Human-Centered Design (HCD) of Personal Decision Support System (PDSS) for understandable individual health management, using Natural Language Processing (NLP) and machine learning

This project addressed the challenges of patients to comprehend typical information (e.g. in the form of ''doctor letters'' or ''patient journals'') about their health condition, and to get understandable and personalized recommendations in a user friendly way to improve their health and well-being, considering their individual and actual health status. The chosen approach has been to explore the possibilities of natural language processing technologies and use of machine learning (particularly random forests), integrated in a Human-Centered Design of a personal decision support system. The project followed a combination of Design Science Research Methodology and Human-Centered Design Methodology. For the initial user needs assessment, 12 interviews with potential users of the target application were carried out, representing varying degrees of experience with the Norwegian health care system. The interviews showed a large gap in the comprehension of the information contained in personal medical journals, and resulted in the problem specification, needs assessment, and a PACT analysis. Further a series of requirements were produced with the use of Volere shells. The project was divided into three main parts, Natural Language Processing (for the data extraction and content summarization), personal health suggestions (with use of machine learning and feature importance determination), and front end design (for an integrated target application). The language processing was largely based on pre-trained transformer based models tuned for downstream tasks. Several models trained with biomedical and clinical text were evaluated on automated summarization, semantic search and assertion detection, and showed promising results. Areas of improvement and future work were summarization of short texts and formal evaluation of medical term explanations. The front end design went trough three iterations, a low fidelity prototype in the for of a paper prototype, and two versions of the high fidelity prototype. The user testing showed an improvement in understanding of medical data in both high fidelity prototypes. The health suggestion were based on the feature importance determination of random forests. Three different determination methods were tested, finding minor variations in results, but with Gini Importance gaining a major advantage in computational speed. The recommendation produced could not be tied to clinical results but would require further study to prove or disprove the effectiveness of the recommendations

Human-Centered Design (HCD) of Personal Decision Support System (PDSS) for understandable individual health management, using Natural Language Processing (NLP) and machine learning

This project addressed the challenges of patients to comprehend typical information (e.g. in the form of ''doctor letters'' or ''patient journals'') about their health condition, and to get understandable and personalized recommendations in a user friendly way to improve their health and well-being, considering their individual and actual health status. The chosen approach has been to explore the possibilities of natural language processing technologies and use of machine learning (particularly random forests), integrated in a Human-Centered Design of a personal decision support system. The project followed a combination of Design Science Research Methodology and Human-Centered Design Methodology. For the initial user needs assessment, 12 interviews with potential users of the target application were carried out, representing varying degrees of experience with the Norwegian health care system. The interviews showed a large gap in the comprehension of the information contained in personal medical journals, and resulted in the problem specification, needs assessment, and a PACT analysis. Further a series of requirements were produced with the use of Volere shells. The project was divided into three main parts, Natural Language Processing (for the data extraction and content summarization), personal health suggestions (with use of machine learning and feature importance determination), and front end design (for an integrated target application). The language processing was largely based on pre-trained transformer based models tuned for downstream tasks. Several models trained with biomedical and clinical text were evaluated on automated summarization, semantic search and assertion detection, and showed promising results. Areas of improvement and future work were summarization of short texts and formal evaluation of medical term explanations. The front end design went trough three iterations, a low fidelity prototype in the for of a paper prototype, and two versions of the high fidelity prototype. The user testing showed an improvement in understanding of medical data in both high fidelity prototypes. The health suggestion were based on the feature importance determination of random forests. Three different determination methods were tested, finding minor variations in results, but with Gini Importance gaining a major advantage in computational speed. The recommendation produced could not be tied to clinical results but would require further study to prove or disprove the effectiveness of the recommendations

Dying and death in the electronic patient record. A qualitative analysis of textual practices

All rights reserved. No part of this publication may be reproduced or transmitted, in any form or by any means, without permission.In Norway, over one in four deaths occur in hospitals, places that operate primarily according to curative logic. One aim of the Norwegian health care system is that patients, at the end of life, should receive high-quality palliative care as defined by the World Health Organization, independent of whether they are dying at home or in a nursing home, hospice or hospital. Against this potentially challenging background, this project investigates the role of the texts about such patients written into the electronic patient record (EPR). Starting from the view of (EPR) texts as active contributors to the whereabouts of (dying) patients, the EPR can be seen as an essential communication and coordination tool contributing to the types of knowledge that circulate about the dying patient and their treatment. This thesis aims to provide insights into how and what knowledge the EPR proposes as relevant by asking: What kind of textual practices of dying and death in medical wards are present in the EPR, and what do these practices achieve? The selected methods were a qualitative document analysis combining elements from the fields of linguistics, literary criticism and science and technology studies. This effort resulted in three articles elaborating different aspects of how dying and death are documented in the EPR. The first article investigated the negotiations of the transition from curative to palliative care. It argued that the text often changes from being technical and conclusive to being uncertain and open to negotiations as a need to align the involved parties in the decision. The second article explored which aspects of dying the text is attuned to in patients’ last 24 hours of life. It argued that the text has three hegemonic modes of ordering – numbering, timing, and classifying – which perform a dominant narrative in which dying is concealed. Yet, in between, there are traces of caring attention to and compassion for the dying patient. The third article considered how the moment of death is documented. It argued for what seem to be established ways of recording this moment as being manageable and portraying it as uneventful or good. This exploration of textual practices suggests that, first, the EPR treats dying and death as observations and tasks to be solved in general biomedical terms, rather than probing the needs of the individual patient. Second, the EPR seems to avoid difficult topics, deliberations, and disagreements, and it never admits to failure. Finally, the EPR sometimes shows professionals’ attempts to reveal individual patients’ needs and suffering and the troubles of dying in a curative context. Paper I: Hov, L., Synnes, O., & Aarseth, G. (2020). Negotiating the turning point in the transition from curative to palliative treatment: A linguistic analysis of medical records of dying patients. BMC Palliative Care, 19 (1), 1–13. https://doi.org/10.1186/s12904-020-00602-4 Paper II: Hov, L., Pasveer, B., & Synnes, O. (2020). Modes of dying in the electronic patient record, Mortality, https://doi.org/10.1080/13576275.2020.1865294 Paper III: Hov, L., Tveit, B., & Synnes O. (2021, May). Nobody dies alone in the electronic patient record – A qualitative analysis of the textual practices of documenting dying and death. OMEGA-Journal of Death and Dying. https://doi.org/10.1177/00302228211019197publishedVersio

