| 1. |
- Weegar, Rebecka, 1982-
(författare)
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Mining Clinical Text in Cancer Care
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Health care and clinical practice generate large amounts of text detailing symptoms, test results, diagnoses, treatments, and outcomes for patients. This clinical text, documented in health records, is a potential source of knowledge and an underused resource for improved health care. The focus of this work has been text mining of clinical text in the domain of cancer care, with the aim to develop and evaluate methods for extracting relevant information from such texts. Two different types of clinical documentation have been included: clinical notes from electronic health records in Swedish and Norwegian pathology reports.Free text, and clinical text in particular, is considered as a kind of unstructured information, which is difficult to process automatically. Therefore, information extraction can be applied to create a more structured representation of a text, making its content more accessible for machine learning and statistics. To this end, this thesis describes the development of an efficient and accurate tool for information extraction for pathology reports.Another application for clinical text mining is risk prediction and diagnosis prediction. The goal for such prediction is to create a machine learning model capable of identifying patients at risk of a specific disease or some other adverse outcome. The motivation for cancer diagnosis prediction is that an early diagnosis can be beneficial for the outcome of treatment. Here, a disease prediction model was developed and evaluated for prediction of cervical cancer. To create this model, health records of patients diagnosed with cervical cancer were processed in two steps. First, clinical events were extracted from free text clinical notes through the use of named entity recognition. The extracted events were next combined with other event types, such as diagnosis codes and drug codes from the same health records. Finally, machine learning models were trained for predicting cervical cancer, and evaluation showed that events extracted from the free text records were the most informative event type for the diagnosis prediction.
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| 2. |
- Henriksson, Aron, 1985-
(författare)
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Semantic Spaces of Clinical Text : Leveraging Distributional Semantics for Natural Language Processing of Electronic Health Records
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The large amounts of clinical data generated by electronic health record systems are an underutilized resource, which, if tapped, has enormous potential to improve health care. Since the majority of this data is in the form of unstructured text, which is challenging to analyze computationally, there is a need for sophisticated clinical language processing methods. Unsupervised methods that exploit statistical properties of the data are particularly valuable due to the limited availability of annotated corpora in the clinical domain.Information extraction and natural language processing systems need to incorporate some knowledge of semantics. One approach exploits the distributional properties of language – more specifically, term co-occurrence information – to model the relative meaning of terms in high-dimensional vector space. Such methods have been used with success in a number of general language processing tasks; however, their application in the clinical domain has previously only been explored to a limited extent. By applying models of distributional semantics to clinical text, semantic spaces can be constructed in a completely unsupervised fashion. Semantic spaces of clinical text can then be utilized in a number of medically relevant applications.The application of distributional semantics in the clinical domain is here demonstrated in three use cases: (1) synonym extraction of medical terms, (2) assignment of diagnosis codes and (3) identification of adverse drug reactions. To apply distributional semantics effectively to a wide range of both general and, in particular, clinical language processing tasks, certain limitations or challenges need to be addressed, such as how to model the meaning of multiword terms and account for the function of negation: a simple means of incorporating paraphrasing and negation in a distributional semantic framework is here proposed and evaluated. The notion of ensembles of semantic spaces is also introduced; these are shown to outperform the use of a single semantic space on the synonym extraction task. This idea allows different models of distributional semantics, with different parameter configurations and induced from different corpora, to be combined. This is not least important in the clinical domain, as it allows potentially limited amounts of clinical data to be supplemented with data from other, more readily available sources. The importance of configuring the dimensionality of semantic spaces, particularly when – as is typically the case in the clinical domain – the vocabulary grows large, is also demonstrated.
