Clinical information extraction applications: A literature review.

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review

Extracting information from the text of electronic medical records to improve case detection: a systematic review.

With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers’ ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.

/pdf/an-overview-of-topic-modeling-and-its-current-applications-3w89c1fp8v.pdf

An overview of topic modeling and its current applications in bioinformatics

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation.

/pdf/clinical-text-data-in-machine-learning-systematic-review-2dxod9n6lm.pdf

Clinical Text Data in Machine Learning: Systematic Review.

Background

Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes.



Objectives

The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction.



Methods

A secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing.



Results

The performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies.



Conclusions

A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data.


Resumen
Clasificacion Automatizada del Resultado de los Informes de Imagen de la Tomografia Computarizada en el Servicio de Urgencias

Introduccion

La fiabilidad de los resultados resumidos de texto libre de las historias clinicas electronicas sigue siendo un reto. Mientras que la clasificacion automatizada de texto libre ha sido un tema frecuente en informatica medica, la validacion del rendimiento usando datos clinicos del mundo real ha sido limitada. Las dos principales aproximaciones son la linguistica (procesamiento de lenguaje natural, PLN) y la estadistica (aprendizaje de maquina, AM). Los autores han desarrollado un sistema hibrido para resumir los informes de la tomografia computarizada (TC) para resultados especificados.



Objetivos

Medir el rendimiento de un sistema hibrido de PLN y AM para la clasificacion automatizada de resultados de los informes de imagen de la TC en el servicio de urgencias (SU). La hipotesis fue que un sistema es comparable a un medico resumiendo los datos.



Metodologia

Se realizo un analisis secundario de un estudio previo de diagnostico por imagen en 3.710 victimas de traumatismo facial cerrado. Los radiologos dictaron los informes de la TC como un texto libre, que fue despues disociado. Un experto en resumen de datos codifico manualmente el resultado de referencia estandar de la fractura de orbita aguda, con una aleatorizacion de los grupos doblemente codificada para la fiabilidad. El conjunto de datos fue dividido de forma aleatoria en formacion e instrumentos de validacion. Los informes del paciente de entrenamiento se usaron como entrada a la herramienta de PLN (Medical Language Extraction and Encoding [MedLEE]) para crear una salida estructurada que contuviera terminos medicos estandarizados y modificadores para certeza y estado temporal. Los resultados se filtraron para modificadores de baja certeza y pasado/futuro, y despues se combinaron con la referencia estandar manual para generar clasificadores de arbol de decision usando las herramientas de extraccion de datos WEKA 3.7.5 y Salford Predictive Miner 6.6. El rendimiento de los clasificadores del arbol de decision se evaluo con un instrumento de validacion con y sin procesador de PLN.



Resultados

El rendimiento del AM aislado fue comparable a estudios previos de PLN (sensibilidad = 0,92, especificidad = 0.93, precision = 0,95, recuerdo = 0,93, f-score = 0,94) y el uso combinado de PLN y AM muestra una mejoria (sensibilidad = 0,93, especificidad = 0,97, precision = 0,97, recuerdo = 0.96, f-score = 0,97). Este rendimiento es similar a, o mayor que, el del personal medico en estudios previos.



Conclusiones

Un sistema de clasificacion automatico de un hibrido de PLN y AM se muestra prometedor en la codificacion de datos clinicos electronicos de texto libre.

Automated outcome classification of emergency department computed tomography imaging reports.

Electronic health records (EHRs) contain important clinical information about patients. Some of these data are in the form of free text and require preprocessing to be able to used in automated systems. Efficient and effective use of this data could be vital to the speed and quality of health care. As a case study, we analyzed classification of CT imaging reports into binary categories. In addition to regular text classification, we utilized topic modeling of the entire dataset in various ways. Topic modeling of the corpora provides interpretable themes that exist in these reports. Representing reports according to their topic distributions is more compact than bag-of-words representation and can be processed faster than raw text in subsequent automated processes. A binary topic model was also built as an unsupervised classification approach with the assumption that each topic corresponds to a class. And, finally an aggregate topic classifier was built where reports are classified based on a single discriminative topic that is determined from the training dataset. Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.

Topic Modeling Based Classification of Clinical Reports

Large amount of electronic clinical data encompass important information in free text format. To be able to help guide medical decision-making, text needs to be efficiently processed and coded. In this research, we investigate techniques to improve classification of Emergency Department computed topography (CT) reports. The proposed system uses Natural Language Processing (NLP) to generate structured output from patient reports and then applies machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Topic modeling of the corpora is also utilized as an alternative representation of the patient reports. Our results show that both NLP and topic modeling improve raw text classification results. Within NLP features, filtering the codes using modifiers produces the best performance. Topic modeling, on the other hand, shows mixed results. Topic vectors provide good dimensionality reduction and get comparable classification results as with NLP features. However, binary topic classification fails to improve upon raw text classification.

Clinical Report Classification Using Natural Language Processing and Topic Modeling

More and more, patient information is being stored in digital formats;however, to be able to maximize its usefulness, automated tools needs to be built that can effectively and efficiently process these records. Clinical decision support systems are such tools that can recommend the need for a certain medical test or therapy by examining prior patient information. This can help the clinician avoid unnecessary or potentially harmful tests or therapies. In addition, this type of automated analysis of patient data can help medical professionals make clinical decisions much faster and with more confidence. As such, the speed and quality of healthcare would be improved with reduced costs. One popular automated use of clinical reports is predicting the existence or absence of certain conditions in a given report. This type of analysis, called text classification in general, can learn the characteristics of such conditions from a previously labeled dataset of clinical reports. 
In this research, novel techniques for better performance of automated classification of clinical reports are developed and compared with conventional approaches. As a first step, classifiers using the raw text of the reports with standard preprocessing techniques are implemented. Additionally, biomedical NLP tools are used to extract the relevant information from the reports in a more consistent way. These extracted features are classified using conventional classifiers, including decision trees and support vector machines (SVM). While results show that the classification performance is significantly improved by using the NLP features over using the raw text, this NLP-based classification is computationally expensive and requires a significant number of manual steps to be used effectively across many different clinical areas. As an alternative, a framework for topic modeling-based classification system is built. Topic modeling techniques automatically find the interpretable themes that exist in a document collection. These topics are used to represent each report and different classifiers are built based on this representation. This system has the advantage of being more adaptable to different clinical domains than custom NLP-based classifiers. It also provides dimension reduction because there are fewer potential topic categories than the number of words in a vocabulary. The performance of topic modeling-based classifiers is better than classification using raw text and it is competitive with classification using NLP features. In addition, they provide a compact and interpretable representation. 
Results from this dissertation research have significant impacts on the quality and efficiency of healthcare. First of all, the classifiers built in this research can be used to automatically predict the conditions in a clinical report. They can replace the manual review of clinical reports, which can be time consuming and error-prone. In addition, with the increased accuracy and interpretability they provide, clinicians can have more confidence in utilizing such systems in real life settings.

Effective classification of clinical reports: natural language processing-based and topic modeling-based approaches

The recent emphasis on health information technology has highlighted the importance of leveraging the large amount of electronic clinical data to help guide medical decision-making. Developing such clinical decision aids requires manual review of many past patient reports in order to generate a good predictive model. In this research, we investigate classification of clinical reports using natural language processing (NLP). The proposed system uses NLP to generate structured output from computed tomography (CT) reports and then machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Our results show that NLP improves upon raw text classification results.

Efficient Classification of Clinical Reports Utilizing Natural Language Processing

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