Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

BMC Medical Informatics and Decision Making
Merijn BeeksmaStef Groenewoud

Abstract

Life expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients. Physicians, however, tend to overestimate life expectancy, and miss the window of opportunity to initiate Advance Care Planning. This research tests the potential of using machine learning and natural language processing techniques for predicting life expectancy from electronic medical records. We approached the task of predicting life expectancy as a supervised machine learning task. We trained and tested a long short-term memory recurrent neural network on the medical records of deceased patients. We developed the model with a ten-fold cross-validation procedure, and evaluated its performance on a held-out set of test data. We compared the performance of a model which does not use text features (baseline model) to the performance of a model which...Continue Reading

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Citations

Jul 30, 2020·International Journal of Environmental Research and Public Health·Israel Edem AgbehadjiRichard C Millham
Jan 1, 2021·BMC Medical Informatics and Decision Making·Sundreen Asad KamalPing Zhang
Jul 15, 2021·BMJ Supportive & Palliative Care·Ludovica De PanfilisCarlo Botrugno
Aug 24, 2021·Arabian Journal for Science and Engineering·Kareen TeoKhin Wee Lai

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Software Mentioned

FaMe
Tensorflow
net
SPICT
word2vec
RADPAC
DeepCare

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