From 3c411598d34b019a251a14a36145dcb8d945980e Mon Sep 17 00:00:00 2001 From: David Date: Tue, 25 Apr 2017 10:44:34 +0000 Subject: [PATCH] Added sections on embeddings for medical ontologies and causal inference (#339) This build is based on https://github.com/greenelab/deep-review/commit/22c54f0e6e1d7530c34b1a5000cff8abb1365d13. This commit was created by the following Travis CI build and job: https://travis-ci.org/greenelab/deep-review/builds/225555590 https://travis-ci.org/greenelab/deep-review/jobs/225555591 [ci skip] The full commit message that triggered this build is copied below: Added sections on embeddings for medical ontologies and causal inference (#339) * Add reference to Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests in Electronic Helath Records data set. * Added in the anchor and learn framework. This isn't strictly deep learning, so I don't know if it should be included, but it is relevant ot the section. * Added in references to neural embeddings in medical coding * Added causal inference references * Changed pmid to doi * Changed PMC id to regular PubMed id. --- all-sections.md | 37 +- bibliography.bib | 48 +++ bibliography.json | 794 ++++++++++++++++++++++++++++++++++++++++ citations.json | 788 +++++++++++++++++++++++++++++++++++++++ processed-citations.tsv | 8 + 5 files changed, 1671 insertions(+), 4 deletions(-) diff --git a/all-sections.md b/all-sections.md index c1a2df78..d9d8fcb0 100644 --- a/all-sections.md +++ b/all-sections.md @@ -316,6 +316,22 @@ This indicates a potential strength of deep methods. It may be possible to repurpose features from task to task, improving overall predictions as the field tackles new challenges. +Several authors have created reusable feature sets for medical terminologies using +neural embeddings, as popularized by word2Vec [@1GhHIDxuW]. This approach +was first used on free text medical notes by De Vine et al. +[@XQtuRkTU] with results at or better than traditional methods. +Y. Choi et al.[@1qa47hoP] built embeddings of standardized +terminologies, such as ICD and NDC, used in widely available administrative +claims data. By learning terminologies for different entities in the same +vector space, they can potentially find relationships between different +domains (e.g. drugs and the diseases they treat). Medical claims data does not +have the natural document structure of clinical notes, and this issue was +addressed by E. Choi et al. [@TwvauiTv], who built +embeddings using a multi-layer network architecture which mimics the structure +of claims data. While promising, difficulties in evaluating the quality of +these kinds of features and variations in clinical coding practices remain as +challenges to using them in practice. + Identifying consistent subgroups of individuals and individual health trajectories from clinical tests is also an active area of research. Approaches inspired by deep learning have been used for both unsupervised feature @@ -331,9 +347,10 @@ scale analysis of an electronic health records system found that a deep denoising autoencoder architecture applied to the number and co-occurrence of clinical test events, though not the results of those tests, constructed features that were more useful for disease prediction than other existing -feature construction methods [@WrNCJ9sO]. Taken together, these -results support the potential of unsupervised feature construction in this -domain. However, numerous challenges including data integration (patient +feature construction methods [@WrNCJ9sO]. Razavian et al. +[@c6MfDdWP] used a set of 18 common lab tests to predict disease onset +using both CNN and LSTM architectures and demonstrated and improvement over baseline +regression models. However, numerous challenges including data integration (patient demographics, family history, laboratory tests, text-based patient records, image analysis, genomic data) and better handling of streaming temporal data with many features, will need to be overcome before we can fully assess the @@ -410,7 +427,9 @@ making methodological choices that either reduce the need for labeled examples or that use transformations to training data to increase the number of times it can be used before overfitting occurs. For example, the unsupervised and semi-supervised methods that we've discussed reduce the need for labeled -examples [@5x3uMSKi]. The adversarial training example +examples [@5x3uMSKi]. The anchor and learn framework +[@A9JeoGV8] uses expert knowledge to identify high confidence +observations from which labels can be inferred. The adversarial training example strategies that we've mentioned can reduce overfitting, if transformations are available that preserve the meaningful content of the data while transforming irrelevant features [@Xxb4t3zO]. While adversarial training examples @@ -1262,6 +1281,16 @@ interpretability of deep learning models, fitting deep models to limited and heterogeneous data, and integrating complex predictive models into a dynamic clinical environment. +A critical challenge in moving from prediction to treatment recommendations +is the necessity to establish a causal relationship for