Medical information extraction with deep neural networks
AbstractBackground. Electronic Health Records (EHRs) are the databases used by hospital and general practitioners to daily log all the information they record from patients (i.e. disorders, taken medications, symptoms, medical tests…). In number of subjects (e.g. 50 million patients in the case of EMIF http://www.emif.eu/), EHRs are the largest source of empirical data in biomedical research, allowing for major scientific findings in central disorders such as cancer and Alzheimer’s disease . However, most of the information held in EHRs is in the form of natural language text (written by the physician during each session with each patient), making it inaccessible for research. Unlocking all this information would bring a very significant advancement to biomedical research, multiplying the quantity and variety of scientifically usable data, which is the reason why major efforts have been relatively recently initiated towards this aim (e.g. I2B2 challenges https://www.i2b2.org/NLP/ or the UK-CRIS network of EHRs https://crisnetwork.co/uk-cris-programme)
Project. Recent Deep Neural Networks (DNN) architectures have shown remarkable results in traditionally unsolved NLP problems, including some IE tasks such as Slot Filling  and Relation Classification . When transferring this success to EHRs, DNNs offer the advantage of not requiring well formatted text, while the problem remains of labelled data being scarce (ranging on the hundreds for EHRs, rather than the tens of thousands used in typical DNN studies). However, ongoing work in our lab has shown that certain extensions of recent NLP-DNN architectures can reproduce the typical remarkable success of DNNs in situations with limited labelled data (paper in preparation). Namely, incorporating interaction terms to feed forwards DNN architectures  can rise the performance of relation classification in I2B2 datasets from 0.65 F1 score to 0.90, while the highest performance previously reported with the same dataset was 0.74. With an F1 score of 0.90, the quality of the extracted information meets the standards required for such information to be used in subsequent biomedical studies, promising to unlock the scientific data that at present is hidden in the free text of EHRs
We therefore propose to apply DNNs to the problem of information extraction in EHRs, using I2B2 and UK-CRIS data as a testbed. More specifically, the DNNs designed and implemented by the student should be able to extract medically relevant information, such as prescribed drugs or diagnoses given to patients. This corresponds to some of the challenges proposed by I2B2 during recent years (https://www.i2b2.org/NLP/Medication/), and are objectives of high interest in UK-CRIS which have sometimes been addressed with older techniques such as rules [1,6,7]. The student is free to use the extension of the feed forward DNN developed in our lab, or to explore other feed forwards or recurrent (e.g. RNN, LSTM or GRU) alternatives. . The DNN should be implemented in Python’s Keras (https://keras.io/), Theano (http://deeplearning.net/software/theano/), Tensorflow (https://www.tensorflow.org/), or PyTorch (http://pytorch.org/).
Bibliography: G. Perera, M. Khondoker, M. Broadbent, G. Breen, R. Stewart, Factors Associated with Response to Acetylcholinesterase Inhibition in Dementia: A Cohort Study from a Secondary Mental Health Care Case Register in London, PLOS ONE. 9 (2014) e109484. doi:10.1371/journal.pone.0109484. Y. LeCun, Y. Bengio, G. Hinton, DL - Deep learning, Nature. 521 (2015) 436–444. doi:10.1038/nature14539. G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, D. Hakkani-Tur, X. He, L. Heck, G. Tur, D. Yu, G. Zweig, Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding, IEEEACM Trans. Audio Speech Lang. Process. 23 (2015) 530–539. doi:10.1109/TASLP.2014.2383614. C.N. dos Santos, B. Xiang, B. Zhou, Classifying Relations by Ranking with Convolutional Neural Networks, CoRR. abs/1504.06580 (2015). http://arxiv.org/abs/1504.06580. M. Denil, A. Demiraj, N. Kalchbrenner, P. Blunsom, N. de Freitas, Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network, CoRR. abs/1406.3830 (2014). http://arxiv.org/abs/1406.3830. E. Iqbal, R. Mallah, R.G. Jackson, M. Ball, Z.M. Ibrahim, M. Broadbent, O. Dzahini, R. Stewart, C. Johnston, R.J.B. Dobson, Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register, PLOS ONE. 10 (2015) e0134208. doi:10.1371/journal.pone.0134208. R.G. Jackson MSc, M. Ball, R. Patel, R.D. Hayes, R.J. Dobson, R. Stewart, TextHunter – A User Friendly Tool for Extracting Generic Concepts from Free Text in Clinical Research, AMIA. Annu. Symp. Proc. 2014 (2014) 729–738.