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Zachary Lipton, C. Elkan, Balakrishnan Narayanaswamy (2014)
Optimal Thresholding of Classifiers to Maximize F1 MeasureMachine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD, 8725
G. Savova, Jin Fan, Zi Ye, Sean Murphy, Jiaping Zheng, C. Chute, I. Kullo (2010)
Discovering peripheral arterial disease cases from radiology notes using natural language processing.AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010
G. Savova, James Masanz, P. Ogren, Jiaping Zheng, S. Sohn, K. Schuler, C. Chute (2010)
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applicationsJournal of the American Medical Informatics Association : JAMIA, 17 5
John Qiu, Hong-Jun Yoon, P. Fearn, G. Tourassi (2018)
Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology ReportsIEEE Journal of Biomedical and Health Informatics, 22
S. Cox, Ashley Lane, S. Volchenboum (2018)
Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials.JCO clinical cancer informatics, 2
Chollet F
Keras documentation
Martín Abadi, Ashish Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. Corrado, Andy Davis, J. Dean, Matthieu Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Yangqing Jia, R. Józefowicz, Lukasz Kaiser, M. Kudlur, J. Levenberg, Dandelion Mané, R. Monga, Sherry Moore, D. Murray, C. Olah, M. Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, P. Tucker, Vincent Vanhoucke, Vijay Vasudevan, F. Viégas, Oriol Vinyals, P. Warden, M. Wattenberg, M. Wicke, Yuan Yu, Xiaoqiang Zheng (2016)
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed SystemsArXiv, abs/1603.04467
P. Harris, Robert Taylor, R. Thielke, Jonathon Payne, N. Gonzalez, J. Conde (2009)
Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics supportJournal of biomedical informatics, 42 2
S. Castro, Eugene Tseytlin, Olga Medvedeva, K. Mitchell, S. Visweswaran, Tanja Bekhuis, Rebecca Jacobson (2017)
Automated annotation and classification of BI-RADS assessment from radiology reportsJournal of biomedical informatics, 69
Po-Hao Chen, H. Zafar, M. Galperin-Aizenberg, T. Cook (2018)
Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology ReportsJournal of Digital Imaging, 31
M. Alawad, Hong-Jun Yoon, G. Tourassi (2018)
Coarse-to-fine multi-task training of convolutional neural networks for automated information extraction from cancer pathology reports2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Sohrab Saeb, L. Lonini, A. Jayaraman, D. Mohr, Konrad Kording (2017)
The need to approximate the use-case in clinical machine learningGigaScience, 6
GitHub
prissmm_notes_nlp
Christopher Grob (2018)
Real-world applicationInventory Management in Multi-Echelon Networks
L. Sholl, K. Do, Priyanka Shivdasani, E. Cerami, A. Dubuc, F. Kuo, E. Garcia, Yonghui Jia, Phani Davineni, R. Abo, Trevor Pugh, P. Hummelen, A. Thorner, M. Ducar, A. Berger, M. Nishino, K. Janeway, A. Church, M. Harris, Lauren Ritterhouse, Joshua Campbell, V. Rojas-Rudilla, A. Ligon, S. Ramkissoon, J. Cleary, U. Matulonis, G. Oxnard, R. Chao, V. Tassell, J. Christensen, W. Hahn, P. Kantoff, D. Kwiatkowski, B. Johnson, M. Meyerson, L. Garraway, G. Shapiro, B. Rollins, N. Lindeman, L. Macconaill (2016)
Institutional implementation of clinical tumor profiling on an unselected cancer population.JCI insight, 1 19
M. Zozus, C. Pieper, Constance Johnson, T. Johnson, A. Franklin, Jack Smith, Jiajie Zhang (2015)
Factors Affecting Accuracy of Data Abstracted from Medical RecordsPLoS ONE, 10
Food and Drug Administration: Submitting documents using real-world data and real-world evidence to FDA for drugs and biologics guidance for industry
K. Kehl, C. Lathan, B. Johnson, D. Schrag (2018)
Race, Poverty, and Initial Implementation of Precision Medicine for Lung Cancer.Journal of the National Cancer Institute, 111 4
Schrag D
GENIE: Real-world application
E. Basch, A. Deal, M. Kris, H. Scher, C. Hudis, P. Sabbatini, L. Rogak, A. Bennett, A. Dueck, T. Atkinson, J. Chou, Dorothy Dulko, L. Sit, A. Barz, P. Novotny, M. Fruscione, J. Sloan, D. Schrag (2016)
Symptom Monitoring With Patient-Reported Outcomes During Routine Cancer Treatment: A Randomized Controlled Trial.Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 34 6
Shang Gao, M. Young, John Qiu, Hong-Jun Yoon, J. Christian, P. Fearn, G. Tourassi, Arvind Ramanthan (2017)
