David Kartchner

			
@inproceedings{
  kartchner2023bioel,
  title={A Comprehensive Evaluation of Biomedical Entity Linking Models},
  author={Kartchner, David and Deng, Jennifer and Lohiya, Shubham and Kopparthi, Tejasri and Bathala, Prasanth and Domingo-Fern\'andez, Daniel and Mitchell, Cassie S},
  booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
  year={2023},
  url={https://openreview.net/forum?id=5Ob6DsDv2V}
}
		

			
@article{kartchner2023cvd, 
  title={Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19}, 
  volume={12}, 
  ISSN={2079-7737}, 
  url={http://dx.doi.org/10.3390/biology12091269}, 
  DOI={10.3390/biology12091269}, 
  number={9}, 
  journal={Biology}, 
  publisher={MDPI AG}, 
  author={Kartchner, David and McCoy, Kevin and Dubey, Janhvi and Zhang, Dongyu and Zheng, Kevin and Umrani, Rushda and Kim, James J. and Mitchell, Cassie S.}, 
  year={2023}, 
  month={Sep}, 
  pages={1269} 
}
		

			
@inproceedings{kartchner-etal-2023-zero-shot,
title = "Zero-Shot Information Extraction for Clinical Meta-Analysis using Large Language Models",
author = "Kartchner, David  and
  Al-Hussaini, Ifran  and
  Ramalingam, Selvi  and
  Kronick, Olivia  and
  Mitchell, Cassie",
booktitle = "Proceedings of the 22nd Workshop on Biomedical Language Processing",
month = July,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
doi = "10.18653/v1/2022.bionlp-1.1",
pages = "1--10",
abstract = "Meta-analysis of randomized clinical trials (RCTs) plays a crucial role in evidence-based medicine but can be labor-intensive and error-prone. This study explores the use of large language models to enhance the efficiency of aggregating results from randomized clinical trials (RCTs) at scale. We perform a detailed comparison of the performance of these models in zero-shot prompt-based information extraction from a diverse set of RCTs to traditional manual annotation methods. We analyze the results for two different meta-analyses aimed at drug repurposing in cancer therapy and pharmacovigilance in chronic myeloid leukemia. Our findings reveal that the best model for the two demonstrated tasks, ChatGPT, can generally extract correct information and identify when the desired information is missing from an article. We additionally conduct a systematic error analysis, documenting the prevalence of diverse error types encountered during the process of prompt-based information extraction. ",
}
		

			
@inproceedings{kartchner2023biosift,
  author  = {Kartchner, David and Al-Hussaini, Irfan and Turner, Haydn and Deng, Jennifer and Lohiya, Shugham and Bathala, Prasanth and Mitchell, Cassie},
  title   = {BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis},
  year    = {2023},
  maintitle = {SIGIR},
  booktitle = {46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  }
		

			@article{kartchner2022rule,
title={Rule-Enhanced Active Learning for Semi-Automated Weak Supervision},
author={Kartchner, David and Nakajima An, Davi and Ren, Wendi and Zhang, Chao and Mitchell, Cassie S},
journal={AI},
volume={3},
number={1},
pages={211--228},
year={2022},
publisher={MDPI}
}

		

			
@inproceedings{christensen2018machine,
    title={Machine learning methods for disease prediction with claims data},
    author={Christensen, Tanner and Frandsen, Abraham and Glazier, Seth and Humpherys, Jeffrey and Kartchner, David},
    booktitle={2018 IEEE International Conference on Healthcare Informatics (ICHI)},
    pages={467--4674},
    year={2018},
    organization={IEEE}
}
		

			
@article{horne2018short,
    title={Short-term elevation of fine particulate matter air pollution and acute lower respiratory infection},
    author={Horne, Benjamin D and Joy, Elizabeth A and Hofmann, Michelle G and Gesteland, Per H and Cannon, John B and Lefler, Jacob S and Blagev, Denitza P and Korgenski, E Kent and Torosyan, Natalie and Hansen, Grant I and others},
    journal={American journal of respiratory and critical care medicine},
    volume={198},
    number={6},
    pages={759--766},
    year={2018},
    publisher={American Thoracic Society}
}
		

			
@article{kartchner2023cvd, 
  title={Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19}, 
  volume={12}, 
  ISSN={2079-7737}, 
  url={http://dx.doi.org/10.3390/biology12091269}, 
  DOI={10.3390/biology12091269}, 
  number={9}, 
  journal={Biology}, 
  publisher={MDPI AG}, 
  author={Kartchner, David and McCoy, Kevin and Dubey, Janhvi and Zhang, Dongyu and Zheng, Kevin and Umrani, Rushda and Kim, James J. and Mitchell, Cassie S.}, 
  year={2023}, 
  month={Sep}, 
  pages={1269} 
}
		

			@article{kartchner2022rule,
title={Rule-Enhanced Active Learning for Semi-Automated Weak Supervision},
author={Kartchner, David and Nakajima An, Davi and Ren, Wendi and Zhang, Chao and Mitchell, Cassie S},
journal={AI},
volume={3},
number={1},
pages={211--228},
year={2022},
publisher={MDPI}
}

		

