Valx: A System for Extracting and Structuring Numeric Lab Test Comparison Statements from Text

Parsing Clinical Trial Eligibility Criteria for Cohort Query by a Multi-Input Multi-Output Sequence Labeling Model

To enable electronic screening of eligible patients for clinical trials, free-text clinical trial eligibility criteria should be translated to a computable format. Natural language processing (NLP) techniques have the potential to automate this process. In this study, we explored a supervised multi-input multi-output (MIMO) sequence labelling model to parse eligibility criteria into combinations of fact and condition tuples. Our experiments on a small manually annotated training dataset showed that that the performance of the MIMO framework with a BERT-based encoder using all the input sequences achieved an overall lenient-level AUROC of 0.61. Although the performance is suboptimal, representing eligibility criteria into logical and semantically clear tuples can potentially make subsequent translation of these tuples into database queries more reliable.

Identifying stroke-related quantified evidence from electronic health records in real-world studies

BACKGROUND

Stroke is one of the leading causes of death and disability worldwide. The National Institutes of Health Stroke Scale (NIHSS) scores in electronic health records (EHRs), which quantitatively describe patients' neurological deficits in evidence-based treatment, are crucial in stroke-related clinical investigations. However, the free-text format and lack of standardization inhibit their effective use. Automatically extracting the scale scores from the clinical free text so that its potential value in real-world studies is realized has become an important goal.

OBJECTIVE

This study aims to develop an automated method to extract scale scores from the free text of EHRs.

METHODS

We propose a two-step pipeline method to identify NIHSS items and numerical scores and validate its feasibility using a freely accessible critical care database: MIMIC-III (Medical Information Mart for Intensive Care III). First, we utilize MIMIC-III to create an annotated corpus. Then, we investigate possible machine learning methods for two subtasks, NIHSS item and score recognition and item-score relation extraction. In the evaluation, we conduct both task-specific and end-to-end evaluations and compare our method with the rule-based method using precision, recall and F1 scores as evaluation metrics.

RESULTS

We use all available discharge summaries of stroke cases in MIMIC-III. The annotated NIHSS corpus contains 312 cases, 2929 scale items, 2774 scores and 2733 relations. The results show that the best F1-score of our method was 0.9006, which was attained by combining BERT-BiLSTM-CRF and Random Forest, and it outperformed the rule-based method (F1-score = 0.8098). In the end-to-end task, our method could successfully recognize the item "1b level of consciousness questions", the score "1" and their relation "('1b level of consciousness questions', '1', 'has value')" from the sentence "1b level of consciousness questions: said name = 1", while the rule-based method could not.

CONCLUSIONS

The two-step pipeline method we propose is an effective approach to identify NIHSS items, scores and their relations. With its help, clinical investigators can easily retrieve and access structured scale data, thereby supporting stroke-related real-world 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.

ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials

Background

Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools.

Objective

The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment.

Methods

We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal’s data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach.

Results

Based on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains.

Conclusions

Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01611-y.

Transformer-Based Named Entity Recognition for Parsing Clinical Trial Eligibility Criteria

The rapid adoption of electronic health records (EHRs) systems has made clinical data available in electronic format for research and for many downstream applications. Electronic screening of potentially eligible patients using these clinical databases for clinical trials is a critical need to improve trial recruitment efficiency. Nevertheless, manually translating free-text eligibility criteria into database queries is labor intensive and inefficient. To facilitate automated screening, free-text eligibility criteria must be structured and coded into a computable format using controlled vocabularies. Named entity recognition (NER) is thus an important first step. In this study, we evaluate 4 state-of-the-art transformer-based NER models on two publicly available annotated corpora of eligibility criteria released by Columbia University (i.e., the Chia data) and Facebook Research (i.e.the FRD data). Four transformer-based models (i.e., BERT, ALBERT, RoBERTa, and ELECTRA) pretrained with general English domain corpora vs. those pretrained with PubMed citations, clinical notes from the MIMIC-III dataset and eligibility criteria extracted from all the clinical trials on ClinicalTrials.gov were compared. Experimental results show that RoBERTa pretrained with MIMIC-III clinical notes and eligibility criteria yielded the highest strict and relaxed F-scores in both the Chia data (i.e., 0.658/0.798) and the FRD data (i.e., 0.785/0.916). With promising NER results, further investigations on building a reliable natural language processing (NLP)-assisted pipeline for automated electronic screening are needed.

Modern Clinical Text Mining: A Guide and Review

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry.

Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review

BACKGROUND

Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data.

METHODS

We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included.

RESULTS

We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F₁ score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care.

CONCLUSION

Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.

How the clinical research community responded to the COVID-19 pandemic: an analysis of the COVID-19 clinical studies in ClinicalTrials.gov

OBJECTIVE

In the past few months, a large number of clinical studies on the novel coronavirus disease (COVID-19) have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the issues that may cause recruitment difficulty or reduce study generalizability.

