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Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment.
Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 1903–1911
Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. 2017 · 1911
Earlier work this paper cites.
A mathematical model of communication
Claude E Shannon and Warren Weaver. 1949 · 1949
Earlier work this paper cites.
Snowball sampling
Leo A Goodman. 1961 · 1961
Earlier work this paper cites.
Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis
Roger C Bone, Robert A Balk, Frank B Cerra, R Phillip Dellinger, Alan M Fein, William A Knaus, Roland MH Schein, and William J Sibbald. 1992 · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Validation of a modified Early Warning Score in medical admissions
Christian P Subbe, Michael Kruger, Peter Rutherford, and L Gemmel. 2001 · 2001
Earlier work this paper cites.
Semi-structured interviews and focus groups
Robyn Longhurst. 2003 · 2003
Earlier work this paper cites.
A general inductive approach for analyzing qualitative evaluation data
David R Thomas. 2006 · 2006
Earlier work this paper cites.
Implementation of the Surviving Sepsis Campaign guidelines for severe sepsis and septic shock: We could go faster
Massimo Zambon, Marcello Ceola, Roberto Almeida de Castro, Antonino Gullo, and Jean-Louis Vincent. 2008 · 2007
Earlier work this paper cites.
Designing the user interface: Strategies for effective human-computer interaction
Ben Shneiderman and Catherine Plaisant. 2010 · 2010
Earlier work this paper cites.
The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death
Gary B Smith, David R Prytherch, Paul Meredith, Paul E Schmidt, and Peter I Featherstone. 2013 · 2013
Earlier work this paper cites.
Medical Decision Making
Harold C Sox, Michael C. Higgins, and Douglas K. Owens. 2013 · 2013
Earlier work this paper cites.
Automated electronic medical record sepsis detection in the emergency department
Su Q Nguyen, Edwin Mwakalindile, James S Booth, Vicki Hogan, Jordan Morgan, Charles T Prickett, John P Donnelly, and Henry E Wang. 2014 · 2014
Earlier work this paper cites.
Risk prediction with electronic health records: A deep learning approach. In Proceedings of the 2016 SIAM international conference on data mining . SIAM, 432–440
Yu Cheng, Fei Wang, Ping Zhang, and Jianying Hu. 2016 · 2016
Earlier work this paper cites.
Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 · 2016
Earlier work this paper cites.
MIMIC-III, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
Earlier work this paper cites.
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
Mervyn Singer, Clifford S. Deutschman, Christopher Warren Seymour, Manu Shankar-Hari, Djillali Annane, Michael Bauer, Rinaldo Bellomo, Gordon R. Bernard, Jean-Daniel Chiche, Craig M. Coopersmith, Richard S. Hotchkiss, Mitchell M. Levy, John C. Marshall, Greg S. Martin, Steven M. Opal, Gordon D. Rubenfeld, Tom van der Poll, Jean-Louis Vincent, and Derek C. Angus. 2016 · 2016
Earlier work this paper cites.
Investigating the heart pump implant decision process: opportunities for decision support tools to help. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems . 4477–4488
Qian Yang, John Zimmerman, Aaron Steinfeld, Lisa Carey, and James F Antaki. 2016 · 2016
Earlier work this paper cites.
Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding. In 2016 IEEE 16th International Conference on Data Mining (ICDM) . 749–758
Zihao Zhu, Changchang Yin, Buyue Qian, Yu Cheng, Jishang Wei, and Fei Wang. 2016 · 2016
Earlier work this paper cites.
Patient subtyping via time-aware LSTM networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 65–74
Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, and Jiayu Zhou. 2017 · 2017
Earlier work this paper cites.
A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs
Estefanía Caballero-Ruiz, Gema García-Sáez, Mercedes Rigla, María Villaplana, Belen Pons, and M Elena Hernando. 2017 · 2017
Earlier work this paper cites.
GRAM: graph-based attention model for healthcare representation learning. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 787–795
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun. 2017 · 2017
Earlier work this paper cites.
qSOFA has poor sensitivity for prehospital identification of severe sepsis and septic shock
Maia Dorsett, Melissa Kroll, Clark S Smith, Phillip Asaro, Stephen Y Liang, and Hawnwan P Moy. 2017 · 2017
Earlier work this paper cites.
LSTM for septic shock: Adding unreliable labels to reliable predictions. In 2017 IEEE International Conference on Big Data (Big Data) . 1233–1242
Yuan Zhang, Chen Lin, Min Chi, Julie Ivy, Muge Capan, and Jeanne M. Huddleston. 2017 · 2017
Earlier work this paper cites.