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| 3. |
- Skeppstedt, Maria, 1977-
(författare)
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Extracting Clinical Findings from Swedish Health Record Text
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Information contained in the free text of health records is useful for the immediate care of patients as well as for medical knowledge creation. Advances in clinical language processing have made it possible to automatically extract this information, but most research has, until recently, been conducted on clinical text written in English. In this thesis, however, information extraction from Swedish clinical corpora is explored, particularly focusing on the extraction of clinical findings. Unlike most previous studies, Clinical Finding was divided into the two more granular sub-categories Finding (symptom/result of a medical examination) and Disorder (condition with an underlying pathological process). For detecting clinical findings mentioned in Swedish health record text, a machine learning model, trained on a corpus of manually annotated text, achieved results in line with the obtained inter-annotator agreement figures. The machine learning approach clearly outperformed an approach based on vocabulary mapping, showing that Swedish medical vocabularies are not extensive enough for the purpose of high-quality information extraction from clinical text. A rule and cue vocabulary-based approach was, however, successful for negation and uncertainty classification of detected clinical findings. Methods for facilitating expansion of medical vocabulary resources are particularly important for Swedish and other languages with less extensive vocabulary resources. The possibility of using distributional semantics, in the form of Random indexing, for semi-automatic vocabulary expansion of medical vocabularies was, therefore, evaluated. Distributional semantics does not require that terms or abbreviations are explicitly defined in the text, and it is, thereby, a method suitable for clinical corpora. Random indexing was shown useful for extending vocabularies with medical terms, as well as for extracting medical synonyms and abbreviation dictionaries.
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| 4. |
- Skeppstedt, Maria, 1977-
(författare)
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From Disorder to Order : Extracting clinical findings from unstructured text
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Medical disorders and findings are examples of important information in health record text. Through developing methods for automatically extracting these entities from the health record text, the possibility of making use of the information by automatic computerised processes increases. That a disorder or finding is mentioned in the health record, however, does not necessarily imply that it has been observed in the patient, because disorders that are ruled out and findings that are not observed in the patient are also mentioned.This licentiate thesis investigates the possibility of automatically extracting disorders and findings from Swedish health record text and the possibility of automatically determining whether these findings and disorders are negated or not.A rule- and terminology-based system that uses several Swedish medical terminologies, including SNOMED~CT and ICD-10 for extracting disorders, findings and body structures mentioned in Swedish clinical text was constructed and evaluated. Moreover, an English rule-based system for negation detection, NegEx, was adapted to Swedish and evaluated on clinical text written in Swedish.The evaluation showed that disorders and findings were recognised with low recall, whereas body structures were recognised with comparatively good results. The negation detection system that was adapted to Swedish achieved the same recall as the English system, but lower precision.The evaluated systems are accurate enough to be useful in some applications, but need to be further developed, especially when it comes to recognising disorders and findings.
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| 5. |
- Velupillai, Sumithra, 1978-
(författare)
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Shades of Certainty : Annotation and Classification of Swedish Medical Records
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Access to information is fundamental in health care. This thesis presents research on Swedish medical records with the overall goal of building intelligent information access tools that can aid health personnel, researchers and other professions in their daily work, and, ultimately, improve health care in general. The issue of ethics and identifiable information is addressed by creating an annotated gold standard corpus and porting an existing de-identification system to Swedish from English. The aim is to move towards making textual resources available to researchers without risking exposure of patients’ confidential information. Results for the rule-based system are not encouraging, but results for the gold standard are fairly high. Affirmed, uncertain and negated information needs to be distinguished when building accurate information extraction tools. Annotation models are created, with the aim of building automated systems. One model distinguishes certain and uncertain sentences, and is applied on medical records from several clinical departments. In a second model, two polarities and three levels of certainty are applied on diagnostic statements from an emergency department. Overall results are promising. Differences are seen depending on clinical practice, annotation task and level of domain expertise among the annotators. Using annotated resources for automatic classification is studied. Encouraging overall results using local context information are obtained. The fine-grained certainty levels are used for building classifiers for real-world e-health scenarios. This thesis contributes two annotation models of certainty and one of identifiable information, applied on Swedish medical records. A deeper understanding of the language use linked to conveying certainty levels is gained. Three annotated resources that can be used for further research have been created, and implications for automated systems are presented.
