Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape

Discriminative and Distinct Phenotyping by Constrained Tensor Factorization

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Conditional Hierarchical Bayesian Tucker Decomposition

Our research focuses on studying and developing methods for reducing the dimensionality of large datasets, common in biomedical applications. A major problem when learning information about patients based on genetic sequencing data is that there are often more feature variables (genetic data) than observations (patients). This makes direct supervised learning difficult. One way of reducing the feature space is to use latent Dirichlet allocation in order to group genetic variants in an unsupervised manner. Latent Dirichlet allocation is a common model in natural language processing, which describes a document as a mixture of topics, each with a probability of generating certain words. This can be generalized as a Bayesian tensor decomposition to account for multiple feature variables. While we made some progress improving and modifying these methods, our significant contributions are with hierarchical topic modeling. We developed distinct methods of incorporating hierarchical topic modeling, based on nested Chinese restaurant processes and Pachinko Allocation Machine, into Bayesian tensor decompositions. We apply these models to predict whether or not patients have autism spectrum disorder based on genetic sequencing data. We examine a dataset from National Database for Autism Research consisting of paired siblings -- one with autism, and the other without -- and counts of their genetic variants. Additionally, we linked the genes with their Reactome biological pathways. We combine this information into a tensor of patients, counts of their genetic variants, and the membership of these genes in pathways. Once we decompose this tensor, we use logistic regression on the reduced features in order to predict if patients have autism. We also perform a similar analysis of a dataset of patients with one of four common types of cancer (breast, lung, prostate, and colorectal).Comment: 20 pages, added model evaluation and log-likelihood section

Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries

Includes details on the implementation of MetaMap and IntraMap, prioritization rules, the test set of clinical trials and the classification of the external test set according to the 171 GBD categories. Dataset S1: Expert-based enrichment database for the classification according to the 28 GBD categories. Manual classification of 503 UMLS concepts that could not be mapped to any of the 28 GBD categories. Dataset S2: Expert-based enrichment database for the classification according to the 171 GBD categories. Manual classification of 655 UMLS concepts that could not be mapped to any of the 171 GBD categories, among which 108 could be projected to candidate GBD categories. Table S1: Excluded residual GBD categories for the grouping of the GBD cause list in 171 GBD categories. A grouping of 193 GBD categories was defined during the GBD 2010 study to inform policy makers about the main health problems per country. From these 193 GBD categories, we excluded the 22 residual categories listed in the Table. We developed a classifier for the remaining 171 GBD categories. Among these residual categories, the unique excluded categories in the grouping of 28 GBD categories were “Other infectious diseases” and “Other endocrine, nutritional, blood, and immune disorders”. Table S2: Per-category evaluation of performance of the classifier for the 171 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities, specificities (in %) and likelihood ratios for each of the 171 GBD categories plus the “No GBD” category for the classifier using the Word Sense Disambiguation server, the expert-based enrichment database and the priority to the health condition field. Table S3: Performance of the 8 versions of the classifier for the 171 GBD categories. Exact-matching and weighted averaged sensitivities and specificities for 8 versions of the classifier for the 171 GBD categories. Exact-matching corresponds to the proportion (in %) of trials for which the automatic GBD classification is correct. Exact-matching was estimated over all trials (N = 2,763), trials concerning a unique GBD category (N = 2,092), trials concerning 2 or more GBD categories (N = 187), and trials not relevant for the GBD (N = 484). The weighted averaged sensitivity and specificity corresponds to the weighted average across GBD categories of the sensitivities and specificities for each GBD category plus the “No GBD” category (in %). The 8 versions correspond to the combinations of the use or not of the Word Sense Disambiguation server during the text annotation, the expert-based enrichment database, and the priority to the health condition field as a prioritization rule. Table S4: Per-category evaluation of the performance of the baseline for the 28 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities and specificities (in %) of the 28 GBD categories plus the “No GBD” category for the classification of clinical trial records towards GBD categories without using the UMLS knowledge source but based on the recognition in free text of the names of diseases defining in each GBD category only. For the baseline a clinical trial records was classified with a GBD category if at least one of the 291 disease names from the GBD cause list defining that GBD category appeared verbatim in the condition field, the public or scientific titles, separately, or in at least one of these three text fields. (DOCX 84 kb