Finding Understudied Disorders Potentially Associated with Maternal Morbidity and Mortality.

Objective Clinical research literature focuses primarily on the most common causes of maternal morbidity and mortality (MMM). We explore sections of the discharge summaries of pregnant or postpartum women admitted to an intensive care unit (ICU) to identify associated disorders and mine the literature to identify knowledge gaps in clinical research. Methods Data for the study were discharge summaries in the MIMIC (Medical Information Mart for Intensive Care) database. We extracted a control cohort to study if there is a difference in comorbidities between pregnant and not pregnant patients with similar reasons for admission. We identified comorbidities of the Unified Medical Language System (UMLS) semantic types disease or syndrome, Mental or behavioral dysfunction, and injury, or poisoning. We used Entrez programming utilities (E-utilities) to query PubMed ® . Results We identified 246 pregnant and postpartum patients. A control group of 587 not pregnancy related admissions matched on age and admit diagnosis. We found overlap of 24.3% discharge diagnoses between the two groups, and 7.5% of the codes exclusively in the pregnancy group. We identified 33 disease mentions not included in the most common reported causes of MMM. Conclusion Our results demonstrate that clinical text provides additional comorbidities associated with maternal complications that need further clinical research.

Mining the literature for genes associated with placenta-mediated maternal diseases.

Automated literature analysis could significantly speed up understanding of the role of the placenta and the impact of its development and functions on the health of the mother and the child. To facilitate automatic extraction of information about placenta-mediated disorders from the literature, we manually annotated genes and proteins, the associated diseases, and the functions and processes involved in the development and function of placenta in a collection of PubMed/MEDLINE abstracts. We developed three baseline approaches to finding sentences containing this information: one based on supervised machine learning (ML) and two based on distant supervision: 1) using automated detection of named entities and 2) using MeSH. We compare the performance of several well-known supervised ML algorithms and identify two approaches, Support Vector Machines (SVM) and Generalized Linear Models (GLM), which yield up to 98% recall precision and F1 score. We demonstrate that distant supervision approaches could be used at the expense of missing up to 15% of relevant documents.

Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.

With regular expressions and manual review, 18,342 FDA-approved drug product labels were processed to determine if the five standard pregnancy drug risk categories were mentioned in the label. After excluding 81 drugs with multiple-risk categories, 83% of the labels had a risk category within the text and 17% labels did not. We trained a Sequential Minimal Optimization algorithm on the labels containing pregnancy risk information segmented into standard document sections. For the evaluation of the classifier on the testing set, we used the Micromedex drug risk categories. The precautions section had the best performance for assigning drug risk categories, achieving Accuracy 0.79, Precision 0.66, Recall 0.64 and F1 measure 0.65. Missing pregnancy risk categories could be suggested using machine learning algorithms trained on the existing publicly available pregnancy risk information.

Analyzing U.S. prescription lists with RxNorm and the ATC/DDD Index.

OBJECTIVES: To evaluate the suitability of the ATC/DDD Index (Anatomical Therapeutic Chemical (ATC) Classification System/Defined Daily Dose) for analyzing prescription lists in the U.S. METHODS: We mapped RxNorm clinical drugs to ATC. We used this mapping to classify a large set of prescription drugs with ATC and compared the prescribed daily dose to the defined daily dose (DDD) in ATC. RESULTS: 64% of the 11,422 clinical drugs could be precisely mapped to ATC. 97% of the 87,001 RxNorm codes from the prescription dataset could be classified with ATC, and 97% of the prescribed daily doses could be assessed. CONCLUSIONS: Although the mapping of RxNorm ingredients to ATC appears to be largely incomplete, the most frequently prescribed drugs in the prescription dataset we analyzed were covered. This study demonstrates the feasibility of using ATC in conjunction with RxNorm for analyzing U.S. prescription datasets for drug classification and assessment of the prescribed daily doses.