Distributional Semantic Models for Clinical Text Applied to Health Record Summarization

As information systems in the health sector are becoming increasingly computerized, large amounts of care-related information are being stored electronically. In hospitals clinicians continuously document treatment and care given to patients in electronic health record (EHR) systems. Much of the information being documented is in the form of clinical notes, or narratives, containing primarily unstructured free-text information. For each care episode, clinical notes are written on a regular basis, ending with a discharge summary that basically summarizes the care episode. Although EHR systems are helpful for storing and managing such information, there is an unrealized potential in utilizing this information for smarter care assistance, as well as for secondary purposes such as research and education. Advances in clinical language processing are enabling computers to assist clinicians in their interaction with the free-text information documented in EHR systems. This includes assisting in tasks like query-based search, terminology development, knowledge extraction, translation, and summarization. This thesis explores various computerized approaches and methods aimed at enabling automated semantic textual similarity assessment and information extraction based on the free-text information in EHR systems. The focus is placed on the task of (semi-)automated summarization of the clinical notes written during individual care episodes. The overall theme of the presented work is to utilize resource-light approaches and methods, circumventing the need to manually develop knowledge resources or training data. Thus, to enable computational semantic textual similarity assessment, word distribution statistics are derived from large training corpora of clinical free text and stored as vector-based representations referred to as distributional semantic models. Also resource-light methods are explored in the task of performing automatic summarization of clinical freetext information, relying on semantic textual similarity assessment. Novel and experimental methods are presented and evaluated that focus on: a) distributional semantic models trained in an unsupervised manner from statistical information derived from large unannotated clinical free-text corpora; b) representing and computing semantic similarities between linguistic items of different granularity, primarily words, sentences and clinical notes; and c) summarizing clinical free-text information from individual care episodes. Results are evaluated against gold standards that reflect human judgements. The results indicate that the use of distributional semantics is promising as a resource-light approach to automated capturing of semantic textual similarity relations from unannotated clinical text corpora. Here it is important that the semantics correlate with the clinical terminology, and with various semantic similarity assessment tasks. Improvements over classical approaches are achieved when the underlying vector-based representations allow for a broader range of semantic features to be captured and represented. These are either distributed over multiple semantic models trained with different features and training corpora, or use models that store multiple sense-vectors per word. Further, the use of structured meta-level information accompanying care episodes is explored as training features for distributional semantic models, with the aim of capturing semantic relations suitable for care episode-level information retrieval. Results indicate that such models performs well in clinical information retrieval. It is shown that a method called Random Indexing can be modified to construct distributional semantic models that capture multiple sense-vectors for each word in the training corpus. This is done in a way that retains the original training properties of the Random Indexing method, by being incremental, scalable and distributional. Distributional semantic models trained with a framework called Word2vec, which relies on the use of neural networks, outperform those trained using the classic Random Indexing method in several semantic similarity assessment tasks, when training is done using comparable parameters and the same training corpora. Finally, several statistical features in clinical text are explored in terms of their ability to indicate sentence significance in a text summary generated from the clinical notes. This includes the use of distributional semantics to enable case-based similarity assessment, where cases are other care episodes and their “solutions”, i.e., discharge summaries. A type of manual evaluation is performed, where human experts rates the different aspects of the summaries using a evaluation scheme/tool. In addition, the original clinician-written discharge summaries are explored as gold standard for the purpose of automated evaluation. Evaluation shows a high correlation between manual and automated evaluation, suggesting that such a gold standard can function as a proxy for human evaluations. --- This thesis has been published jointly with Norwegian University of Science and Technology, Norway and University of Turku, Finland.This thesis has beenpublished jointly with Norwegian University of Science and Technology, Norway.Siirretty Doriast

Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan

Automated summarization of clinical texts can reduce the burden of medical professionals. "Discharge summaries" are one promising application of the summarization, because they can be generated from daily inpatient records. Our preliminary experiment suggests that 20-31% of the descriptions in discharge summaries overlap with the content of the inpatient records. However, it remains unclear how the summaries should be generated from the unstructured source. To decompose the physician's summarization process, this study aimed to identify the optimal granularity in summarization. We first defined three types of summarization units with different granularities to compare the performance of the discharge summary generation: whole sentences, clinical segments, and clauses. We defined clinical segments in this study, aiming to express the smallest medically meaningful concepts. To obtain the clinical segments, it was necessary to automatically split the texts in the first stage of the pipeline. Accordingly, we compared rule-based methods and a machine learning method, and the latter outperformed the formers with an F1 score of 0.846 in the splitting task. Next, we experimentally measured the accuracy of extractive summarization using the three types of units, based on the ROUGE-1 metric, on a multi-institutional national archive of health records in Japan. The measured accuracies of extractive summarization using whole sentences, clinical segments, and clauses were 31.91, 36.15, and 25.18, respectively. We found that the clinical segments yielded higher accuracy than sentences and clauses. This result indicates that summarization of inpatient records demands finer granularity than sentence-oriented processing. Although we used only Japanese health records, it can be interpreted as follows: physicians extract "concepts of medical significance" from patient records and recombine them ..

Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data

The increasing adoption of electronic health records (EHRs) has led to an unprecedented amount of patient health information stored in an electronic format. The ability to comb through this information is imperative, both for patient care and computational modeling. Creating a system to minimize unnecessary EHR data, automatically distill longitudinal patient information, and highlight salient parts of a patient’s record is currently an unmet need. However, summarization of EHR data is not a trivial task, as there exist many challenges with reasoning over this data. EHR data elements are most often obtained at irregular intervals as patients are more likely to receive medical care when they are ill, than when they are healthy. The presence of narrative documentation adds another layer of complexity as the notes are riddled with over-sampled text, often caused by the frequent copy-and-pasting during the documentation process. This dissertation synthesizes a set of challenges for automated EHR summarization identified in the literature and presents an array of methods for dealing with some of these challenges. We used hybrid data-driven and knowledge-based approaches to examine abundant redundancy in clinical narrative text, a data-driven approach to identify and mitigate biases in laboratory testing patterns with implications for using clinical data for research, and a probabilistic modeling approach to automatically summarize patient records and learn computational models of disease with heterogeneous data types. The dissertation also demonstrates two applications of the developed methods to important clinical questions: the questions of laboratory test overutilization and cohort selection from EHR data

Extractive Summarization : Experimental work on nursing notes in Finnish

Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that is concerned with how a computer machine interacts with human language. With the increasing computational power and the advancement in technologies, researchers have been successful at proposing various NLP tasks that have already been implemented as real-world applications today. Automated text summarization is one of the many tasks that has not yet completely matured particularly in health sector. A success in this task would enable healthcare professionals to grasp patient's history in a minimal time resulting in faster decisions required for better care. Automatic text summarization is a process that helps shortening a large text without sacrificing important information. This could be achieved by paraphrasing the content known as the abstractive method or by concatenating relevant extracted sentences namely the extractive method. In general, this process requires the conversion of text into numerical form and then a method is executed to identify and extract relevant text. This thesis is an attempt of exploring NLP techniques used in extractive text summarization particularly in health domain. The work includes a comparison of basic summarizing models implemented on a corpus of patient notes written by nurses in Finnish language. Concepts and research studies required to understand the implementation have been documented along with the description of the code. A python-based project is structured to build a corpus and execute multiple summarizing models. For this thesis, we observe the performance of two textual embeddings namely Term Frequency - Inverse Document Frequency (TF-IDF) which is based on simple statistical measure and Word2Vec which is based on neural networks. For both models, LexRank, an unsupervised stochastic graph-based sentence scoring algorithm, is used for sentence extraction and a random selection method is used as a baseline method for evaluation. To evaluate and compare the performance of models, summaries of 15 patient care episodes of each model were provided to two human beings for manual evaluations. According to the results of the small sample dataset, we observe that both evaluators seem to agree with each other in preferring summaries produced by Word2Vec LexRank over the summaries generated by TF-IDF LexRank. Both models have also been observed, by both evaluators, to perform better than the baseline model of random selection

