Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text.

OBJECTIVES: This study aimed to enable clinical researchers without expertise in natural language processing (NLP) to extract and analyze information about sexual and reproductive health (SRH), or other sensitive health topics, from large sets of clinical notes. METHODS: (1) We retrieved text from the electronic health record as individual notes. (2) We segmented notes into sentences using one of scispaCy's NLP toolkits. (3) We exported sentences to the labeling application Watchful and annotated subsets of these as relevant or irrelevant to various SRH categories by applying a combination of regular expressions and manual annotation. (4) The labeled sentences served as training data to create machine learning models for classifying text; specifically, we used spaCy's default text classification ensemble, comprising a bag-of-words model and a neural network with attention. (5) We applied each model to unlabeled sentences to identify additional references to SRH with novel relevant vocabulary. We used this information and repeated steps 3 to 5 iteratively until the models identified no new relevant sentences for each topic. Finally, we aggregated the labeled data for analysis. RESULTS: This methodology was applied to 3,663 Child Neurology notes for 971 female patients. Our search focused on six SRH categories. We validated the approach using two subject matter experts, who independently labeled a sample of 400 sentences. Cohen's kappa values were calculated for each category between the reviewers (menstruation: 1, sexual activity: 0.9499, contraception: 0.9887, folic acid: 1, teratogens: 0.8864, pregnancy: 0.9499). After removing the sentences on which reviewers did not agree, we compared the reviewers' labels to those produced via our methodology, again using Cohen's kappa (menstruation: 1, sexual activity: 1, contraception: 0.9885, folic acid: 1, teratogens: 0.9841, pregnancy: 0.9871). CONCLUSION: Our methodology is reproducible, enables analysis of large amounts of text, and has produced results that are highly comparable to subject matter expert manual review.

A retrospective textual analysis of sexual and reproductive health counseling for adolescent and young adult people with epilepsy of gestational capacity.

Rationale The American Academy of Neurology (AAN) recommends annual sexual and reproductive health (SRH) counseling for all people with epilepsy of gestational capacity (PWEGC). Child neurologists report discussing SRH concerns infrequently with adolescents. Limited research exists regarding documentation of such counseling. METHODS: We retrospectively studied clinical notes using natural language processing to investigate child neurologists' documentation of SRH counseling for adolescent and young adult PWEGC. We segmented notes into sentences and evaluated for references to menstruation, sexual activity, contraception, folic acid, teratogens, and pregnancy. We developed training sets in a labeling application and used machine learning to identify additional counseling instances. We repeated this iteratively until we identified no new relevant sentences. We validated results using external reviewers; after removing sentences reviewers disagreed on (n = 13/400), we calculated Cohen's kappa values between the model and reviewers (>0.98 for all categories). We evaluated labeled texts for each patient per calendar year with descriptive statistics and logistic regression, adjusting for race/ethnicity, age, and teratogen use. RESULTS: Data comprised 971 PWEGC age 13-21 years with 2277 patient-years and 3663 outpatient child neurology notes. Nearly half of patient-years lacked SRH counseling documentation (49.1%). Among all patients, 38.0% never had SRH counseling documented. Documentation was present regarding menstruation in 26.7% of patient-years, folic acid in 25.0%, contraception in 21.9%, pregnancy in 3.5%, teratogens in 3.0%, and sexual activity in 1.8%. Documentation regarding menstruation and contraception was associated with prescription of antiseizure medications that have a higher risk of teratogenic effects (OR = 1.27, p = 0.020, 95% CI = [1.04,1.54]; OR = 1.27, p = 0.027, 95% CI = [1.03,1.58]). Documentation regarding contraception, folic acid, and sexual activity was increased among older patients (OR = 1.28, p < 0.001, 95% CI = [1.21,1.35]; OR = 1.26, p < 0.001, 95% CI = [1.19,1.32]; OR = 1.26, p = 0.004, 95% CI = [1.08,1.47]). Documentation regarding sexual activity was decreased among patients identifying as White/Non-Hispanic (OR = 0.39, p = 0.007, 95% CI = [0.20,0.78]). CONCLUSION: Child neurologists counsel PWEGC on SRH less frequently than recommended by the AAN based on documentation.

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

The triple variable index combines information generated over time from common monitoring variables to identify patients expressing distinct patterns of intraoperative physiology.

BACKGROUND: Mean arterial pressure (MAP), bispectral index (BIS), and minimum alveolar concentration (MAC) represent valuable, yet dynamic intraoperative monitoring variables. They provide information related to poor outcomes when considered together, however their collective behavior across time has not been characterized. METHODS: We have developed the Triple Variable Index (TVI), a composite variable representing the sum of z-scores from MAP, BIS, and MAC values that occur together during surgery. We generated a TVI expression profile, defined as the sequential TVI values expressed across time, for each surgery where concurrent MAP, BIS, and MAC monitoring occurred in an adult patient (≥18 years) at the University of Pittsburgh Medical Center between January and July 2014 (n = 5296). Patterns of TVI expression were identified using k-means clustering and compared across numerous patient, procedure, and outcome characteristics. TVI and the triple low state were compared as prediction models for 30-day postoperative mortality. RESULTS: The median frequency MAP, BIS, and MAC were recorded was one measurement every 3, 5, and 5 min. Three expression patterns were identified: elevated, mixed, and depressed. The elevated pattern displayed the highest average MAP, BIS, and MAC values (86.5 mmHg, 45.3, and 0.98, respectively), while the depressed pattern displayed the lowest values (76.6 mmHg, 38.0, 0.66). Patterns (elevated, mixed, depressed) were distinct across the following characteristics: average patient age (52, 53, 54 years), American Society of Anesthesiologists Physical Status 4 (6.7, 16.1, 27.3%) and 5 (0.1, 0.6, 1.6%) categories, cardiac (2.2, 6.5, 16.1%) and emergent (5.8, 10.5, 12.8%) surgery, cardiopulmonary bypass use (0.3, 2.6, 9.8%), intraoperative medication administration including etomidate (3.0, 7.3, 12.6%), hydromorphone (47.6, 26.3, 25.2%), ketamine (11.2, 4.6, 3.0%), dexmedetomidine (18.4, 16.6, 13.6%), phenylephrine (74.0, 74.8, 83.0), epinephrine (2.0, 6.0, 18.0%), norepinephrine (2.4, 7.5, 21.2%), vasopressin (3.4, 7.6, 21.0%), succinylcholine (74.0, 69.0, 61.9%), intraoperative hypotension (28.8, 33.0, 52.3%) and the triple low state (9.4, 30.3, 80.0%) exposure, and 30-day postoperative mortality (0.8, 2.7, 5.6%). TVI was a better predictor of patients that died or survived in the 30 days following surgery compared to cumulative triple low state exposure (AUC 0.68 versus 0.62, p < 0.05). CONCLUSIONS: Surgeries that share similar patterns of TVI expression display distinct patient, procedure, and outcome characteristics.

