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Clinical Text Mining: Secondary Use of Electronic Patient Records
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2018 (English)Book (Other academic)
Abstract [en]

Patient records are written by the physician during the treatment of the patient for mnemonic reasons and internal use within the clinical unit, but the patient record is also written for legal reasons. Today a very large number of patient records are produced in the healthcare system. The patient records are mostly in electronic form and are written by health personnel. They describe initial symptoms, diagnosis, treatment and outcomes of the treatment, but they may also contain nursing narratives or daily notes. In addition, patient records contain valuable structured information such as laboratory results, blood tests and drugs. These records are seldom reused, most likely because of ignorance, but also due to a lack of tools to process them adequately, and last but not least, there are ethical policies that make the records difficult to use for research and for developing tools for physicians and researchers. There is a plethora of reasons to unlock and reuse the content of electronic patient records, since they contain valuable information about a vast number of patients who have been treated by highly skilled physicians and taken care of by well- trained and experienced nurses. Over time a massive amount of patient record data is accumulated where old knowledge can be confirmed and new knowledge can be obtained. This book was written since there was a lack of a textbook describing the area of clinical text mining. The healthcare domain area is complex and can be difficult to apprehend. There are plenty of specialised disciplines in healthcare. Applying text mining and natural language processing to health records needs special care and understanding of the domain. This book will help the reader to quickly and easily understand the health care domain. Some issues that are treated in this book are: What are the problems in clinical text mining and what are their solutions? Which are the coding and classification systems in the health care domain? What do they actually contain and how are they used? How do physicians reason to make vii viii Preface a diagnosis? What is their typical jargon when writing in the patient record? Does jargon differ between different medical specialities? This book will give the reader the background knowledge on the research front on clinical text mining and health informatics, and specifically in healthcare analytics. It is valuable for a researcher or a student who needs to learn the clinical research area in a fast and efficient way. A book is also a valuable resource for targeting a new natural language in the domain. Each additional language will add a piece to the whole equation. The experiences described in this book originate mainly from research that utilised over two million Swedish hospital records from the Karolinska University Hospital during the years 2007–2014. The general aim was to build basic tools for clinical text mining for Swedish patient records and to address specific issues. These tools were used to automatically: • detect and predict healthcare associated infections; • find adverse (drug) events; and • detect early symptoms of cancer. To accomplish this, the text in the patient records was manually annotated by physicians and then different machine learning tools were trained on these annotated texts to simulate the physicians’ skills, knowledge and intelligence. The book is also based on the extensive source of scientific literature from the large research community in clinical text mining that has been compiled and explained in this book. This book will also describe how to get access to patient records, the ethical problems involved and how to de-identify the patient records automatically before using the records, and finally, methods to build tools that will improve healthcare. The research question of this 10-year research project are many fold, and started with the general research question(s): • Using artificial intelligence to analyse patient records: Is it possible and will it improve healthcare? This actually can be distilled to several research questions of which one is of special interest: • Can one process clinical text written in Swedish with natural language processing tools developed for standard Swedish such as news paper and web texts to extract named entities such as symptoms, diagnosis, drugs and body parts from clinical text? This major issue can then be subdivided into the following questions: • Can one decide the factuality of a diagnosis found in a clinical text? What does Pneumonia? or Angina pectoris cannot be excluded or just No signs of pneumonia? really mean? • Can one determine of the temporal order of clinical events? Have the symptoms occurred a week ago or two years ago? Preface ix • Can new adverse drug effects be found by extracting relations between drug intake and adverse drug effect? • How much clinical text must be annotated manually to obtain correct and useful results? • How can patient privacy be maintained while carrying out research in clinical text mining?

Place, publisher, year, edition, pages
Springer, 2018. , p. 181
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-178739DOI: 10.1007/978-3-319-78503-5ISBN: 978-3-319-78502-8 (print)ISBN: 978-3-319-78503-5 (electronic)OAI: oai:DiVA.org:su-178739DiVA, id: diva2:1391117
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-03Bibliographically approved

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