Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. .

We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.

OBJECTIVE: This work investigates the problem of clinical coreference resolution in a model that explicitly tracks entities, and aims to measure the performance of that model in both traditional in-domain train/test splits and cross-domain experiments that measure the generalizability of learned models. METHODS: The two methods we compare are a baseline mention-pair coreference system that operates over pairs of mentions with best-first conflict resolution and a mention-synchronous system that incrementally builds coreference chains. We develop new features that incorporate distributional semantics, discourse features, and entity attributes. We use two new coreference datasets with similar annotation guidelines - the THYME colon cancer dataset and the DeepPhe breast cancer dataset. RESULTS: The mention-synchronous system performs similarly on in-domain data but performs much better on new data. Part of speech tag features prove superior in feature generalizability experiments over other word representations. Our methods show generalization improvement but there is still a performance gap when testing in new domains. DISCUSSION: Generalizability of clinical NLP systems is important and under-studied, so future work should attempt to perform cross-domain and cross-institution evaluations and explicitly develop features and training regimens that favor generalizability. A performance-optimized version of the mention-synchronous system will be included in the open source Apache cTAKES software.

Medical reports include many occurrences of relevant events in the form of free-text. To make data easily accessible and improve medical decisions, clinical information extraction is crucial. Traditional extraction methods usually rely on the availability of external resources, or require complex annotated corpora and elaborate designed features. Especially for languages other than English, progress has been limited by scarce availability of tools and resources. In this work, we explore recurrent neural network (RNN) architectures for clinical event extraction from Italian medical reports. The proposed model includes an embedding layer and an RNN layer. To find the best configuration for event extraction, we explored different RNN architectures, including Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). We also tried feeding morpho-syntactic information into the network. The best result was obtained by using the GRU network with additional morpho-syntactic inputs.

Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.

Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.

We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their "reviews" are spaced over time. Our algorithm uses only 34-50% of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available at scholar.harvard.edu/hadi/RbF/.

OBJECTIVE: To develop an open-source temporal relation discovery system for the clinical domain. The system is capable of automatically inferring temporal relations between events and time expressions using a multilayered modeling strategy. It can operate at different levels of granularity--from rough temporality expressed as event relations to the document creation time (DCT) to temporal containment to fine-grained classic Allen-style relations. MATERIALS AND METHODS: We evaluated our systems on 2 clinical corpora. One is a subset of the Temporal Histories of Your Medical Events (THYME) corpus, which was used in SemEval 2015 Task 6: Clinical TempEval. The other is the 2012 Informatics for Integrating Biology and the Bedside (i2b2) challenge corpus. We designed multiple supervised machine learning models to compute the DCT relation and within-sentence temporal relations. For the i2b2 data, we also developed models and rule-based methods to recognize cross-sentence temporal relations. We used the official evaluation scripts of both challenges to make our results comparable with results of other participating systems. In addition, we conducted a feature ablation study to find out the contribution of various features to the system's performance. RESULTS: Our system achieved state-of-the-art performance on the Clinical TempEval corpus and was on par with the best systems on the i2b2 2012 corpus. Particularly, on the Clinical TempEval corpus, our system established a new F1 score benchmark, statistically significant as compared to the baseline and the best participating system. CONCLUSION: Presented here is the first open-source clinical temporal relation discovery system. It was built using a multilayered temporal modeling strategy and achieved top performance in 2 major shared tasks.