Sepsis and septic shock
Maurizio Cecconi, Laura Evans, Mitchell Levy, and Andrew Rhodes. 2018 · 2018
Earlier work this paper cites.
Time-to-Positivity of Blood Cultures in Children With Sepsis
Alexa Dierig, Christoph Berger, Philipp K. A. Agyeman, Sara Bernhard-Stirnemann, Eric Giannoni, Martin Stocker, Klara M. Posfay-Barbe, Anita Niederer-Loher, Christian R. Kahlert, Alex Donas, Paul Hasters, Christa Relly, Thomas Riedel, Christoph Aebi, Luregn J. Schlapbach, and Swiss Pediatric Sepsis Study Heininger, Ulrich and. 2018 · 2018
Earlier work this paper cites.
Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management
Min Kyung Lee. 2018 · 2018
Earlier work this paper cites.
Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM. In 2018 IEEE international conference on healthcare informatics (ICHI) . IEEE, 219–228
Chen Lin, Yuan Zhang, Julie Ivy, Muge Capan, Ryan Arnold, Jeanne M Huddleston, and Min Chi. 2018 · 2018
Earlier work this paper cites.
Kame: Knowledge-based attention model for diagnosis prediction in healthcare. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 743–752
Fenglong Ma, Quanzeng You, Houping Xiao, Radha Chitta, Jing Zhou, and Jing Gao. 2018 · 2018
Earlier work this paper cites.
Big data and black-box medical algorithms
W Nicholson Price. 2018 · 2018
Earlier work this paper cites.
Early prediction of sepsis in EMR records using traditional ML techniques and deep learning LSTM networks. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . IEEE, 4038–4041
Mohammed Saqib, Ying Sha, and May D Wang. 2018 · 2018
Earlier work this paper cites.
AI for medical imaging goes deep
Daniel SW Ting, Yong Liu, Philippe Burlina, Xinxing Xu, Neil M Bressler, and Tien Y Wong. 2018 · 2018
Earlier work this paper cites.
Artificial intelligence in healthcare
Kun-Hsing Yu, Andrew L Beam, and Isaac S Kohane. 2018 · 2018
Earlier work this paper cites.
Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–13
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019 · 2019
Earlier work this paper cites.
Minimal impact of implemented early warning score and best practice alert for patient deterioration
Armando D Bedoya, Meredith E Clement, Matthew Phelan, Rebecca C Steorts, Cara O’Brien, and Benjamin A Goldstein. 2019 · 2019
Earlier work this paper cites.
Human-centered tools for coping with imperfect algorithms during medical decision-making. In Proceedings of the 2019 chi conference on human factors in computing systems . 1–14
Carrie J Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S Corrado, Martin C Stumpe, et al · 2019
Earlier work this paper cites.
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making
Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019b · 2019
Cited alongside, same era.
Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–12
Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O’Connell, Terrance Gray, F. Maxwell Harper, and Haiyi Zhu. 2019 · 2019
Cited alongside, same era.
Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation
Norman Lance Downing, Joshua Rolnick, Sarah F Poole, Evan Hall, Alexander J Wessels, Paul Heidenreich, and Lisa Shieh. 2019 · 2019
Cited alongside, same era.
Sepsis: early recognition and optimized treatment
Hwan Il Kim and Sunghoon Park. 2019 · 2019
Cited alongside, same era.
The Epic Sepsis Model Falls Short—The Importance of External Validation
Anand R. Habib, Anthony L. Lin, and Richard W. Grant. 2021 · 2021
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Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 659, 14 pages
Maia Jacobs, Jeffrey He, Melanie F. Pradier, Barbara Lam, Andrew C. Ahn, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, and Krzysztof Z. Gajos. 2021 · 2021
Later among the works it cites.
Integrating Al algorithms into the clinical workflow
Krishna Juluru, Hao-Hsin Shih, Krishna Nand Keshava Murthy, Pierre Elnajjar, Amin El-Rowmeim, Christopher Roth, Brad Genereaux, Josef Fox, Eliot Siegel, and Daniel L Rubin. 2021 · 2021
Later among the works it cites.
Towards a science of human-ai decision making: a survey of empirical studies
Vivian Lai, Chacha Chen, Q Vera Liao, Alison Smith-Renner, and Chenhao Tan. 2021 · 2021
Later among the works it cites.
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On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA) (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 29–38
Vivian Lai and Chenhao Tan. 2019 · 2019
Cited alongside, same era.
Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU
R. Liu, J.L. Greenstein, S.J. Granite, J.C. Fackler, M.M. Bembea, S.V. Sarma, and R.L. Winslow. 2019 · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Machine learning in medicine
Alvin Rajkomar, Jeffrey Dean, and Isaac Kohane. 2019 · 2019
Cited alongside, same era.
What AI means for doctors and doctoring
Nirav R Shah and Thomas H Lee. 2019 · 2019
Cited alongside, same era.
High-performance medicine: the convergence of human and artificial intelligence
Eric J Topol. 2019 · 2019
Cited alongside, same era.
Comparison of SIRS, qSOFA, and NEWS for the early identification of sepsis in the Emergency Department
Omar A Usman, Asad A Usman, and Michael A Ward. 2019 · 2019
Cited alongside, same era.
Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students
Xuhai Xu, Prerna Chikersal, Afsaneh Doryab, Daniella K. Villalba, Janine M. Dutcher, Michael J. Tumminia, Tim Althoff, Sheldon Cohen, Kasey G. Creswell, J. David Creswell, Jennifer Mankoff, and Anind K. Dey. 2019 · 2019
Cited alongside, same era.
A Human-AI Collaborative Approach for Clinical Decision Making on Rehabilitation Assessment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 392, 14 pages
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, and Sergi Bermúdez Bermúdez i Badia. 2021 · 2021
Later among the works it cites.
Who Is Included in Human Perceptions of AI?: Trust and Perceived Fairness around Healthcare AI and Cultural Mistrust. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 138, 14 pages
Min Kyung Lee and Katherine Rich. 2021 · 2021
Later among the works it cites.
Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 78, 16 pages
Zhuoran Lu and Ming Yin. 2021 · 2021
Later among the works it cites.
Designing Interactive Transfer Learning Tools for ML Non-Experts. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 364, 15 pages
Swati Mishra and Jeffrey M Rzeszotarski. 2021 · 2021
Later among the works it cites.
Uncertainty Visualization
Lace Padilla, Matthew Kay, and Jessica Hullman. 2021 · 2021
Later among the works it cites.
SSP: Early prediction of sepsis using fully connected LSTM-CNN model
Alireza Rafiei, Alireza Rezaee, Farshid Hajati, Soheila Gheisari, and Mojtaba Golzan. 2021 · 2021
Later among the works it cites.
“Brilliant AI Doctor” in Rural Clinics: Challenges in AI-Powered Clinical Decision Support System Deployment. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21) . Association for Computing Machinery, New York, NY, USA, Article 697, 18 pages
Dakuo Wang, Liuping Wang, Zhan Zhang, Ding Wang, Haiyi Zhu, Yvonne Gao, Xiangmin Fan, and Feng Tian. 2021 · 2021
Later among the works it cites.
Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making. In 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21) . Association for Computing Machinery, New York, NY, USA, 318–328
Xinru Wang and Ming Yin. 2021 · 2021
Later among the works it cites.
External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients
Andrew Wong, Erkin Otles, John P Donnelly, Andrew Krumm, Jeffrey McCullough, Olivia DeTroyer-Cooley, Justin Pestrue, Marie Phillips, Judy Konye, Carleen Penoza, et al · 2021
Later among the works it cites.
To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
Julia Amann, Dennis Vetter, Stig Nikolaj Blomberg, Helle Collatz Christensen, Megan Coffee, Sara Gerke, Thomas K Gilbert, Thilo Hagendorff, Sune Holm, Michelle Livne, et al · 2022
Later among the works it cites.
Improving Biomedical Question Answering by Data Augmentation and Model Weighting
Yongping Du, Jingya Yan, Yuxuan Lu, Yiliang Zhao, and Xingnan Jin. 2023 · 2022
Later among the works it cites.
Human-Centered Explainable AI (HCXAI): Beyond Opening the Black-Box of AI. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22) . Association for Computing Machinery, New York, NY, USA, Article 109, 7 pages
Upol Ehsan, Philipp Wintersberger, Q. Vera Liao, Elizabeth Anne Watkins, Carina Manger, Hal Daumé III, Andreas Riener, and Mark O Riedl. 2022 · 2022
Later among the works it cites.
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 1362–1374
Riccardo Fogliato, Shreya Chappidi, Matthew Lungren, Paul Fisher, Diane Wilson, Michael Fitzke, Mark Parkinson, Eric Horvitz, Kori Inkpen, and Besmira Nushi. 2022 · 2022
Later among the works it cites.
Critical assessment of transformer-based AI models for German clinical notes
Manuel Lentzen, Sumit Madan, Vanessa Lage-Rupprecht, Lisa Kühnel, Juliane Fluck, Marc Jacobs, Mirja Mittermaier, Martin Witzenrath, Peter Brunecker, Martin Hofmann-Apitius, et al · 2022
Later among the works it cites.