Rationale and Design of the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis Study. Alpha-1 Protocol.

Severe deficiency of alpha-1 antitrypsin has a highly variable clinical presentation. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis α1 Study is a prospective, multicenter, cross-sectional study of adults older than age 35 years with PiZZ or PiMZ alpha-1 antitrypsin genotypes. It is designed to better understand if microbial factors influence this heterogeneity. Clinical symptoms, pulmonary function testing, computed chest tomography, exercise capacity, and bronchoalveolar lavage (BAL) will be used to define chronic obstructive pulmonary disease (COPD) phenotypes that can be studied with an integrated systems biology approach that includes plasma proteomics; mouth, BAL, and stool microbiome and virome analysis; and blood microRNA and blood mononuclear cell RNA and DNA profiling. We will rely on global genome, transcriptome, proteome, and metabolome datasets. Matched cohorts of PiZZ participants on or off alpha-1 antitrypsin augmentation therapy, PiMZ participants not on augmentation therapy, and control participants from the Subpopulations and Intermediate Outcome Measures in COPD Study who match on FEV1 and age will be compared. In the primary analysis, we will determine if the PiZZ individuals on augmentation therapy have a difference in lower respiratory tract microbes identified compared with matched PiZZ individuals who are not on augmentation therapy. By characterizing the microbiome in alpha-1 antitrypsin deficiency (AATD), we hope to define new phenotypes of COPD that explain some of the diversity of clinical presentations. As a unique genetic cause of COPD, AATD may inform typical COPD pathogenesis, and better understanding of it may illuminate the complex interplay between environment and genetics. Although the biologic approaches are hypothesis generating, the results may lead to development of novel biomarkers, better understanding of COPD phenotypes, and development of novel diagnostic and therapeutic trials in AATD and COPD. Clinical trial registered with www.clinicaltrials.gov (NCT01832220).

Rationale and Design of the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) Study. Sarcoidosis Protocol.

Sarcoidosis is a systemic disease characterized by noncaseating granulomatous inflammation with tremendous clinical heterogeneity and uncertain pathobiology and lacking in clinically useful biomarkers. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study is an observational cohort study designed to explore the role of the lung microbiome and genome in these two diseases. This article describes the design and rationale for the GRADS study sarcoidosis protocol. The study addresses the hypothesis that distinct patterns in the lung microbiome are characteristic of sarcoidosis phenotypes and are reflected in changes in systemic inflammatory responses as measured by peripheral blood changes in gene transcription. The goal is to enroll 400 participants, with a minimum of 35 in each of 9 clinical phenotype subgroups prioritized by their clinical relevance to understanding of the pathobiology and clinical heterogeneity of sarcoidosis. Participants with a confirmed diagnosis of sarcoidosis undergo a baseline visit with self-administered questionnaires, chest computed tomography, pulmonary function tests, and blood and urine testing. A research or clinical bronchoscopy with a research bronchoalveolar lavage will be performed to obtain samples for genomic and microbiome analyses. Comparisons will be made by blood genomic analysis and with clinical phenotypic variables. A 6-month follow-up visit is planned to assess each participant's clinical course. By the use of an integrative approach to the analysis of the microbiome and genome in selected clinical phenotypes, the GRADS study is powerfully positioned to inform and direct studies on the pathobiology of sarcoidosis, identify diagnostic or prognostic biomarkers, and provide novel molecular phenotypes that could lead to improved personalized approaches to therapy for sarcoidosis.

Disease insights through cross-species phenotype comparisons.

New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient's set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene.

Assessing the usability of a telemedicine-based Medication Delivery Unit for older adults through inspection methods.

Polypharmacy and medication non-adherence are common in older adults, potentially leading to medication-related problems and increased healthcare expenditures. Medication Delivery Units (MDUs) may improve adherence, but their interfaces may present usability challenges for older adults with age-related impairments. We used a combination of three inspection methods - heuristic evaluation, cognitive walkthrough, and simulated elderly interaction, to identify potential concerns with the usability of a commercially available telemedicine MDU. Each method revealed different problems, with repeated discoveries via different methods providing triangulated evidence. Despite the MDU's general usability, issues of varying severity were discovered. Significant usability issues associated with physical interactions with the MDU included loading and unloading the medication blister packs, and opening the delivered medication prior to administration. Less severe issues centered on small text sizes and poor user feedback. Further usability testing, involving older adults with a variety of impairments, is needed to validate findings.