Automated methods to extract patient new information from clinical notes in electronic health record systems

University of Minnesota Ph.D. dissertation. November 2013. Major: Health Informatics. Advisor: Serguei Pakhomov. 1 computer file (PDF); xii, 102 pages.The widespread adoption of Electronic Health Record (EHR) has resulted in rapid text proliferation within clinical care. Clinicians' use of copying and pasting functions in EHR systems further compounds this by creating a large amount of redundant clinical information in clinical documents. A mixture of redundant information (especially outdated and incorrect information) and new information in a single clinical note increases clinicians' cognitive burden and results in decision-making difficulties. Moreover, replicated erroneous information can potentially cause risks to patient safety. However, automated methods to identify redundant or relevant new information in clinical texts have not been extensively investigated. The overarching goal of this research is to develop and evaluate automated methods to identify new and clinically relevant information in clinical notes using expert-derived reference standards. Modified global alignment methods were adapted to investigate the pattern of redundancy in individual longitudinal clinical notes as well as a larger group of patient clinical notes. Statistical language models were also developed to identify new and clinically relevant information in clinical notes. Relevant new information identified by automated methods will be highlighted in clinical notes to provide visualization cues to clinicians. New information proportion (NIP) was used to indicate the quantity of new information in each note and also navigate clinician notes with more new information. Classifying semantic types of new information further provides clinicians with specific types of new information that they are interested in finding. The techniques developed in this research can be incorporated into production EHR systems and could potentially aid clinicians in finding and synthesizing new information in a note more purposely, and could finally improve the efficiency of healthcare delivery

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

Machine learning approaches to identifying social determinants of health in electronic health record clinical notes

Social determinants of health (SDH) represent the complex set of circumstances in which individuals are born, or with which they live, that impact health. Relatively little attention has been given to processes needed to extract SDH data from electronic health records. Despite their importance, SDH data in the EHR remains sparse, typically collected only in clinical notes and thus largely unavailable for clinical decision making. I focus on developing and validating more efficient information extraction approaches to identifying and classifying SDH in clinical notes. In this dissertation, I have three goals: First, I develop a word embedding model to expand SDH terminology in the context of identifying SDH clinical text. Second, I examine the effectiveness of different machine learning algorithms and a neural network model to classify the SDH characteristics financial resource strain and poor social support. Third, I compare the highest performing approaches to simpler text mining techniques and evaluate the models based on performance, cost, and generalizability in the task of classifying SDH in two distinct data sources.Doctor of Philosoph