a recommendation. +Causal inference is often framed in terms of counterfactual question +[@cpNVdlL7]. Johansson et al [@173ftiSzF] use deep neural +networks to create representation models for covariates that capture nonlinear +effects and show significant performance improvements over existing models. In +a less formal approach, Kale et al [@FUIfIdE] first create a deep neural +network to model clinical time series and then analyze the relationship of the +hidden features to the output using a causal approach. + #### Applications ##### Trajectory Prediction for Treatment diff --git a/bibliography.bib b/bibliography.bib index 2a32b6e2..6069477a 100644 --- a/bibliography.bib +++ b/bibliography.bib @@ -1690,3 +1690,51 @@ @article{xl1ijigK year = {2017} } + +@article{173ftiSzF, + abstract = {Observational studies are rising in importance due to the widespread +accumulation of data in fields such as healthcare, education, employment and +ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different +medication?". We propose a new algorithmic framework for counterfactual +inference which brings together ideas from domain adaptation and representation +learning. In addition to a theoretical justification, we perform an empirical +comparison with previous approaches to causal inference from observational +data. Our deep learning algorithm significantly outperforms the previous +state-of-the-art.}, + archiveprefix = {arXiv}, + author = {Fredrik D. Johansson and Uri Shalit and David Sontag}, + eprint = {1605.03661v2}, + file = {1605.03661v2.pdf}, + link = {http://arxiv.org/abs/1605.03661v2}, + month = {May}, + primaryclass = {stat.ML}, + title = {Learning Representations for Counterfactual Inference}, + year = {2016} +} + + +@article{c6MfDdWP, + abstract = {Disparate areas of machine learning have benefited from models that can take +raw data with little preprocessing as input and learn rich representations of +that raw data in order to perform well on a given prediction task. We evaluate +this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in +clinical data, to predict disease onsets. In particular, we train a Long +Short-Term Memory (LSTM) recurrent neural network and two novel convolutional +neural networks for multi-task prediction of disease onset for 133 conditions +based on 18 common lab tests measured over time in a cohort of 298K patients +derived from 8 years of administrative claims data. We compare the neural +networks to a logistic regression with several hand-engineered, clinically +relevant features. We find that the representation-based learning approaches +significantly outperform this baseline. We believe that our work suggests a new +avenue for patient risk stratification based solely on lab results.}, + archiveprefix = {arXiv}, + author = {Narges Razavian and Jake Marcus and David Sontag}, + eprint = {1608.00647v3}, + file = {1608.00647v3.pdf}, + link = {http://arxiv.org/abs/1608.00647v3}, + month = {Aug}, + primaryclass = {cs.LG}, + title = {Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests}, + year = {2016} +} + diff --git a/bibliography.json b/bibliography.json index 85ad31c8..a63109c9 100644 --- a/bibliography.json +++ b/bibliography.json @@ -25550,6 +25550,744 @@ ], "id": "ppGS5h4v" }, + { + "indexed": { + "date-parts": [ + [ + 2017, + 4, + 2 + ] + ], + "date-time": "2017-04-02T04:48:24Z", + "timestamp": 1491108504137 + }, + "reference-count": 0, + "publisher": "American Psychological Association (APA)", + "issue": "5", + "content-domain": { + "domain": [], + "crossmark-restriction": false + }, + "short-container-title": [ + "Journal of Educational 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Discovery via Deep Networks", + "author": [ + { + "family": "Kale", + "given": "David C." + }, + { + "family": "Che", + "given": "Zhengping" + }, + { + "family": "Bahadori", + "given": "Mohammad Taha" + }, + { + "family": "Li", + "given": "Wenzhe" + }, + { + "family": "Liu", + "given": "Yan" + }, + { + "family": "Wetzel", + "given": "Randall" + } + ], + "container-title-short": "AMIA Annu Symp Proc", + "container-title": "AMIA Annual Symposium Proceedings", + "publisher": "American Medical Informatics Association", + "issued": { + "date-parts": [ + [ + 2015 + ] + ] + }, + "page": "677-686", + "volume": "2015", + "PMID": "26958203", + "PMCID": "PMC4765623", + "type": "article-journal" + }, { "abstract": "After a more than decade-long period of relatively little research activity in the area of recurrent neural networks, several new developments will be reviewed here that have allowed substantial progress both in understanding and in technical solutions towards more efficient training of recurrent networks. These advances have been motivated by and related to the optimization issues surrounding deep learning. Although recurrent networks are extremely powerful in what they can in principle represent in terms of modelling sequences,their training is plagued by two aspects of the same issue regarding the learning of long-term dependencies. Experiments reported here evaluate the use of clipping gradients, spanning longer time ranges with leaky integration, advanced momentum techniques, using more powerful output probability models, and encouraging sparser gradients to help symmetry breaking and credit assignment. The experiments are performed on text and music data and show off the combined effects of these techniques in generally improving both training and test error.", "author": [ @@ -27624,5 +28362,61 @@ }, "title": "Generating multi-label discrete electronic health records using generative adversarial networks", "type": "article-journal" + }, + { + "abstract": "Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, “Would this patient have lower blood sugar had she received a different medication?”