			
@article{mccoy2021biomedical,
    title={Biomedical Text Link Prediction for Drug Discovery: A Case Study with COVID-19},
    author={McCoy, Kevin and Gudapati, Sateesh and He, Lawrence and Horlander, Elaina and Kartchner, David and Kulkarni, Soham and Mehra, Nidhi and Prakash, Jayant and Thenot, Helena and Vanga, Sri Vivek and others},
    journal={Pharmaceutics},
    volume={13},
    number={6},
    pages={794},
    year={2021},
    publisher={Multidisciplinary Digital Publishing Institute}
}
		

			
@article{horne2018short,
    title={Short-term elevation of fine particulate matter air pollution and acute lower respiratory infection},
    author={Horne, Benjamin D and Joy, Elizabeth A and Hofmann, Michelle G and Gesteland, Per H and Cannon, John B and Lefler, Jacob S and Blagev, Denitza P and Korgenski, E Kent and Torosyan, Natalie and Hansen, Grant I and others},
    journal={American journal of respiratory and critical care medicine},
    volume={198},
    number={6},
    pages={759--766},
    year={2018},
    publisher={American Thoracic Society}
}
		

			
@inproceedings{
  kartchner2023bioel,
  title={A Comprehensive Evaluation of Biomedical Entity Linking Models},
  author={Kartchner, David and Deng, Jennifer and Lohiya, Shubham and Kopparthi, Tejasri and Bathala, Prasanth and Domingo-Fern\'andez, Daniel and Mitchell, Cassie S},
  booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
  year={2023},
  url={https://openreview.net/forum?id=5Ob6DsDv2V}
}
		

			
@inproceedings{kartchner2023biosift,
  author  = {Kartchner, David and Al-Hussaini, Irfan and Turner, Haydn and Deng, Jennifer and Lohiya, Shugham and Bathala, Prasanth and Mitchell, Cassie},
  title   = {BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis},
  year    = {2023},
  maintitle = {SIGIR},
  booktitle = {46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  }
		

			
@inproceedings{ren-etal-2020-denoising,
    title = "Denoising Multi-Source Weak Supervision for Neural Text Classification",
    author = "Ren, Wendi  and
        Li, Yinghao  and
        Su, Hanting  and
        Kartchner, David  and
        Mitchell, Cassie  and
        Zhang, Chao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.334",
    doi = "10.18653/v1/2020.findings-emnlp.334",
    pages = "3739--3754"
}
		

			
@inproceedings{christensen2018machine,
    title={Machine learning methods for disease prediction with claims data},
    author={Christensen, Tanner and Frandsen, Abraham and Glazier, Seth and Humpherys, Jeffrey and Kartchner, David},
    booktitle={2018 IEEE International Conference on Healthcare Informatics (ICHI)},
    pages={467--4674},
    year={2018},
    organization={IEEE}
}
		

			
@inproceedings{kartchner2017code2vec,
    title={Code2vec: Embedding and clustering medical diagnosis data},
    author={Kartchner, David and Christensen, Tanner and Humpherys, Jeffrey and Wade, Sean},
    booktitle={2017 IEEE International Conference on Healthcare Informatics (ICHI)},
    pages={386--390},
    year={2017},
    organization={IEEE}
}
		

			
@inproceedings{kartchner2017cost,
    title={Cost reduction via patient targeting and outreach: a statistical approach},
    author={Kartchner, David and Merrill, Andy and Wrathall, Jonathan},
    booktitle={2017 IEEE International Conference on Healthcare Informatics (ICHI)},
    pages={513--517},
    year={2017},
    organization={IEEE}
}
		

			
@inproceedings{kartchner-etal-2023-zero-shot,
title = "Zero-Shot Information Extraction for Clinical Meta-Analysis using Large Language Models",
author = "Kartchner, David  and
  Al-Hussaini, Ifran  and
  Ramalingam, Selvi  and
  Kronick, Olivia  and
  Mitchell, Cassie",
booktitle = "Proceedings of the 22nd Workshop on Biomedical Language Processing",
month = July,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
doi = "10.18653/v1/2022.bionlp-1.1",
pages = "1--10",
abstract = "Meta-analysis of randomized clinical trials (RCTs) plays a crucial role in evidence-based medicine but can be labor-intensive and error-prone. This study explores the use of large language models to enhance the efficiency of aggregating results from randomized clinical trials (RCTs) at scale. We perform a detailed comparison of the performance of these models in zero-shot prompt-based information extraction from a diverse set of RCTs to traditional manual annotation methods. We analyze the results for two different meta-analyses aimed at drug repurposing in cancer therapy and pharmacovigilance in chronic myeloid leukemia. Our findings reveal that the best model for the two demonstrated tasks, ChatGPT, can generally extract correct information and identify when the desired information is missing from an article. We additionally conduct a systematic error analysis, documenting the prevalence of diverse error types encountered during the process of prompt-based information extraction. ",
}
		

			
@inproceedings{kartchner2020regal,
    author    = {Kartchner, David and Ren, Wendi and Nakajima An, Davi and Zhang, Chao and Mitchell, Cassie},
    title     = {ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision},
    year      = {2020},
    maintitle = {Neural Information Processing Systems},
    booktitle = {Human and Model-in-the-Loop Evaluation and Training Stragegies Workshop},
}
		

			
@article{miller2017forward,
    title={Forward thinking: Building deep random forests},
    author={Miller, Kevin and Hettinger, Chris and Humpherys, Jeffrey and Jarvis, Tyler and Kartchner, David},
    journal={arXiv preprint arXiv:1705.07366},
    year={2017}
}