Evidence Inference 2.0: More Data, Better Models

How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles. Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled systematic reviews of medical literature to inform care. NLP may speed this process up, and eventually facilitate immediate consult of published evidence. The Evidence Inference dataset was recently released to facilitate research toward this end. This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence. For instance: Does this article report that chemotherapy performed better than surgery for five-year survival rates of operable cancers? In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25\%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality. We also release an abstract only (as opposed to full-texts) version of the task for rapid model prototyping. The updated corpus, documentation, and code for new baselines and evaluations are available at http://evidence-inference.ebm-nlp.com/.Comment: Accepted as workshop paper into BioNLP Updated results from SciBERT to Biomed RoBERT

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction). We introduce new data for this task, and evaluate models that have recently achieved state-of-the-art results on similar tasks in Natural Language Processing. We then propose a new method motivated by how trial results are typically presented that outperforms these purely data-driven baselines. Finally, we run a fielded evaluation of the model with a non-profit seeking to identify existing drugs that might be re-purposed for cancer, showing the potential utility of end-to-end evidence extraction systems

Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization

We consider the problem of automatically generating a narrative biomedical evidence summary from multiple trial reports. We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews previously conducted by members of the Cochrane collaboration, using the authors conclusions section of the review abstract as our target. We enlist medical professionals to evaluate generated summaries, and we find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual. We propose new approaches that capitalize on domain-specific models to inform summarization, e.g., by explicitly demarcating snippets of inputs that convey key findings, and emphasizing the reports of large and high-quality trials. We find that these strategies modestly improve the factual accuracy of generated summaries. Finally, we propose a new method for automatically evaluating the factuality of generated narrative evidence syntheses using models that infer the directionality of reported findings.Comment: 11 pages, 2 figures. Accepted for presentation at the 2021 AMIA Informatics Summi

Interpreting Neural Networks for and with Natural Language

In the past decade, natural language processing (NLP) systems have come to be built almost exclusively on a backbone of large neural models. As the landscape of feasible tasks has widened due to the capabilities of these models, the space of applications has also widened to include subfields with real-world consequences, such as fact-checking, fake news detection, and medical decision support. The increasing size and nonlinearity of these models results in an opacity that hinders efforts by machine learning practitioners and lay-users alike to understand their internals and derive meaning or trust from their predictions. The fields of explainable artificial intelligence (XAI) and more specifically explainable NLP (ExNLP) have emerged as an active area for remedying this opacity and for ensuring models' reliability and trustworthiness in high-stakes scenarios, by providing textual explanations meaningful to human users. Models that produce justifications for their individual predictions can be inspected for the purposes of debugging, quantifying bias and fairness, understanding model behavior, and ascertaining robustness and privacy. Textual explanation is a predominant form of explanation in machine learning datasets regardless of task modality. As such, this dissertation covers both explaining tasks with natural language and explaining natural language tasks. In this dissertation, I propose test suites for evaluating the quality of model explanations under two definitions of meaning: faithfulness and human acceptability. I use these evaluation methods to investigate the utility of two explanation forms and three model architectures. I finally propose two methods to improve explanation quality– one which increases the likelihood of faithful highlight explanations and one which improves the human acceptability of free-text explanations. This work strives to increase the likelihood of positive use and outcomes when AI systems are deployed in practice.Ph.D

Preclinical risk of bias assessment and PICO extraction using natural language processing

Drug development starts with preclinical studies which test the efficacy and toxicology of potential candidates in living animals, before proceeding to clinical trials examined on human subjects. Many drugs shown to be effective in preclinical animal studies fail in clinical trials, indicating the potential reproducibility issues and translation failure. To obtain less biased research findings, systematic reviews are performed to collate all relevant evidence from publications. However, systematic reviews are time-consuming and researchers have advocated the use of automation techniques to speed the process and reduce human efforts. Good progress has been made in implementing automation tools into reviews for clinical trials while the tools developed for preclinical systematic reviews are scarce. Tools for preclinical systematic reviews should be designed specifically because preclinical experiments differ from clinical trials. In this thesis, I explore natural language processing models for facilitating two stages in preclinical systematic reviews: risk of bias assessment and PICO extraction. There are a range of measures used to reduce bias in animal experiments and many checklist criteria require the reporting of those measures in publications. In the first part of the thesis, I implement several binary classification models to indicate the reporting of random allocation to groups, blinded assessment of outcome, conflict of interests, compliance of animal welfare regulations, and statement of animal exclusions in preclinical publications. I compare traditional machine learning classifiers with several text representation methods, convolutional/recurrent/hierarchical neural networks, and propose two strategies to adapt BERT models to long documents. My findings indicate that neural networks and BERT-based models achieve better performance than traditional classifiers and rule-based approaches. The attention mechanism and hierarchical architecture in neural networks do not improve performance but are useful for extracting relevant words or sentences from publications to inform users’ judgement. The advantages of the transformer structure are hindered when documents are long and computing resources are limited. In literature retrieval and citation screening of published evidence, the key elements of interest are Population, Intervention, Comparator and Outcome, which compose the framework of PICO. In the second part of the thesis, I first apply several question answering models based on attention flows and transformers to extract phrases describing intervention or method of induction of disease models from clinical abstracts and preclinical full texts. For preclinical datasets describing multiple interventions or induction methods in the full texts, I apply additional unsupervised information retrieval methods to extract relevant sentences. The question answering models achieve good performance when the text is at abstract-level and contains only one intervention or induction method, while for truncated documents with multiple PICO mentions, the performance is less satisfactory. Considering this limitation, I then collect preclinical abstracts with finer-grained PICO annotations and develop named entity recognition models for extraction of preclinical PICO elements including Species, Strain, Induction, Intervention, Comparator and Outcome. I decompose PICO extraction into two independent tasks: 1) PICO sentences classification, and 2) PICO elements detection. For PICO extraction, BERT-based models pre-trained from biomedical corpus outperform recurrent networks and the conditional probabilistic module only shows advantages in recurrent networks. Self-training strategy applied to enlarge training set from unlabelled abstracts yields better performance for PICO elements which lack enough amount of instances. Experimental results demonstrate the possibilities of facilitating preclinical risk of bias assessment and PICO extraction by natural language processing