Doctor of Philosophy

dissertationIn its report To Err is Human, The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by conventional methods. The objective of this study was to examine novel KDD techniques used by other disciplines to create predictive models using healthcare data and validate the results through clinical domain expertise and performance measures. Patient records for the present study were extracted from the enterprise data warehouse (EDW) from Intermountain Healthcare. Patients with reported adverse events were identified from ICD9 codes. A clinical classification of the ICD9 codes was developed, and the clinical categories were analyzed for risk factors for adverse events including adverse drug events. Pharmacy data were categorized and used for detection of drugs administered in temporal sequence with antidote drugs. Data sampling and data boosting algorithms were used as signal amplification techniques. Decision trees, Naïve Bayes, Canonical Correlation Analysis, and Sequence Analysis were used as machine learning algorithms. iv Performance measures of the classification algorithms demonstrated statistically significant improvement after the transformation of the dataset through KDD techniques, data boosting and sampling. Domain expertise was applied to validate clinical significance of the results. KDD methodologies were applied successfully to a complex clinical dataset. The use of these methodologies was empirically proven effective in healthcare data through statistically significant measures and clinical validation. Although more research is required, we demonstrated the usefulness of KDD methodologies in knowledge extraction from complex clinical data

Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction

Background Elderly patients are at increased risk for Adverse Drug Events (ADEs). Proactively screening elderly people visiting the emergency department for the possibility of their hospital admission being drug-related helps to improve patient care as well as prevent potential unnecessary medical costs. Existing routine ADE assessment heavily relies on a rule-based checking process. Recently, machine learning methods have been shown to be effective in automating the detection of ADEs, however, most approaches used only either structured data or free texts for their feature engineering. How to better exploit all available EHRs data for better predictive modeling remains an important question. On the other hand, automated reasoning for the preventability of ADEs is still a nascent line of research. Methods Clinical information of 714 elderly ED-visit patients with ADE preventability labels was provided as ground truth data by Jeroen Bosch Ziekenhuis hospital, the Netherlands. Methods were developed to address the challenges of applying feature engineering to heterogeneous EHRs data. A Dual Autoencoders (2AE) model was proposed to solve the problem of imbalance embedded in the existing training data. Results Experimental results showed that 2AE can capture the patterns of the minority class without incorporating an extra process for class balancing. 2AE yields adequate performance and outperforms other more mainstream approaches, resulting in an AUPRC score of 0.481. Conclusions We have demonstrated how machine learning can be employed to analyze both structured and unstructured data from electronic health records for the purpose of preventable ADE prediction. The developed algorithm 2AE can be used to effectively learn minority group phenotype from imbalanced data

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore ‘Challenges in Mining Drug Adverse Reactions’. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.M.K.: This work was supported in part through the collaboration between the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center; we also acknowledge the 2020 Proyectos de I+D+i - RTI Tipo A (PID2020-119266RA-I00) for support. Ö.U.: This study was supported in part by the National Library of Medicine under Award Number R15LM013209 and R13LM013127.Peer ReviewedPostprint (published version

Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal

BACKGROUND One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. OBJECTIVE This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. METHODS This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged ≥65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants' EMRs. Third, several automated rule-based and machine learning-based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F$_{1}$-score, sensitivity, specificity, and positive and negative predictive values. RESULTS After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged ≥65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. CONCLUSIONS This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/40456

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes.

There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods

Adverse drug reaction extraction on electronic health records written in Spanish

148 p.This work focuses on the automatic extraction of Adverse Drug Reactions (ADRs) in Electronic HealthRecords (EHRs). That is, extracting a response to a medicine which is noxious and unintended and whichoccurs at doses normally used. From Natural Language Processing (NLP) perspective, this wasapproached as a relation extraction task in which the drug is the causative agent of a disease, sign orsymptom, that is, the adverse reaction.ADR extraction from EHRs involves major challenges. First, ADRs are rare events. That is, relationsbetween drugs and diseases found in an EHR are seldom ADRs (are often unrelated or, instead, related astreatment). This implies the inference from samples with skewed class distribution. Second, EHRs arewritten by experts often under time pressure, employing both rich medical jargon together with colloquialexpressions (not always grammatical) and it is not infrequent to find misspells and both standard andnon-standard abbreviations. All this leads to a high lexical variability.We explored several ADR detection algorithms and representations to characterize the ADR candidates.In addition, we have assessed the tolerance of the ADR detection model to external noise such as theincorrect detection of implied medical entities implied in the ADR extraction, i.e. drugs and diseases. Westtled the first steps on ADR extraction in Spanish using a corpus of real EHRs