Methods and Applications for Summarising Free-Text Narratives in Electronic Health Records

As medical services move towards electronic health record (EHR) systems the breadth and depth of data stored at each patient encounter has increased. This growing wealth of data and investment in care systems has arguably put greater strain on services, as those at the forefront are pushed towards greater time spent in front of computers over their patients. To minimise the use of EHR systems clinicians often revert to using free-text data entry to circumvent the structured input fields. It has been estimated that approximately 80% of EHR data is within the free-text portion. Outside of their primary use, that is facilitating the direct care of the patient, secondary use of EHR data includes clinical research, clinical audits, service improvement research, population health analysis, disease and patient phenotyping, clinical trial recruitment to name but a few.This thesis presents a number of projects, previously published and original work in the development, assessment and application of summarisation methods for EHR free-text. Firstly, I introduce, define and motivate EHR free-text analysis and summarisation methods of open-domain text and how this compares to EHR free-text. I then introduce a subproblem in natural language processing (NLP) that is the recognition of named entities and linking of the entities to pre-existing clinical knowledge bases (NER+L). This leads to the first novel contribution the Medical Concept Annotation Toolkit (MedCAT) that provides a software library workflow for clinical NER+L problems. I frame the outputs of MedCAT as a form of summarisation by showing the tools contributing to published clinical research and the application of this to another clinical summarisation use-case ‘clinical coding’. I then consider methods for the textual summarisation of portions of clinical free-text. I show how redundancy in clinical text is empirically different to open-domain text discussing how this impacts text-to-text summarisation. I then compare methods to generate discharge summary sections from previous clinical notes using methods presented in prior chapters via a novel ‘guidance’ approach.I close the thesis by discussing my contributions in the context of state-of-the-art and how my work fits into the wider body of clinical NLP research. I briefly describe the challenges encountered throughout, offer my perspectives on the key enablers of clinical informatics research, and finally the potential future work that will go towards translating research impact to real-world benefits to healthcare systems, workers and patients alike

Adverse Drug Event Detection, Causality Inference, Patient Communication and Translational Research

Adverse drug events (ADEs) are injuries resulting from a medical intervention related to a drug. ADEs are responsible for nearly 20% of all the adverse events that occur in hospitalized patients. ADEs have been shown to increase the cost of health care and the length of stays in hospital. Therefore, detecting and preventing ADEs for pharmacovigilance is an important task that can improve the quality of health care and reduce the cost in a hospital setting. In this dissertation, we focus on the development of ADEtector, a system that identifies ADEs and medication information from electronic medical records and the FDA Adverse Event Reporting System reports. The ADEtector system employs novel natural language processing approaches for ADE detection and provides a user interface to display ADE information. The ADEtector employs machine learning techniques to automatically processes the narrative text and identify the adverse event (AE) and medication entities that appear in that narrative text. The system will analyze the entities recognized to infer the causal relation that exists between AEs and medications by automating the elements of Naranjo score using knowledge and rule based approaches. The Naranjo Adverse Drug Reaction Probability Scale is a validated tool for finding the causality of a drug induced adverse event or ADE. The scale calculates the likelihood of an adverse event related to drugs based on a list of weighted questions. The ADEtector also presents the user with evidence for ADEs by extracting figures that contain ADE related information from biomedical literature. A brief summary is generated for each of the figures that are extracted to help users better comprehend the figure. This will further enhance the user experience in understanding the ADE information better. The ADEtector also helps patients better understand the narrative text by recognizing complex medical jargon and abbreviations that appear in the text and providing definitions and explanations for them from external knowledge resources. This system could help clinicians and researchers in discovering novel ADEs and drug relations and also hypothesize new research questions within the ADE domain

Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings

The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers

Automatic Discharge Summary Generation using Neural Network Models

東京都立大学Tokyo Metropolitan University博士（情報科学）doctoral thesi