Title |
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives
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Published in |
Scientific Reports, July 2018
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DOI | 10.1038/s41598-018-27946-5 |
Pubmed ID | |
Authors |
Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Sonya Aggarwal, Daniel T. Chang, Daniel L. Rubin |
Abstract |
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. In a single framework, we integrated semantic data mapping and neural embedding technique to produce a text processing method that extracts relevant information from heterogeneous types of clinical notes in an unsupervised manner, and we designed a recurrent neural network to model the temporal dependency of the patient visits. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset (1818 patients). Our method achieved an area under the ROC curve (AUC) of 0.89. To provide explain-ability, we developed an interactive graphical tool that may improve physician understanding of the basis for the model's predictions. The high accuracy and explain-ability of the PPES-Met model may enable our model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 4 | 50% |
Netherlands | 1 | 13% |
Unknown | 3 | 38% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 50% |
Practitioners (doctors, other healthcare professionals) | 3 | 38% |
Scientists | 1 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 78 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 17 | 22% |
Student > Master | 8 | 10% |
Student > Ph. D. Student | 7 | 9% |
Student > Bachelor | 6 | 8% |
Student > Doctoral Student | 5 | 6% |
Other | 9 | 12% |
Unknown | 26 | 33% |
Readers by discipline | Count | As % |
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Computer Science | 16 | 21% |
Medicine and Dentistry | 15 | 19% |
Mathematics | 4 | 5% |
Engineering | 4 | 5% |
Biochemistry, Genetics and Molecular Biology | 3 | 4% |
Other | 10 | 13% |
Unknown | 26 | 33% |