METHODS

We analyzed 3765 COVID-19 studies registered in the largest public registry-ClinicalTrials.gov, leveraging natural language processing (NLP) and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population.

RESULTS

Our analysis included 2295 interventional studies and 1470 observational studies. Most trials did not explicitly exclude older adults with common chronic conditions. However, known risk factors such as diabetes and hypertension were considered by less than 5% of trials based on their trial description. Pregnant women were excluded by 34.9% of the studies.

CONCLUSIONS

Most COVID-19 clinical studies included both genders and older adults. However, risk factors such as diabetes, hypertension, and pregnancy were under-represented, likely skewing the population that was sampled. A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.

DQueST: dynamic questionnaire for search of clinical trials

Objective

Information overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires.

Materials and Methods

DQueST first performs information extraction and criteria library curation. DQueST transforms criteria narratives in the ClinicalTrials.gov repository into a structured format, normalizes clinical entities using standard concepts, clusters related criteria, and stores the resulting curated library. DQueST then implements a real-time dynamic question generation algorithm. During user interaction, the initial search is similar to a standard search engine, and then DQueST performs real-time dynamic question generation to select criteria from the library 1 at a time by maximizing its relevance score that reflects its ability to rule out ineligible trials. DQueST dynamically updates the remaining trial set by removing ineligible trials based on user responses to corresponding questions. The process iterates until users decide to stop and begin manually reviewing the remaining trials.

Results

In simulation experiments initiated by 10 diseases, DQueST reduced information overload by filtering out 60%–80% of initial trials after 50 questions. Reviewing the generated questions against previous answers, on average, 79.7% of the questions were relevant to the queried conditions. By examining the eligibility of random samples of trials ruled out by DQueST, we estimate the accuracy of the filtering procedure is 63.7%. In a study using 5 mock patient profiles, DQueST on average retrieved trials with a 1.465 times higher density of eligible trials than an existing search engine. In a patient-centered usability evaluation, patients found DQueST useful, easy to use, and returning relevant results.

Conclusion

DQueST contributes a novel framework for transforming free-text eligibility criteria to questions and filtering out clinical trials based on user answers to questions dynamically. It promises to augment keyword-based methods to improve clinical trial search.

Retrieving Lab Test Related Questions from Social Q&A Sites by Combining Shallow Features and Deep Representations

Patients face challenges in accurately interpreting their lab test results. To fulfill their knowledge gap, patients often turn to online resources, such as Community Question-Answering (CQA) sites, to seek meaningful information and support from their peers. Retrieving the most relevant information to patients' queries is important to help patients understand lab test results. However, few studies investigated the retrieval of lab test-related questions on CQA platforms. To address this research gap, we build and evaluate a system that automatically ranks questions about lab tests based on their similarity to a given question. The system is tested using diabetes-related questions collected from Yahoo! Answers' health section. Experimental results show that the regression-weighted combination of deep representations and shallow features was most effective in the Yahoo! Answers dataset. The proposed system can be extended to medical question retrieval, where questions contain a variety of lab tests.

How the clinical research community responded to the COVID-19 pandemic: An analysis of the COVID-19 clinical studies in ClinicalTrials.gov

OBJECTIVE

The novel coronavirus disease (COVID-19), broke out in December 2019, and is now a global pandemic. In the past few months, a large number of clinical studies have been initiated worldwide to find effective therapeutics, vaccines, and preventive strategies for COVID-19. In this study, we aim to understand the landscape of COVID-19 clinical research and identify the gaps such as the lack of population representativeness and issues that may cause recruitment difficulty.

MATERIALS AND METHODS

We analyzed 3,765 COVID-19 studies registered in the largest public registry - ClinicalTrials.gov, leveraging natural language processing and using descriptive, association, and clustering analyses. We first characterized COVID-19 studies by study features such as phase and tested intervention. We then took a deep dive and analyzed their eligibility criteria to understand whether these studies: (1) considered the reported underlying health conditions that may lead to severe illnesses, and (2) excluded older adults, either explicitly or implicitly, which may reduce the generalizability of these studies to the older adults population.

RESULTS

Most trials did not have an upper age limit and did not exclude patients with common chronic conditions such as hypertension and diabetes that are more prevalent in older adults. However, known risk factors that may lead to severe illnesses have not been adequately considered.

CONCLUSIONS

A careful examination of existing COVID-19 studies can inform future COVID-19 trial design towards balanced internal validity and generalizability.

LATTE: A knowledge-based method to normalize various expressions of laboratory test results in free text of Chinese electronic health records

BACKGROUND

A wealth of clinical information is buried in free text of electronic health records (EHR), and converting clinical information to machine-understandable form is crucial for the secondary use of EHRs. Laboratory test results, as one of the most important types of clinical information, are written in various styles in free text of EHRs. This has brought great difficulties for data integration and utilization of EHRs. Therefore, developing technology to normalize different expressions of laboratory test results in free text is indispensable for the secondary use of EHRs.

METHODS

In this study, we developed a knowledge-based method named LATTE (transforming lab test results), which could transform various expressions of laboratory test results into a normalized and machine-understandable format. We first identified the analyte of a laboratory test result with a dictionary-based method and then designed a series of rules to detect information associated with the analyte, including its specimen, measured value, unit of measure, conclusive phrase and sampling factor. We determined whether a test result is normal or abnormal by understanding the meaning of conclusive phrases or by comparing its measured value with an appropriate normal range. Finally, we converted various expressions of laboratory test results, either in numeric or textual form, into a normalized form as "specimen-analyte-abnormality". With this method, a laboratory test with the same type of abnormality would have the same representation, regardless of the way that it is mentioned in free text.

RESULTS

LATTE was developed and optimized on a training set including 8894 laboratory test results from 756 EHRs, and evaluated on a test set including 3740 laboratory test results from 210 EHRs. Compared to experts' annotations, LATTE achieved a precision of 0.936, a recall of 0.897 and an F1 score of 0.916 on the training set, and a precision of 0.892, a recall of 0.843 and an F1 score of 0.867 on the test set. For 223 laboratory tests with at least two different expression forms in the test set, LATTE transformed 85.7% (2870/3350) of laboratory test results into a normalized form. Besides, LATTE achieved F1 scores above 0.8 for EHRs from 18 of 21 different hospital departments, indicating its generalization capabilities in normalizing laboratory test results.

CONCLUSION

In conclusion, LATTE is an effective method for normalizing various expressions of laboratory test results in free text of EHRs. LATTE will facilitate EHR-based applications such as cohort querying, patient clustering and machine learning.

AVAILABILITY

LATTE is freely available for download on GitHub (https://github.com/denglizong/LATTE).

Quantifying risk factors in medical reports with a context-aware linear model

OBJECTIVE

We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behaviors) in text is a well-researched task. However, determining the level of risk associated with them is partly dependent on the textual context in which they appear, which may describe severity, temporal aspects, quantity, etc.

METHODS

To take into account that a given word appearing in the context of different risk factors (medical concepts) can make different contributions toward risk level, we propose a multitask approach, called context-aware linear modeling, which can be applied using appropriately regularized linear regression. To improve the performance for risk factors unseen in training data (eg, rare diseases), we take into account their distributional similarity to other concepts.

RESULTS

The evaluation is based on a corpus of 531 reports from EHRs with 99 376 risk factors rated manually by experts. While context-aware linear modeling significantly outperforms single-task models, taking into account concept similarity further improves performance, reaching the level of human annotators' agreements.

CONCLUSION

Our results show that automatic quantification of risk factors in EHRs can achieve performance comparable to human assessment, and taking into account the multitask structure of the problem and the ability to handle rare concepts is crucial for its accuracy.

Comparing the Study Populations in Dietary Supplement and Drug Clinical Trials for Metabolic Syndrome and Related Disorders

Clinical trials are essential in exploring the safety and efficacy of a new intervention. However, restrictive eligibility criteria pose recruitment challenges that could prolong study durations and reduce study generalizability to the real-world population. The objective of this study is to compare the study populations of dietary supplement (DS) and drug trials on metabolic syndrome related conditions. Using the COMPACT database, we retrieved the DS and drug trials related to metabolic syndrome and performed aggregate analyses on the study populations with respect to various quantitative eligibility criteria. We also extracted and compared baseline characteristics, both quantitative and qualitative, of recruited patients in completed trials. We found similarities and differences in baseline characteristics of enrolled patients between drug and DS clinical trials on metabolic syndrome-related conditions. This comparative aggregate analysis is an initial step towards improving patient recruitment efficacy and population representativeness for clinical trials across conditions and intervention types.

Criteria2Query: a natural language interface to clinical databases for cohort definition

OBJECTIVE

Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.

MATERIALS AND METHODS

Criteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability.

RESULTS

Criteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks.

CONCLUSIONS

We contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

BACKGROUND

Temporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts.

METHODS

A novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple <M, A, N>, TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N.

RESULTS

Based on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries.

CONCLUSIONS

An automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.

A bibliometric analysis of natural language processing in medical research

Background

Natural language processing (NLP) has become an increasingly significant role in advancing medicine. Rich research achievements of NLP methods and applications for medical information processing are available. It is of great significance to conduct a deep analysis to understand the recent development of NLP-empowered medical research field. However, limited study examining the research status of this field could be found. Therefore, this study aims to quantitatively assess the academic output of NLP in medical research field.

