Table of Links
2 Data
3 Methods
3.1 Lexicon Creation and Expansion
4 Results
4.1 Demographics and 4.2 System Performance
6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References
SUPPLEMENTARY
Guidelines for Annotating Social Support and Social Isolation in Clinical Notes
OTHER SUPERVISED MODELS
We performed a sentence-level classification using SVM and BERT. The entity-level annotation were converted to sentence-level.
SVM: Initially, we used two VSM methods namely term frequency-inverse document frequency (TF-IDF) and word2vec [1] to convert the clinical notes into vectors. However, the word2vecbased system performed better than the TF-IDF-based system and we reported the performance of word2vec-based system next. We used the same embeddings that we used to identify similar words. We used Eq 1 to convert the word vectors to a single vector. The embeddings settings are as follows: vector size: 300, minimum word count: 3, and window size: 10. We used the linear kernel in the SVM classifier.
References
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Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 2013.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805,
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Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020.
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Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
Table S10: Erroneous examples from Rule-based NLP systems (RBS) and LLM-based NLP systems.
This paper is available on arxiv under CC BY 4.0 DEED license.
Authors:
(1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.