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| 6. |
- Andrenucci, Andrea, 1971-
(författare)
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Using Language Technology to Mediate Medical Information on Health Portals : User Studies and Experiments
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The World Wide Web has revolutionized our lifestyle, our economies and services within health care. Health care services are no longer provided only at specialist centers and at scheduled hours, but also through online tools that give health care consumers access to medical information, health records, medical counselling and peer support. Such tools and applications are generally available on larger web sites or gateways called health portals. A large majority of online medical information consumers are laypeople (i.e. non experts) who appreciate the possibility to submit their information needs in their own native language. The information retrieval process where information requests from users and retrieved documents/answers are in different languages is called cross-language information retrieval (CLIR). Mental health is one of the medical areas where some online applications have been successfully deployed in order to help people by providing in-depth medical information, counseling and advice. Despite the fact that online health portals are considered priority e-health tools for improving mental health, there are no formal knowledge instruments such as knowledge patterns that explicitly support the development of online health portals in the field of psychology/psychotherapy. The goal of this research is to produce and evaluate a set of knowledge patterns, for the development and implementation of cross-lingual online health portals aimed at information seekers without medical expertise in the domain of psychology and psychotherapy. The knowledge patterns synthetize results of three research foundations: 1) User studies of portal interaction, based on interviews and observations about how users experience health information online and personalized search 2) Knowledge integration of existing language technology approaches, and 3) Experiments with language technology applications, in the field of cross-lingual information retrieval/question-answering. The target groups of this research are developers, researchers and health care providers, i.e. people who are responsible for mediating medical information on online health portals for users without medical expertise. The chosen research framework is design science, i.e. the science that focuses on the study, development and evaluation of artefacts (objects that help people solve a practical problem). Typical examples of artefacts in IT are algorithms, software solutions and databases, but also objects such as processes or knowledge patterns. The developed and evaluated artefact in this research is a set of knowledge patterns for online health portal development. The developed artefact contains fourteen knowledge patterns covering the three research foundations. Formative (structured workshops) and summative (online survey) evaluation of the artefact indicate that the knowledge patterns are useful, relevant and adoptable to a large extent, they also provide further directions for development of online mental health portals. Developing portals with multilingual support and tailored interfaces has the potential of helping larger groups of citizens to access relevant medical information.
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| 7. |
- Henriksson, Aron, 1985-
(författare)
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Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i.e., human annotations are not required. The ability to obtain inexpensive word representations in this manner helps to alleviate the bottleneck of fully supervised approaches to natural language processing, especially since models of distributional semantics are data-driven and hence agnostic to both language and domain.All that is required to obtain distributed word representations is a sizeable corpus; however, the composition of the semantic space is not only affected by the underlying data but also by certain model hyperparameters. While these can be optimized for a specific downstream task, there are currently limitations to the extent the many aspects of semantics can be captured in a single model. This dissertation investigates the possibility of capturing multiple aspects of lexical semantics by adopting the ensemble methodology within a distributional semantic framework to create ensembles of semantic spaces. To that end, various strategies for creating the constituent semantic spaces, as well as for combining them, are explored in a number of studies.The notion of semantic space ensembles is generalizable across languages and domains; however, the use of unsupervised methods is particularly valuable in low-resource settings, in particular when annotated corpora are scarce, as in the domain of Swedish healthcare. The semantic space ensembles are here empirically evaluated for tasks that have promising applications in healthcare. It is shown that semantic space ensembles – created by exploiting various corpora and data types, as well as by adjusting model hyperparameters such as the size of the context window and the strategy for handling word order within the context window – are able to outperform the use of any single constituent model on a range of tasks. The semantic space ensembles are used both directly for k-nearest neighbors retrieval and for semi-supervised machine learning. Applying semantic space ensembles to important medical problems facilitates the secondary use of healthcare data, which, despite its abundance and transformative potential, is grossly underutilized.
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