Systems and methods for extracting specified data from narrative text

Embodiments are directed to extracting specified data items from narrative text. In one scenario, a computer system accesses narrative textual information which includes data items that are to be identified and extracted. The computer system identifies specified data items in the narrative textual information that are to be extracted from the narrative textual information. The computer system then filters the identified data items to remove false positive identifications. The false positive filtering includes classifying the identified data items as specified data items, so that classified data items are identified as true positive items that are to be extracted from the narrative textual information. The computer system further extracts, from the narrative textual information, those filtered data items that were classified as being true positive items.

BACKGROUND

Medical errors (and specifically, medication errors) are recognized as an important cause of injuries and death. Many could be prevented. These errors cause an estimated 44,000 to 98,000 deaths and 1,000,000 injuries every year in the U.S. Among these preventable deaths in the U.S., about 7,000 can be associated with medication errors, which is more than the number attributable to work-related injuries. Medication errors also cause 400,000 preventable injuries each year in hospitals, as well as 800,000 injuries in long-term care settings, and 530,000 injuries among Medicare recipients in outpatient clinics. The preventable injuries caused by medication errors in hospitals alone result in around $3.5 billion in additional medical costs.

Computerized physician order-entry (CPOE) systems may be able reduce this risk of medical or medication errors. These systems, however, only reduce errors when they provide decision support, including the detection of interactions between medications, and medication contraindications related to allergies, laboratory testing results, or diseases. These systems typically rely on structured and coded information in the electronic health record (e.g., entries in a problem list), but a substantial proportion of this information is only mentioned in narrative clinical text documents. Newly implemented “meaningful use” rules establish requirements such as maintaining a list of problems, and a list of medication allergies in the electronic health record (EHR) of 80% of inpatients. Besides decision support and lists of problems or allergies, summarizing the patient record, reporting, billing, and secondary uses of clinical data (e.g., clinical research) all require structured and coded information.

BRIEF SUMMARY

Embodiments described herein are directed to extracting specified data items from narrative text. In one embodiment, a computer system accesses narrative textual information which includes data items that are to be identified and extracted. The computer system identifies specified data items in the narrative textual information that are to be extracted from the narrative textual information. The identifying includes at least one of the following: performing a dictionary-based search, performing a pattern-based search and implementing machine learning to identify the data items that are to be extracted. The computer system then filters the identified data items to remove false positive identifications. The false positive filtering includes classifying the identified data items as specified data items, so that classified data items are identified as true positive items that are to be extracted from the narrative textual information. The computer system further extracts, from the narrative textual information, those filtered data items that were classified as being true positive items.

Additional features and advantages will be set forth in the description which follows, and in part will be apparent to one of ordinary skill in the art from the description, or may be learned by the practice of the teachings herein. Features and advantages of embodiments described herein may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. Features of the embodiments described herein will become more fully apparent from the following description and appended claims.

DETAILED DESCRIPTION

Embodiments described herein are directed to extracting specified data items from narrative text. In one embodiment, a computer system accesses narrative textual information which includes data items that are to be identified and extracted. The computer system identifies specified data items in the narrative textual information that are to be extracted from the narrative textual information. The identifying includes at least one of the following: performing a dictionary-based search, performing a pattern-based search and implementing machine learning to identify the data items that are to be extracted. The computer system then filters the identified data items to remove false positive identifications. The false positive filtering includes classifying the identified data items as specified data items, so that classified data items are identified as true positive items that are to be extracted from the narrative textual information. The computer system further extracts, from the narrative textual information, those filtered data items that were classified as being true positive items.

The following discussion now refers to a number of methods and method acts that may be performed. It should be noted, that although the method acts may be discussed in a certain order or illustrated in a flow chart as occurring in a particular order, no particular ordering is necessarily required unless specifically stated, or required because an act is dependent on another act being completed prior to the act being performed.

Embodiments described herein may implement various types of computing systems. These computing systems are now increasingly taking a wide variety of forms. Computing systems may, for example, be handheld devices, appliances, laptop computers, desktop computers, mainframes, distributed computing systems, or even devices that have not conventionally been considered a computing system. In this description and in the claims, the term “computing system” is defined broadly as including any device or system (or combination thereof) that includes at least one physical and tangible processor, and a physical and tangible memory capable of having thereon computer-executable instructions that may be executed by the processor. A computing system may be distributed over a network environment and may include multiple constituent computing systems.