. We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.", + "author": [ + { + "family": "Johansson", + "given": "Fredrik D." + }, + { + "family": "Shalit", + "given": "Uri" + }, + { + "family": "Sontag", + "given": "David" + } + ], + "id": "173ftiSzF", + "issued": { + "date-parts": [ + [ + 2016, + 5 + ] + ] + }, + "title": "Learning representations for counterfactual inference", + "type": "article-journal" + }, + { + "abstract": "Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient’s health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for 133 conditions based on 18 common lab tests measured over time in a cohort of 298K patients derived from 8 years of administrative claims data. We compare the neural networks to a logistic regression with several hand-engineered, clinically relevant features. We find that the representation-based learning approaches significantly outperform this baseline. We believe that our work suggests a new avenue for patient risk stratification based solely on lab results.", + "author": [ + { + "family": "Razavian", + "given": "Narges" + }, + { + "family": "Marcus", + "given": "Jake" + }, + { + "family": "Sontag", + "given": "David" + } + ], + "id": "c6MfDdWP", + "issued": { + "date-parts": [ + [ + 2016, + 8 + ] + ] + }, + "title": "Multi-task prediction of disease onsets from longitudinal lab tests", + "type": "article-journal" } ] \ No newline at end of file diff --git a/citations.json b/citations.json index 20d49e31..5fb39c57 100644 --- a/citations.json +++ b/citations.json @@ -27272,5 +27272,793 @@ "standard_citation": "arxiv:1703.06490v1", "bibtex": "@article{xl1ijigK,\n abstract = {Access to electronic health records (EHR) data has motivated computational\nadvances in medical research. However, various concerns, particularly over\nprivacy, can limit access to and collaborative use of EHR data. Sharing\nsynthetic EHR data could mitigate risk. In this paper, we propose a new\napproach, medical Generative Adversarial Network (medGAN), to generate\nrealistic synthetic EHRs. Based on an input EHR dataset, medGAN can generate\nhigh-dimensional discrete variables (e.g., binary and count features) via a\ncombination of an autoencoder and generative adversarial networks. We also\npropose minibatch averaging to efficiently avoid mode collapse, and increase\nthe learning efficiency with batch normalization and shortcut connections. To\ndemonstrate feasibility, we showed that medGAN generates synthetic EHR datasets\nthat achieve comparable performance to real data on many experiments including\ndistribution statistics, predictive modeling tasks and medical expert review.},\n archiveprefix = {arXiv},\n author = {Edward Choi and Siddharth Biswal and Bradley Malin and Jon Duke and Walter F. Stewart and Jimeng Sun},\n eprint = {1703.06490v1},\n file = {1703.06490v1.pdf},\n link = {http://arxiv.org/abs/1703.06490v1},\n month = {Mar},\n primaryclass = {cs.LG},\n title = {Generating Multi-label Discrete Electronic Health Records using\nGenerative Adversarial Networks},\n year = {2017}\n}\n\n", "citation_id": "xl1ijigK" + }, + "arxiv:1605.03661": { + "source": "arxiv", + "identifer": "1605.03661", + "standard_citation": "arxiv:1605.03661", + "bibtex": "@article{173ftiSzF,\n abstract = {Observational studies are rising in importance due to the widespread\naccumulation of data in fields such as healthcare, education, employment and\necology. We consider the task of answering counterfactual questions such as, \"Would this patient have lower blood sugar had she received a different\nmedication?\". We propose a new algorithmic framework for counterfactual\ninference which brings together ideas from domain adaptation and representation\nlearning. 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We evaluate\nthis approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in\nclinical data, to predict disease onsets. In particular, we train a Long\nShort-Term Memory (LSTM) recurrent neural network and two novel convolutional\nneural networks for multi-task prediction of disease onset for 133 conditions\nbased on 18 common lab tests measured over time in a cohort of 298K patients\nderived from 8 years of administrative claims data. We compare the neural\nnetworks to a logistic regression with several hand-engineered, clinically\nrelevant features. We find that the representation-based learning approaches\nsignificantly outperform this baseline. 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