Methods

We conducted a bibliometric analysis on NLP-empowered medical research publications retrieved from PubMed in the period 2007–2016. The analysis focused on three aspects. Firstly, the literature distribution characteristics were obtained with a statistics analysis method. Secondly, a network analysis method was used to reveal scientific collaboration relations. Finally, thematic discovery and evolution was reflected using an affinity propagation clustering method.

Results

There were 1405 NLP-empowered medical research publications published during the 10 years with an average annual growth rate of 18.39%. 10 most productive publication sources together contributed more than 50% of the total publications. The USA had the highest number of publications. A moderately significant correlation between country’s publications and GDP per capita was revealed. Denny, Joshua C was the most productive author. Mayo Clinic was the most productive affiliation. The annual co-affiliation and co-country rates reached 64.04% and 15.79% in 2016, respectively. 10 main great thematic areas were identified including Computational biology, Terminology mining, Information extraction, Text classification, Social medium as data source, Information retrieval, etc.

Conclusions

A bibliometric analysis of NLP-empowered medical research publications for uncovering the recent research status is presented. The results can assist relevant researchers, especially newcomers in understanding the research development systematically, seeking scientific cooperation partners, optimizing research topic choices and monitoring new scientific or technological activities.

Clinical information extraction applications: A literature review

BACKGROUND

With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.

OBJECTIVES

In this literature review, we present a review of recent published research on clinical information extraction (IE) applications.

METHODS

A literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library.

RESULTS

A total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations.

CONCLUSIONS

Clinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.

EliIE: An open-source information extraction system for clinical trial eligibility criteria

OBJECTIVE

To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5.0.

MATERIALS AND METHODS

EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer's clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling-based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring.

RESULTS

In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation.

CONCLUSIONS

This study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.

The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0

Objective

The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics.

Methods

Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted.

Results

A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness.

Conclusions

Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.

Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose

It is widely acknowledged that information extraction of unstructured clinical notes using natural language processing (NLP) and text mining is essential for secondary use of clinical data for clinical research and practice. Lab test results are currently structured in most of the electronic health record (EHR) systems. However, for referral patients or lab tests that can be done in non-clinical setting, the results can be captured in unstructured clinical notes. In this study, we proposed a rule-based information extraction system to extract the lab test results with temporal information from clinical notes. The lab test results of glucose and HbA1c from 104 randomly sampled diabetes patients selected from 1996 to 2015 are extracted and further correlated with structured lab test information in the Mayo Clinic EHRs. The system has high F1-scores of 0.964, 0.967 and 0.966 in glucose, HbA1c and overall extraction, respectively.

Comparing and Contrasting A Priori and A Posteriori Generalizability Assessment of Clinical Trials on Type 2 Diabetes Mellitus

Clinical trials are indispensable tools for evidence-based medicine. However, they are often criticized for poor generalizability. Traditional trial generalizability assessment can only be done after the trial results are published, which compares the enrolled patients with a convenience sample of real-world patients. However, the proliferation of electronic data in clinical trial registries and clinical data warehouses offer a great opportunity to assess the generalizability during the design phase of a new trial. In this work, we compared and contrasted a priori (based on eligibility criteria) and a posteriori (based on enrolled patients) generalizability of Type 2 diabetes clinical trials. Further, we showed that comparing the study population selected by the clinical trial eligibility criteria to the real-world patient population is a good indicator of the generalizability of trials. Our findings demonstrate that the a priori generalizability of a trial is comparable to its a posteriori generalizability in identifying restrictive quantitative eligibility criteria.

An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria

Eligibility criteria are important for clinical research protocols or clinical practice guidelines for determining who qualify for studies and to whom clinical evidence is applicable, but the free-text format is not amenable for computational processing. In this paper, we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer's clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models. We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model (CDM) v5. We created a database schema design to store syntactic relations to facilitate efficient cohort queries. We further discussed the potential of applying this method to trials on other diseases and the promise of using it to accelerate clinical research with electronic health records.

GIST 2.0: A scalable multi-trait metric for quantifying population representativeness of individual clinical studies

The design of randomized controlled clinical studies can greatly benefit from iterative assessments of population representativeness of eligibility criteria. We propose a multi-trait metric - GIST 2.0 that can compute the a priori generalizability based on the population representativeness of a clinical study by explicitly modeling the dependencies among all eligibility criteria. We evaluate this metric on twenty clinical studies of two diseases and analyze how a study's eligibility criteria affect its generalizability (collectively and individually). We statistically analyze the effects of trial setting, trait selection and trait summarizing technique on GIST 2.0. Finally we provide theoretical as well as empirical validations for the expected properties of GIST 2.0.