Contextual Embedding and Model Weighting by Fusing Domain Knowledge on Biomedical Question Answering. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (Northbrook, Illinois) (BCB ’22) . Association for Computing Machinery, New York, NY, USA, Article 54, 4 pages
Yuxuan Lu, Jingya Yan, Zhixuan Qi, Zhongzheng Ge, and Yongping Du. 2022 · 2022
Later among the works it cites.
Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease
Yuka Otaki, Ananya Singh, Paul Kavanagh, Robert JH Miller, Tejas Parekh, Balaji K Tamarappoo, Tali Sharir, Andrew J Einstein, Mathews B Fish, Terrence D Ruddy, et al · 2022
Later among the works it cites.
Multiple forecast visualizations (mfvs): Trade-offs in trust and performance in multiple covid-19 forecast visualizations
Lace Padilla, Racquel Fygenson, Spencer C Castro, and Enrico Bertini. 2022 · 2022
Later among the works it cites.
Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians
Jasmine A Silvestri, Tyler E Kmiec, Nicholas S Bishop, Susan H Regli, and Gary E Weissman. 2022 · 2022
Later among the works it cites.
Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for depression treatment using a simulation center
Myriam Tanguay-Sela, David Benrimoh, Christina Popescu, Tamara Perez, Colleen Rollins, Emily Snook, Eryn Lundrigan, Caitrin Armstrong, Kelly Perlman, Robert Fratila, et al · 2022
Later among the works it cites.
Challenges of human—machine collaboration in risky decision-making
Wei Xiong, Hongmiao Fan, Liang Ma, and Chen Wang. 2022 · 2022
Later among the works it cites.
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. In Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 24655–24692
Xuhai Xu, Han Zhang, Yasaman Sefidgar, Yiyi Ren, Xin Liu, Woosuk Seo, Jennifer Brown, Kevin Kuehn, Mike Merrill, Paula Nurius, Shwetak Patel, Tim Althoff, Margaret Morris, Eve Riskin, Jennifer Mankoff, and Anind Dey. 2022 · 2022
Later among the works it cites.
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . Association for Computing Machinery, New York, NY, USA, 2316–2326
Changchang Yin, Ruoqi Liu, Jeffrey Caterino, and Ping Zhang. 2022 · 2022
Later among the works it cites.
Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 15, 19 pages
Eleanor R. Burgess, Ivana Jankovic, Melissa Austin, Nancy Cai, Adela Kapuścińska, Suzanne Currie, J. Marc Overhage, Erika S Poole, and Jofish Kaye. 2023 · 2023
Closest in time.
Survey of explainable AI techniques in healthcare
Ahmad Chaddad, Jihao Peng, Jian Xu, and Ahmed Bouridane. 2023 · 2023
Closest in time.
Are Two Heads Better Than One in AI-Assisted Decision Making? Comparing the Behavior and Performance of Groups and Individuals in Human-AI Collaborative Recidivism Risk Assessment. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 348, 18 pages
Chun-Wei Chiang, Zhuoran Lu, Zhuoyan Li, and Ming Yin. 2023 · 2023
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Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study
John Cull, Robert Brevetta, Jeff Gerac, Shanu Kothari, and Dawn Blackhurst. 2023 · 2023
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Factors associated with variability in the performance of a proprietary sepsis prediction model across 9 networked hospitals in the US
Patrick G Lyons, Mackenzie R Hofford, C Yu Sean, Andrew P Michelson, Philip RO Payne, Catherine L Hough, and Karandeep Singh. 2023 · 2023
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Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 754, 18 pages
Venkatesh Sivaraman, Leigh A Bukowski, Joel Levin, Jeremy M. Kahn, and Adam Perer. 2023 · 2023
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GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling
Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman Sefidgar, Woosuk Seo, Kevin S. Kuehn, Jeremy F. Huckins, Margaret E. Morris, Paula S. Nurius, Eve A. Riskin, Shwetak Patel, Tim Althoff, Andrew Campbell, Anind K. Dey, and Jennifer Mankoff. 2023 · 2023
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Harnessing Biomedical Literature to Calibrate Clinicians’ Trust in AI Decision Support Systems. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 14, 14 pages
Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, and Fei Wang. 2023 · 2023
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Clinician-Facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI
Hubert D. Zając, Dana Li, Xiang Dai, Jonathan F. Carlsen, Finn Kensing, and Tariq O. Andersen. 2023 · 2023
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