icu-stays-with-real-time-data.html">https://www.cio.com/article/3336486/penn-medicine-shortens-icu-stays-with-real-time-data.html</a></li>
<li>2016 – Operational Congestive Heart Failure predictive system <a target="_blank" href="https://www.informationweek.com/strategic-cio/penn-medicine-using-data-to-save-patient-lives/d/d-id/1325364">https://www.informationweek.com/strategic-cio/penn-medicine-using-data-to-save-patient-lives/d/d-id/1325364</a></li>
<li>2017 – Operational maternal morbidity risk system <a target="_blank" href="https://penntoday.upenn.edu/spotlights/harnessing-powerful-technology-improve-patient-care">https://penntoday.upenn.edu/spotlights/harnessing-powerful-technology-improve-patient-care</a></li>
<li>2017 – Operational predictive lung cancer risk <a target="_blank" href="https://medcitynews.com/2017/02/penn-medicine-wields-big-data-help-lung-cancer-patients-avoid-er/">https://medcitynews.com/2017/02/penn-medicine-wields-big-data-help-lung-cancer-patients-avoid-er/</a></li>
<li>2017 - UPenn Uses Machine Learning, EHRs to Target Severe Sepsis <a target="_blank" href="https://healthitanalytics.com/news/upenn-uses-machine-learning-ehrs-to-target-severe-sepsis">https://healthitanalytics.com/news/upenn-uses-machine-learning-ehrs-to-target-severe-sepsis</a></li>
<li>2018 - Operational predictive Palliative Care screening system <a target="_blank" href="https://www.philly.com/philly/health/sepsis-algorithm-end-of-life-care-20180626.html\">https://www.philly.com/philly/health/sepsis-algorithm-end-of-life-care-20180626.html\</a></li>
<li>2019 - Machine learning could reduce testing, improve treatment for intensive care patients <a target="_blank" href="https://www.princeton.edu/news/2019/01/15/machine-learning-could-reduce-testing-improve-treatment-intensive-care-patients">https://www.princeton.edu/news/2019/01/15/machine-learning-could-reduce-testing-improve-treatment-intensive-care-patients</a></li>
<li>2019 - Vent Program <a target="_blank" href="https://www.cio.com/article/3336486/penn-medicine-shortens-icu-stays-with-real-time-data.html">https://www.cio.com/article/3336486/penn-medicine-shortens-icu-stays-with-real-time-data.html</a></li>
<li>2019 - Vent Program <a target="_blank" href="https://www.modernhealthcare.com/operations/penn-medicine-reduces-icu-stays-clinical-alert-app-built-house?itx%5bidio%5d=6241141&ito=792&itq=0a965593-d31e-4a6c-8955-87da7cceeb9f">https://www.modernhealthcare.com/operations/penn-medicine-reduces-icu-stays-clinical-alert-app-built-house?itx%5bidio%5d=6241141&ito=792&itq=0a965593-d31e-4a6c-8955-87da7cceeb9f</a></li>
<li>2019 - Palliative Connect <a target="_blank" href="https://healthitanalytics.com/news/predictive-analytics-tool-increases-palliative-care-consultations">https://healthitanalytics.com/news/predictive-analytics-tool-increases-palliative-care-consultations</a></li>
<li>2020 - Penn Medicine released a digital tool to help hospitals with COVID-19 capacity planning, https://technical.ly/philly/2020/03/17/penn-medicine-chime-digital-tool-hospitals-covid19-coronavirus-capacity-planning/</li>
<li>2020 - Pandemic Surge Models in the Time of Severe Acute Respiratory Syndrome Coronavirus-2: Wrong or Useful? https://www.acpjournals.org/doi/10.7326/M20-1956</li>
<li>2020 - Disease modelers are wary of reopening the country. Here’s how they arrive at their verdict. https://www.washingtonpost.com/graphics/2020/health/disease-modeling-coronavirus-cases-reopening/ </li>
<li>2020 - How many people in Pa. will be infected with the coronavirus? Scientific modeling is trying to find the answer. https://www.inquirer.com/health/coronavirus/spl/pennsylvania-coronavirus-modeling-projections-hospitals-estimates-20200327.html</li>
- 2016 - Penn Research Team Receives $5 Million Grant to Use Big Data to Improve Health, https://www.pennmedicine.org/news/news-releases/2016/february/penn-research-team-receives-5
<li>2016 - Penn Medicine Information Services Ranks Fourth in InformationWeek's Elite 100, <a href="https://www.pennmedicine.org/news/news-releases/2016/may/penn-medicine-information-serv" target="_blank">https://www.pennmedicine.org/news/news-releases/2016/may/penn-medicine-information-serv</a></li>
<li>2018 - Penn Medicine shortens ICU stays with real-time data, Digital Edge 50, <a href="https://www.cio.com/article/3336486/penn-medicine-shortens-icu-stays-with-real-time-data.html" target="_blank">https://www.cio.com/article/3336486/penn-medicine-shortens-icu-stays-with-real-time-data.html</a></li>