Computing systems, as used herein, typically include at least one processing unit and memory. The memory may be physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may also be used herein to refer to non-volatile mass storage such as physical storage media. If the computing system is distributed, the processing, memory and/or storage capability may be distributed as well.

As used herein, the term “executable module” or “executable component” can refer to software objects, routings, or methods that may be executed on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system (e.g., as separate threads).

In the description that follows, embodiments are described with reference to acts that are performed by one or more computing systems. If such acts are implemented in software, one or more processors of the associated computing system that performs the act direct the operation of the computing system in response to having executed computer-executable instructions. For example, such computer-executable instructions may be embodied on one or more computer-readable media that form a computer program product. An example of such an operation involves the manipulation of data. The computer-executable instructions (and the manipulated data) may be stored in the memory of the computing system. Computing system may also contain communication channels that allow the computing system to communicate with other message processors over a wired or wireless network.

Embodiments described herein may comprise or utilize a special-purpose or general-purpose computer system that includes computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. The system memory may be included within the overall memory. The system memory may also be referred to as “main memory”, and includes memory locations that are addressable by the at least one processing unit over a memory bus in which case the address location is asserted on the memory bus itself. System memory has been traditionally volatile, but the principles described herein also apply in circumstances in which the system memory is partially, or even fully, non-volatile.

Still further, system architectures described herein can include a plurality of independent components that each contribute to the functionality of the system as a whole. This modularity allows for increased flexibility when approaching issues of platform scalability and, to this end, provides a variety of advantages. System complexity and growth can be managed more easily through the use of smaller-scale parts with limited functional scope. Platform fault tolerance is enhanced through the use of these loosely coupled modules. Individual components can be grown incrementally as business needs dictate. Modular development also translates to decreased time to market for new functionality. New functionality can be added or subtracted without impacting the core system.

Embodiments described herein generally relate to extracting specified data items from text documents. These data items may include medical problems (such as diagnoses), allergies, treatments, or other types of data items. These text documents may include narrative text, structured, form-based text or other types of text, especially that found in electronic health records (EHRs).

In one embodiment, an EHR enrichment system110may be implemented, as shown inFIG. 1. The EHR enrichment system may also use data mining and rules to infer possible medical problems and allergies (or other specified data) from existing structured clinical information in the EHR. The EHR enrichment system may further combine all this extracted and inferred information, and analyze the existing lists to prevent duplicates, highlight conflicts or possible errors, and notify users of where these lists or other inferred or extracted information came from. The EHR enrichment system110also indicates how the information was extracted or inferred. For instance, the information combining module106may note where the extracted information107and inferred information108were extracted and inferred, respectively.

Embodiments of the EHR enrichment system110are designed to support and help EHR or other types of records to create and maintain complete lists of a patient's medical problems, allergies, etc. As such, at least in some embodiments, the EHR enrichment system may assist in achieving “meaningful use” core objectives including the following: maintain up-to-date problem lists, and maintain active medication allergy lists. The EHR enrichment system110ensures that the medical problems and allergies a patient suffers from, and the medications the patient is taking, are clearly known by their healthcare providers. Moreover, the EHR enrichment system110ensures that this information is available for decision support and quality improvement, thereby improving the health care the patient will receive.

Still further, at least in some embodiments, the EHR enrichment system110assists healthcare providers in maintaining complete and timely lists of problems and allergies, providing them with an efficient overview of a patient, and further helping healthcare organizations attain meaningful use requirements. The EHR enrichment system may be used in inpatient and/or outpatient settings, in hospitals and/or private practices, increasing the efficiency of busy healthcare providers by saving time. The EHR enrichment system may further aid healthcare organizations in demonstrating “meaningful use” and obtaining Centers for Medicare & Medicaid Services incentive payments. Moreover, the EHR enrichment system may include an extended output that uses a modular design, allowing for utilization of each module and accumulated data to be offered as products. The EHR enrichment system may thus be applied to support efficient health care systems by extracting medical problems and allergies (or other information) from narrative or structured text documents (e.g. using the extracting module104). The inferring module105may infer medical problems and allergies or other information from existing structured clinical information, or may access previously (manually entered) information in electronic lists.

The EHR enrichment system110may further be configured to apply Natural Language Processing (NLP) to automatically enrich lists of medical problems and allergies. It may further use NLP in real-time to support clinical care, and may also apply a stepwise hybrid approach for high accuracy information extraction. Natural language processing methods have previously been investigated to extract structured data from narrative text, with accuracy sometimes rivaling humans. However, most recent NLP research in the biomedical domain has focused on biomedical text (i.e. scientific publications), while clinical text has received less interest. Clinical text is often ungrammatical and “telegraphic” in style, rich in ambiguous abbreviations, and sometimes structured with templates or “text tables”.

As mentioned above and as depicted in the conceptual model ofFIG. 1, the EHR enrichment system110provides decision support systems with structured and coded information102about medical problems, allergies and other information. The EHR enrichment system may be configured to automatically extract medical problems, allergies and other types of information in real-time from narrative or other text documents101in the EHR or from another source, discovering possible medical problems, diagnoses, treatments, allergies or other information by mining existing structured clinical information in the EHR, and finally combining all this extracted information in to a data output103such as a dynamic list.

In one embodiment, a reference standard is created for training and testing the EHR enrichment system110. This reference standard may include a random sample of de-identified clinical narrative documents (e.g.101) with problems and allergies (or other specified data) annotated by domain experts. The total number of documents may be based on power calculations, and may ensure sufficient precision for proper measurement. The clinical documents may be selected from the EHR of patients in a study population. The clinical documents may be accessed and then automatically de-identified according to Heath Insurance Portability and Accountability Act (HIPAA) Safe Harbor rules. The reference standard may consist of documents with “annotations”, the latter being structured information (e.g., type of data) with extents of the text. The annotation schema (i.e., a detailed list of information to annotate) may include medical concepts (e.g. problems, allergies, or other specified data), and modifiers (e.g. subject, temporality, negation, certainty).

Embodiments of the EHR enrichment system may thus automatically extract medical problems and allergies (or other specified data) in real-time from text documents including clinical narrative documents. Some embodiments may include the following design parameters: high accuracy (i.e. a focus is placed on high sensitivity, with good positive predictive value), and very fast performance (real-time and scalable). To meet these parameters, the information extraction system may implement a stepwise hybrid approach to maximize sensitivity first, and then filter out false positives to enhance positive predictive value. The information extraction system may also use fast methods and be optimized for speed. Such systems may be built on an enterprise-grade framework. This robust framework offers flexible and powerful scalability capabilities that include massive parallelization.

The information extraction module104may include any number of different components for performing desired functionality. In some embodiments, as shown inFIG. 2, three main components may be used: a text pre-processing component201, a high-sensitivity extraction component202, and a false positives filtering component203. The pre-processing component201may start by detecting and classifying clinical text sections (at least in some cases) based on machine learning classifiers. This may then be followed by sentence detection, in which the text is segmented into sentences, followed by tokenization, which splits the text into tokens such as words, numbers, symbols, and punctuation. The pre-processing component201may then implement part-of-speech (POS) tagging as well as dependency parsing. The dependency parser allows for fast processing, analyzing the syntactic dependencies of a sentence in very short timeframe. It should be noted that the pre-processing may include any one or more of the above components, or may include entirely different components, depending on the implementation.

The high-sensitivity extraction component202focuses on the extraction of possible medical problems, allergies or other information that might be of interest to a medical professional or staff member (provided to the user as extracted information107), aiming at as high sensitivity as possible, even if numerous false positives are also detected. In some embodiments, the high-sensitivity extraction component202detects problems and allergies (or other specified data) with a dictionary lookup module. This dictionary lookup module may implement public or proprietary full-text search and indexing platforms, and may add extensions to known platforms. Pattern matching may implement regular expressions to detect specific strings of characters that denote problems or allergies (or other specified data), adding concepts that could have been missed by the dictionary lookup. Finally, a machine learning-based module may use Conditional Random Fields (CRF) to further enhance the sensitivity of the extraction. As with the pre-processing module, it should be noted that any or all of the above components may be implemented in the high-sensitivity extraction module202. Alternatively, entirely different components may be used if so configured. This is also true for the false positives filtering component203.

The false positives filtering component203classifies each “candidate” problem or allergy (or other specified data) from the high-sensitivity extraction component as a true positive or a false positive, and filters out the latter. It first analyses the local context of the extracted problems and allergies (or other specified data) by looking for expressions that indicate the negation or temporality of the concepts, or whether the patient or someone else is their subject. This functionality may be based on various dependency parse trees and/or lists of keywords. Different features used for the classification task are then extracted. The classifiers may be implemented as binary Support Vector Machines. For instance, one classifier may be implemented for medical problems, and another one for allergies (or still another for another specified data type). Using the features extracted previously, the Support Vector Machines classify each problem or each allergy as a true positive (i.e., a true problem or allergy), or as a false positive.

In the last processing step, the data107is exported in one or more different formats. For example, the data may be exported in a format that can be used to visualize the clinical narratives with highlighted problems and allergies (or other specified data) for quality control, while another format may be used to store the extracted information back into the EHR. These concepts will be described further below with regard to computing architecture300ofFIG. 3and method400ofFIG. 4.

FIG. 3illustrates a computer architecture300in which at least one embodiment may be employed. Computer architecture300includes computer system301. Computer system301may be any type of local or distributed computer system, including a cloud computing system. The computer system301includes modules for performing a variety of different functions. For instance, the communications module304may be configured to communicate with other computing systems. The computing module304may include any wired or wireless communication means that can receive and/or transmit data to or from other computing systems. The communications module304may be configured to interact with databases, mobile computing devices (such as mobile phones or tablets), embedded or other types of computing systems.

In one embodiment, the computer system301may be implemented to extract specified data items from narrative text. It should be noted that while medical problems and allergies are mentioned herein as examples of specified data, substantially any type of data may be identified and extracted using the methods and systems described herein. Accordingly, data accessing module305of computer system301may be configured to access one or more portions of narrative textual information313(410). The narrative textual information may be taken from substantially any type of document, including a clinical document used by a doctor to keep notes about a patient. The narrative textual information313may include various data items314that are to be identified and extracted, such as medical problems or allergies such as allergies to medications. Accordingly, while some of the narrative textual information may not be highly relevant to a medical professional, embodiments described herein are designed to find and filter those data items that are or may be relevant to a medical professional. As such, these data items may comprise medical conditions, problems, allergies, sickness patterns, signs, symptoms, diagnoses, treatments, test results, or other data. In some cases, a user111(such as a doctor or medical staff member) may be able to specify which terms or types of terms are to be identified by identifying module306as specified data items.

In some cases, a clinical document that is to be searched for specified terms is annotated with one or more portions of annotation information. The annotations may have been added manually, or automatically by a software program. In cases where the annotations have been added automatically, an annotations schema may be used to annotate the clinical document. The annotations schema may include a list of specified data items (or data types) that are to be annotated. The annotations schema used to annotate the clinical document may include any of the following: medical problems, allergies (including medication allergies), or any other type of data, including data modifiers that modify the subject, temporality, negation and certainty of a medical condition.

Continuing this embodiment, the identifying module306of computer system301may then identify specified data items307in the narrative textual information313that are to be extracted from the narrative textual information (420). The identifying may include any or all of the following: performing a dictionary-based search, performing a pattern-based search and implementing machine learning to identify the data items that are to be extracted. In some cases, the dictionary search may implement performance parameters that maximize identification of specified data items307at the expense of falsely identifying non-specified data items. Similarly, the pattern-based search may implement sensitive patterns that maximize identification of specified data items at the expense of falsely identifying non-specified data items. Thus, identifying a data item may be performed with high sensitivity to identify every possible specified data type, even if the result is having multiple false positives. The identifying step may also use context around each specified data item in order to properly identify the item. For instance, the identifying module306may use context such as words next to and following the specified data item, as well as the structure of the document, to identify items of interest.

Computer system301may then filter the identified data items to remove false positive identifications (430). The false positive filtering, performed by filtering module308, may include classifying the identified data items307as specified data items, so that classified data items are identified as true positive items315that are to be extracted from the narrative textual information. The extracting module310of computer system301may then extract, from the narrative textual information313, those filtered data items309that were classified as being true positive items315(440). These specified data items may be extracted in real-time as the data items are being filtered from the identified data items307. The determined true positive items315(such as a determined medical condition or allergy) are transferred to a user such as user111or may be transferred to a data store112. The true positive items may be stored in a local or distributed data store, and may be accessible to the user111in a variety of forms. Because the true positive items are identified, filtered and extracted items, it is much more likely that the true positive items315will be useful to medical professional or staff member. Accordingly, methods, systems and computer program products are provided in which specified data items are identified and extracted from narrative text.