DutchHatTrick: semantic query modeling, ConText, section detection, and match score maximization

This report discusses the collaborative work of the ErasmusMC, University of Twente, and the University of Amsterdam on the TREC 2011 Medical track. Here, the task is to retrieve patient visits from the University of Pittsburgh NLP Repository for 35 topics. The repository consists of 101,711 patient reports, and a patient visit was recorded in one or more reports

Clinical narrative analytics challenges

Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different services, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and validate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts provided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish

Identifying Outcomes of Care from Medical Records to Improve Doctor-Patient Communication

Between appointments, healthcare providers have limited interaction with their patients, but patients have similar patterns of care. Medications have common side effects; injuries have an expected healing time; and so on. By modeling patient interventions with outcomes, healthcare systems can equip providers with better feedback. In this work, we present a pipeline for analyzing medical records according to an ontology directed at allowing closed-loop feedback between medical encounters. Working with medical data from multiple domains, we use a combination of data processing, machine learning, and clinical expertise to extract knowledge from patient records. While our current focus is on technique, the ultimate goal of this research is to inform development of a system using these models to provide knowledge-driven clinical decision-making

Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

An automated technique for identifying associations between medications, laboratory results and problems

AbstractBackgroundThe patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete.ObjectiveTo determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list.DesignAssociation rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women’s Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results.MeasurementsCandidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby’s Diagnostic and Laboratory Test Reference for laboratory results.ResultsWe were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest).ConclusionAssociation rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches

Automated clinical coding:What, why, and where we are?

Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html . Funding Information: The work is supported by WellCome Trust iTPA Awards (PIII009, PIII032), Health Data Research UK National Phenomics and Text Analytics Implementation Projects, and the United Kingdom Research and Innovation (grant EP/S02431X/1), UKRI Centre for Doctoral Training in Biomedical AI at the University of Edinburgh, School of Informatics. H.D. and J.C. are supported by the Engineering and Physical Sciences Research Council (EP/V050869/1) on “ConCur: Knowledge Base Construction and Curation”. HW was supported by Medical Research Council and Health Data Research UK (MR/S004149/1, MR/S004149/2); British Council (UCL-NMU-SEU international collaboration on Artificial Intelligence in Medicine: tackling challenges of low generalisability and health inequality); National Institute for Health Research (NIHR202639); Advanced Care Research Centre at the University of Edinburgh. We thank constructive comments from Murray Bell and Janice Watson in Terminology Service in Public Health Scotland, and information provided by Allison Reid in the coding department in NHS Lothian, Paul Mitchell, Nicola Symmers, and Barry Hewit in Edinburgh Cancer Informatics, and staff in Epic Systems Corporation. Thanks for the suggestions from Dr. Emma Davidson regarding clinical research. Thanks to the discussions with Dr. Kristiina Rannikmäe regarding the research on clinical coding and with Ruohua Han regarding the social and qualitative aspects of this research. In Fig. 1 , the icon of “Clinical Coders” was from Freepik in Flaticon, https://www.flaticon.com/free-icon/user_747376 ; the icon of “Automated Coding System” was from Free Icon Library, https://icon-library.com/png/272370.html. Publisher Copyright: © 2022, The Author(s).Clinical coding is the task of transforming medical information in a patient’s health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019–early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable processof a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.Peer reviewe

Ethical Issues in Text Mining for Mental Health

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2

User Interfaces for Patient-Centered Communication of Health Status and Care Progress

The recent trend toward patients participating in their own healthcare has opened up numerous opportunities for computing research. This dissertation focuses on how technology can foster this participation, through user interfaces to effectively communicate personal health status and care progress to hospital patients. I first characterize the design space for electronic information communication to patients through field studies conducted in multiple hospital settings. These studies utilize a combination of survey instruments, and low- and high-fidelity prototypes, including a document-editing prototype through which users can view and manage clinical data to automatically associate it with progress notes. The prototype, activeNotes, includes the first known techniques supporting clinical information requests directly within a document editor. A usage study with ICU physicians at New York-Presbyterian Hospital (NYP) substantiated our design and revealed how electronic information related to patient status and care progress is derived from a typical Electronic Health Record system. Insights gained from this study informed following studies to understand how to design abstracted, plain-language views suitable for patients. We gauged both patient and physician responses to information display prototypes deployed in patient rooms for a formative study exploring their design. Following my reports on this study, I discuss the design, development and pilot evaluations of a prototype Personal Health Record application providing live, abstracted clinical information for patients at NYP. The portal, evaluated by cardiothoracic surgery patients, is the first of its kind to allow patients to capture and monitor live data related to their care. Patient use of the portal influenced the subsequent design of tools to support users in making sense of online medication information. These tools, designed with nurses and pharmacists and evaluated by cardiothoracic surgery patients at NYP, were developed using topic modeling approaches and text analysis techniques. Embodied in a prototype called Remedy, they enable rapid filtering and comparison of medication-related search results, based on a number of website features and content topics. I conclude by discussing how findings from this series of studies can help shape the ongoing design and development of patient-centered technology