Temporal reasoning with medical data--a review with emphasis on medical natural language processing

Tools for improving the characterization and visualization of changes in neuro-oncology patients

Capturing how a patient's medical problems change over time is important for understanding the progression of a disease, its effects, and response to treatment. We describe two prototype tools that are being developed as part of a data processing pipeline for standardizing, structuring, and visualizing problems and findings documented in clinical reports associated with neuro-oncology patients. Given a list of problems and findings identified using a natural language processing (NLP) system, we have created a mapping tool that assigns an observation of a problem to one of nine classes that describe change. The second tool utilizes iconic representations of the nine classes to generate a timeline interface, enabling users to pan, zoom, and filter the data. The result of this preliminary work is an automated approach for understanding and summarizing the evolution of a problem within the patient electronic medical record.

Extracting timing and status descriptors for colonoscopy testing from electronic medical records

Colorectal cancer (CRC) screening rates are low despite confirmed benefits. The authors investigated the use of natural language processing (NLP) to identify previous colonoscopy screening in electronic records from a random sample of 200 patients at least 50 years old. The authors developed algorithms to recognize temporal expressions and 'status indicators', such as 'patient refused', or 'test scheduled'. The new methods were added to the existing KnowledgeMap concept identifier system, and the resulting system was used to parse electronic medical records (EMR) to detect completed colonoscopies. Using as the 'gold standard' expert physicians' manual review of EMR notes, the system identified timing references with a recall of 0.91 and precision of 0.95, colonoscopy status indicators with a recall of 0.82 and precision of 0.95, and references to actually completed colonoscopies with recall of 0.93 and precision of 0.95. The system was superior to using colonoscopy billing codes alone. Health services researchers and clinicians may find NLP a useful adjunct to traditional methods to detect CRC screening status. Further investigations must validate extension of NLP approaches for other types of CRC screening applications.

A Neuro-Oncology Workstation for Structuring, Modeling, and Visualizing Patient Records

The patient medical record contains a wealth of information consisting of prior observations, interpretations, and interventions that need to be interpreted and applied towards decisions regarding current patient care. Given the time constraints and the large-often extraneous-amount of data available, clinicians are tasked with the challenge of performing a comprehensive review of how a disease progresses in individual patients. To facilitate this process, we demonstrate a neuro-oncology workstation that assists in structuring and visualizing medical data to promote an evidence-based approach for understanding a patient's record. The workstation consists of three components: 1) a structuring tool that incorporates natural language processing to assist with the extraction of problems, findings, and attributes for structuring observations, events, and inferences stated within medical reports; 2) a data modeling tool that provides a comprehensive and consistent representation of concepts for the disease-specific domain; and 3) a visual workbench for visualizing, navigating, and querying the structured data to enable retrieval of relevant portions of the patient record. We discuss this workstation in the context of reviewing cases of glioblastoma multiforme patients.

Towards temporal relation discovery from the clinical narrative

Disease progression and understanding relies on temporal concepts. Discovery of automated temporal relations and timelines from the clinical narrative allows for mining large data sets of clinical text to uncover patterns at the disease and patient level. Our overall goal is the complex task of building a system for automated temporal relation discovery. As a first step, we evaluate enabling methods from the general natural language processing domain - deep parsing and semantic role labeling in predicate-argument structures - to explore their portability to the clinical domain. As a second step, we develop an annotation schema for temporal relations based on TimeML. In this paper we report results and findings from these first steps. Our next efforts will scale up the data collection to develop domain-specific modules for the enabling technologies within Mayo's open-source clinical Text Analysis and Knowledge Extraction System.

What can natural language processing do for clinical decision support?

Computerized clinical decision support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. natural language processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.

Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.

Using empiric semantic correlation to interpret temporal assertions in clinical texts

OBJECTIVE

To measure the uncertainty of temporal assertions like "3 weeks ago" in clinical texts.

DESIGN

Temporal assertions extracted from narrative clinical reports were compared to facts extracted from a structured clinical database for the same patients.

MEASUREMENTS

The authors correlated the assertions and the facts to determine the dependence of the uncertainty of the assertions on the semantic and lexical properties of the assertions.

RESULTS

The observed deviation between the stated duration and actual duration averaged about 20% of the stated deviation. Linear regression revealed that assertions about events further in the past tend to be more uncertain, smaller numeric values tend to be more uncertain (1 mo v. 30 d), and round numbers tend to be more uncertain (10 versus 11 yrs).

CONCLUSIONS

The authors empirically derived semantics behind statements of duration using "ago," and verified intuitions about how numbers are used.

Fuzzy temporal constraint networks for clinical information

Modeling the temporal information in the medical record is an important area of research. This paper describes an extension of TimeText, a temporal reasoning system designed to represent, extract, and reason about temporal information in clinical text, to include the use of fuzzy temporal constraints. The addition of fuzzy temporal constraints increases TimeTexts ability to handle uncertainty in temporal relations. We use a three-state, staircase possibility distribution function in conjunction with earlier methods of finding solutions to fuzzy temporal constraint networks. We perform analysis to determine the complexity of using this staircase in conjunction with finding solutions to fuzzy temporal constraint satisfaction problems and show that these solutions can be efficiently computed in O(n3).

From episodes of care to diagnosis codes: automatic text categorization for medico-economic encoding

We report on the design and evaluation of an original system to help assignment ICD (International Classification of Disease) codes to clinical narratives. The task is defined as a multi-class multi-document classification task. We combine a set of machine learning and data-poor methods to generate a single automatic text categorizer, which returns a ranked list of ICD codes. The combined ranking system currently obtains a precision of 75% at high ranks and a recall of about 63% for the top twenty returned codes for a theoretical upper bound of about 79% (inter-coder agreement). The performance of the data-poor classifier is weak, whereas the use of tempo-rally-typed contents such as anamnesis and prescription free text sections results in a statistically significant improvement.

The evaluation of a temporal reasoning system in processing clinical discharge summaries

CONTEXT

TimeText is a temporal reasoning system designed to represent, extract, and reason about temporal information in clinical text.

OBJECTIVE

To measure the accuracy of the TimeText for processing clinical discharge summaries.

DESIGN

Six physicians with biomedical informatics training served as domain experts. Twenty discharge summaries were randomly selected for the evaluation. For each of the first 14 reports, 5 to 8 clinically important medical events were chosen. The temporal reasoning system generated temporal relations about the endpoints (start or finish) of pairs of medical events. Two experts (subjects) manually generated temporal relations for these medical events. The system and expert-generated results were assessed by four other experts (raters). All of the twenty discharge summaries were used to assess the system's accuracy in answering time-oriented clinical questions. For each report, five to ten clinically plausible temporal questions about events were generated. Two experts generated answers to the questions to serve as the gold standard. We wrote queries to retrieve answers from system's output.

MEASUREMENTS

Correctness of generated temporal relations, recall of clinically important relations, and accuracy in answering temporal questions.

RESULTS

The raters determined that 97% of subjects' 295 generated temporal relations were correct and that 96.5% of the system's 995 generated temporal relations were correct. The system captured 79% of 307 temporal relations determined to be clinically important by the subjects and raters. The system answered 84% of the temporal questions correctly.

CONCLUSION

The system encoded the majority of information identified by experts, and was able to answer simple temporal questions.