Ontology-based categorization of clinical studies by their conditions.

OBJECTIVE: The free-text Condition data field in the ClinicalTrials.gov is not amenable to computational processes for retrieving, aggregating and visualizing clinical studies by condition categories. This paper contributes a method for automated ontology-based categorization of clinical studies by their conditions. MATERIALS AND METHODS: Our method first maps text entries in ClinicalTrials.gov's Condition field to standard condition concepts in the OMOP Common Data Model by using SNOMED CT as a reference ontology and using Usagi for concept normalization, followed by hierarchical traversal of the SNOMED ontology for concept expansion, ontology-driven condition categorization, and visualization. We compared the accuracy of this method to that of the MeSH-based method. RESULTS: We reviewed the 4,506 studies on Vivli.org categorized by our method. Condition terms of 4,501 (99.89%) studies were successfully mapped to SNOMED CT concepts, and with a minimum concept mapping score threshold, 4,428 (98.27%) studies were categorized into 31 predefined categories. When validating with manual categorization results on a random sample of 300 studies, our method achieved an estimated categorization accuracy of 95.7%, while the MeSH-based method had an accuracy of 85.0%. CONCLUSION: We showed that categorizing clinical studies using their Condition terms with referencing to SNOMED CT achieved a better accuracy and coverage than using MeSH terms. The proposed ontology-driven condition categorization was useful to create accurate clinical study categorization that enables clinical researchers to aggregate evidence from a large number of clinical studies.

Deep learning for rare disease: A scoping review.

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of each rare disease causes formidable challenges in accurately diagnosing and caring for these patients and engaging participants in research to advance treatments. Deep learning has advanced many scientific fields and has been applied to many healthcare tasks. This study reviewed the current uses of deep learning to advance rare disease research. Among the 332 reviewed articles, we found that deep learning has been actively used for rare neoplastic diseases (250/332), followed by rare genetic diseases (170/332) and rare neurological diseases (127/332). Convolutional neural networks (307/332) were the most frequently used deep learning architecture, presumably because image data were the most commonly available data type in rare disease research. Diagnosis is the main focus of rare disease research using deep learning (263/332). We summarized the challenges and future research directions for leveraging deep learning to advance rare disease research.

Representation and Normalization of Complex Interventions for Evidence Computing.

Complex interventions are ubiquitous in healthcare. A lack of computational representations and information extraction solutions for complex interventions hinders accurate and efficient evidence synthesis. In this study, we manually annotated and analyzed 3,447 intervention snippets from 261 randomized clinical trial (RCT) abstracts and developed a compositional representation for complex interventions, which captures the spatial, temporal and Boolean relations between intervention components, along with an intervention normalization pipeline that automates three tasks: (i) treatment entity extraction; (ii) intervention component relation extraction; and (iii) attribute extraction and association. 361 intervention snippets from 29 unseen abstracts were included to report on the performance of the evaluation. The average F-measure was 0.74 for treatment entity extraction on an exact match and 0.82 for attribute extraction. The F-measure for relation extraction of multi-component complex interventions was 0.90. 93% of extracted attributes were correctly attributed to corresponding treatment entities.

Data-Driven Modeling of Randomized Controlled Trial Outcomes.

Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a data-driven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies.

Combining human and machine intelligence for clinical trial eligibility querying.

OBJECTIVE: To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries. MATERIALS AND METHODS: Criteria2Query (C2Q) 2.0 was developed to enable real-time user intervention for criteria selection and simplification, parsing error correction, and concept mapping. The accuracy, precision, recall, and F1 score of enhanced modules for negation scope detection, temporal and value normalization were evaluated using a previously curated gold standard, the annotated eligibility criteria of 1010 COVID-19 clinical trials. The usability and usefulness were evaluated by 10 research coordinators in a task-oriented usability evaluation using 5 Alzheimer's disease trials. Data were collected by user interaction logging, a demographic questionnaire, the Health Information Technology Usability Evaluation Scale (Health-ITUES), and a feature-specific questionnaire. RESULTS: The accuracies of negation scope detection, temporal and value normalization were 0.924, 0.916, and 0.966, respectively. C2Q 2.0 achieved a moderate usability score (3.84 out of 5) and a high learnability score (4.54 out of 5). On average, 9.9 modifications were made for a clinical study. Experienced researchers made more modifications than novice researchers. The most frequent modification was deletion (5.35 per study). Furthermore, the evaluators favored cohort queries resulting from modifications (score 4.1 out of 5) and the user engagement features (score 4.3 out of 5). DISCUSSION AND CONCLUSION: Features to engage domain experts and to overcome the limitations in automated machine output are shown to be useful and user-friendly. We concluded that human-computer collaboration is key to improving the adoption and user-friendliness of natural language processing.

Potential Role of Clinical Trial Eligibility Criteria in Electronic Phenotyping.

2,719 distinctive phenotyping variables from 176 electronic phenotypes were compared with 57,150 distinctive clinical trial eligibility criteria concepts to assess the phenotype knowledge overlap between them. We observed a high percentage (69.5%) of eMERGE phenotype features and a lower percentage (47.6%) of OHDSI phenotype features matched to clinical trial eligibility criteria, possibly due to the relative emphasis on specificity for eMERGE phenotypes and the relative emphasis on sensitivity for OHDSI phenotypes. The study results show the potential of reusing clinical trial eligibility criteria for phenotyping feature selection and moderate benefits of using them for local cohort query implementation.

Pumpless microfluidic device with open top cell culture under oscillatory shear stress.

Here we developed a 96-well plate-based pumpless microfluidic device to mimic bidirectional oscillatory shear stress experienced by osteoblasts at the endosteal niche located at the interface between bone and bone marrow. The culture device was designed to be high-throughput with 32 open top culture chambers for convenient cell seeding and staining. Mathematical modeling was used to simulate the control of oscillatory shear stress with the peak stress in the range of 0.3 to 50 mPa. Osteoblasts, cultured under oscillatory shear stress, were found to be highly viable and significantly aligned along the direction of flow. The modeling and experimental results demonstrate for the first time that cells can be cultured under controllable oscillatory shear stress in the open top culture chamber and pumpless configurations.

Pumpless platform for high-throughput dynamic multicellular culture and chemosensitivity evaluation.

We report here a novel pumpless, 96-well plate-based platform for high-throughput dynamic multicellular culture and chemosensitivity evaluation. A gravity-driven flow strategy was developed to generate and sustain the flow rate of culture medium within 10% in the platform's 20 culture chambers. The ability of the platform to generate and sustain the medium flow was demonstrated by computational simulation, flow visualization, and ascertaining the previously known effect of flow-induced shear stress on the stimulated osteogenic differentiation of osteoblasts. The high-throughput utility of the platform was demonstrated by in situ cell staining and high content screening of chemosensitivity assays of multiple myeloma and osteoblast co-cultures. Endpoint characterization and data analyses for all 20 culture chambers required less than 1 hour.