Hierarchical attention networks for information extraction from cancer pathology reportsJournal of the American Medical Informatics Association : JAMIA, 25
K. Kehl, Haitham Elmarakeby, M. Nishino, E. Allen, E. Lepisto, M. Hassett, B. Johnson, D. Schrag (2019)
Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports.JAMA oncology
T. Therneau, P. Grambsch (2000)
Modeling Survival Data: Extending the Cox Model
G. Savova, I. Danciu, Folami Alamudun, Timothy Miller, Chen Lin, D. Bitterman, G. Tourassi, J. Warner (2019)
Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.Cancer research
Rachel Sherman, S. Anderson, G. Pan, Gerry Gray, T. Gross, Nina Hunter, L. LaVange, D. Marinac-Dabic, P. Marks, Melissa Robb, J. Shuren, R. Temple, J. Woodcock, L. Yue*, R. Califf (2016)
Real-World Evidence - What Is It and What Can It Tell Us?The New England journal of medicine, 375 23
D. Carrell, Scott Halgrim, Diem-Thy Tran, D. Buist, Jessica Chubak, W. Chapman, G. Savova (2014)
Using natural language processing to improve efficiency of manual chart abstraction in research: the case of breast cancer recurrence.American journal of epidemiology, 179 6
The Medicine (2018)
Opening the black box of machine learning.The Lancet. Respiratory medicine, 6 11
John Orechia, Ameet Pathak, Yunling Shi, Aniket Nawani, A. Belozerov, Caitlin Fontes, Camille Lakhiani, C. Jawale, C. Patel, Daniel Quinn, Dmitry Botvinnik, Eddie Mei, Elizabeth Cotter, James Byleckie, Mollie Ullman-Cullere, P. Chhetri, Poornima Chalasani, P. Karnam, Ronald Beaudoin, S. Sahu, Yelena Belozerova, J. Mathew (2015)
OncDRS: An integrative clinical and genomic data platform for enabling translational research and precision medicineApplied & Translational Genomics, 6
M. Curtis, S. Griffith, Melisa Tucker, Michael Taylor, W. Capra, G. Carrigan, B. Holzman, Aracelis Torres, P. You, B. Arnieri, A. Abernethy (2018)
Development and Validation of a High‐Quality Composite Real‐World Mortality EndpointHealth Services Research, 53
Calders T, Esposito F, Hullermeier E (eds)
Optimal thresholding of classifiers to maximize F1 measure, in Machine Learning and Knowledge Discovery in Databases
E. Basch, D. Schrag (2019)
The Evolving Uses of "Real-World" Data.JAMA, 321 14
Submitting documents using real - world data and real - world evidence to FDA for drugs and biologics guidance for industry
PURPOSE: Cancer research using electronic health records and genomic data sets requires clinical outcomes data, which may be recorded only in unstructured text by treating oncologists. Natural language processing (NLP) could substantially accelerate extraction of this information. METHODS: Patients with lung cancer who had tumor sequencing as part of a single-institution precision oncology study from 2013 to 2018 were identified. Medical oncologists' progress notes for these patients were reviewed. For each note, curators recorded whether the assessment/plan indicated any cancer, progression/worsening of disease, and/or response to therapy or improving disease. Next, a recurrent neural network was trained using unlabeled notes to extract the assessment/plan from each note. Finally, convolutional neural networks were trained on labeled assessments/plans to predict the probability that each curated outcome was present. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) among a held-out test set of 10% of patients. Associations between curated response or progression end points and overall survival were measured using Cox models among patients receiving palliative-intent systemic therapy. RESULTS: Medical oncologist notes (n = 7,597) were manually curated for 919 patients. In the 10% test set, NLP models replicated human curation with AUROCs of 0.94 for the any-cancer outcome, 0.86 for the progression outcome, and 0.90 for the response outcome. Progression/worsening events identified using NLP models were associated with shortened survival (hazard ratio [HR] for mortality, 2.49; 95% CI, 2.00 to 3.09); response/improvement events were associated with improved survival (HR, 0.45; 95% CI, 0.30 to 0.67). CONCLUSION: NLP models based on neural networks can extract meaningful outcomes from oncologist notes at scale. Such models may facilitate identification of clinical and genomic features associated with response to cancer treatment.
JCO Clinical Cancer Informatics – Wolters Kluwer Health
Published: Aug 5, 2020
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