A modular, open-source information extraction framework for identifying clinical concepts and processes of care in clinical narratives

In this thesis, a synthesis is presented of the knowledge models required by clinical informa- tion systems that provide decision support for longitudinal processes of care. Qualitative research techniques and thematic analysis are novelly applied to a systematic review of the literature on the challenges in implementing such systems, leading to the development of an original conceptual framework. The thesis demonstrates how these process-oriented systems make use of a knowledge base derived from workflow models and clinical guidelines, and argues that one of the major barriers to implementation is the need to extract explicit and implicit information from diverse resources in order to construct the knowledge base. Moreover, concepts in both the knowledge base and in the electronic health record (EHR) must be mapped to a common ontological model. However, the majority of clinical guideline information remains in text form, and much of the useful clinical information residing in the EHR resides in the free text fields of progress notes and laboratory reports. In this thesis, it is shown how natural language processing and information extraction techniques provide a means to identify and formalise the knowledge components required by the knowledge base. Original contributions are made in the development of lexico-syntactic patterns and the use of external domain knowledge resources to tackle a variety of information extraction tasks in the clinical domain, such as recognition of clinical concepts, events, temporal relations, term disambiguation and abbreviation expansion. Methods are developed for adapting existing tools and resources in the biomedical domain to the processing of clinical texts, and approaches to improving the scalability of these tools are proposed and evalu- ated. These tools and techniques are then combined in the creation of a novel approach to identifying processes of care in the clinical narrative. It is demonstrated that resolution of coreferential and anaphoric relations as narratively and temporally ordered chains provides a means to extract linked narrative events and processes of care from clinical notes. Coreference performance in discharge summaries and progress notes is largely dependent on correct identification of protagonist chains (patient, clinician, family relation), pronominal resolution, and string matching that takes account of experiencer, temporal, spatial, and anatomical context; whereas for laboratory reports additional, external domain knowledge is required. The types of external knowledge and their effects on system performance are identified and evaluated. Results are compared against existing systems for solving these tasks and are found to improve on them, or to approach the performance of recently reported, state-of-the- art systems. Software artefacts developed in this research have been made available as open-source components within the General Architecture for Text Engineering framework

Knowledge representation and text mining in biomedical, healthcare, and political domains

Knowledge representation and text mining can be employed to discover new knowledge and develop services by using the massive amounts of text gathered by modern information systems. The applied methods should take into account the domain-specific nature of knowledge. This thesis explores knowledge representation and text mining in three application domains. Biomolecular events can be described very precisely and concisely with appropriate representation schemes. Protein–protein interactions are commonly modelled in biological databases as binary relationships, whereas the complex relationships used in text mining are rich in information. The experimental results of this thesis show that complex relationships can be reduced to binary relationships and that it is possible to reconstruct complex relationships from mixtures of linguistically similar relationships. This encourages the extraction of complex relationships from the scientific literature even if binary relationships are required by the application at hand. The experimental results on cross-validation schemes for pair-input data help to understand how existing knowledge regarding dependent instances (such those concerning protein–protein pairs) can be leveraged to improve the generalisation performance estimates of learned models. Healthcare documents and news articles contain knowledge that is more difficult to model than biomolecular events and tend to have larger vocabularies than biomedical scientific articles. This thesis describes an ontology that models patient education documents and their content in order to improve the availability and quality of such documents. The experimental results of this thesis also show that the Recall-Oriented Understudy for Gisting Evaluation measures are a viable option for the automatic evaluation of textual patient record summarisation methods and that the area under the receiver operating characteristic curve can be used in a large-scale sentiment analysis. The sentiment analysis of Reuters news corpora suggests that the Western mainstream media portrays China negatively in politics-related articles but not in general, which provides new evidence to consider in the debate over the image of China in the Western media

Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone