CREATE: Clinical Record Analysis Technology Ensemble

In this thesis, we describe an approach that won a psychiatric symptom severity prediction challenge. The challenge was to correctly predict the severity of psychiatric symptoms on a 4-point scale. Our winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new features, followed by a (iv) feature selection step to eliminate irrelevant features, followed by a (v) classifier training algorithm in which a total of 22 classifiers including new classifier variants of AdaBoost and RandomForest were trained on seven different data views, and (vi) finally an ensemble learning step, in which ensembles of best learners were used to improve on the accuracy of individual learners. All of this was tested via standard 10-fold cross-validation on training data provided by the N-GRID challenge organizers, of which the three best ensembles were selected for submission to N-GRID\u27s blind testing. The best of our submitted solutions garnered an overall final score of 0.863 according to the organizer\u27s measure. All 3 of our submissions placed within the top 10 out of the 65 total submissions. The challenge constituted Track 2 of the 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID) Shared Task in Clinical Natural Language Processing

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges

DEVELOPMENT OF A COMPREHENSIVE PRIMARY CARE ALGORITHM TO MANAGE CHILDREN WHO ARE OVERWEIGHT OR OBESE

ABSTRACT Almeida-Trujillo, Erika LeAnn. Development of a comprehensive primary care algorithm to manage children who are overweight and obese. Unpublished Doctor of Nursing Practice Scholarly Research Project, University of Northern Colorado, 2023. Childhood obesity is an epidemic that continues to increase not only in the United States but also worldwide. For children aged 5-19 years, being overweight is considered a body mass index greater than one standard deviation above the growth reference median and obesity is defined as excess body fat that contributes to functional loss and life-threatening comorbidities. The literature indicated that previous population-based obesity prevention efforts have only been moderately successful and might not reflect the complex needs and preferences of some children and families. Thus, there was a need for individualized interventions that supported children who are overweight or obese in developing healthier practices that persist into adulthood. Primary care providers administer everything from prenatal to end-of-life care and are in a key position to monitor the health and wellbeing of children. However, many primary care providers serving pediatric populations lack a flexible set of guidelines to inform their care of children who are overweight or obese. Having a systematic yet localized approach might streamline the intervention process and improve patient outcomes. Clinical tools such as algorithms might guide providers toward evidence-based interventions and utilization of local services. The purpose of this Doctor of Nursing Practice scholarly project was to develop and evaluate a treatment algorithm for children identified as being overweight or obese designed for use in the primary care setting using published evidence and a panel of clinical experts. Using the Delphi method, a panel of nine clinical experts provided feedback on increasingly refined drafts of a iv proposed algorithm. The Stetler (2001) model was utilized as a theoretical framework throughout the project. After two rounds of feedback and revisions, broad consensus among the panel was achieved. Findings from this scholarly project also included a proposal for future pilot testing of the final draft algorithm in a family practice or pediatric clinical setting. Keywords: childhood obesity, obese, epidemic, comorbidities, intervention, algorith

L'usage secondaire des données médico-administratives afin d’optimiser l’usage des médicaments chez les patients atteints de maladies respiratoires chroniques : adhésion aux médicaments, identification de cas et intensification du traitement

Medication adherence in patients with asthma and chronic obstructive pulmonary disease (COPD) is notoriously low and is associated with suboptimal therapeutic outcomes. To intervene effectively, family physicians need to assess medication adherence efficiently and accurately. Otherwise, failure to detect nonadherence may further reduce patient disease control and result in unnecessary treatment escalation that can increase the risk of adverse events and lead to more complex and costly drug regimens. The overarching goal of this thesis was to investigate how the use of secondary healthcare data can be leveraged to optimize medication adherence in clinical practice. Methodological considerations to facilitate our understanding of treatment escalation in asthma using secondary healthcare data were also examined. In the first part of my doctoral research program, I led a project which aimed at developing e-MEDRESP, a novel web-based tool built from pharmacy claims data that provides to family physicians with objective and easily interpretable information on patient adherence to asthma/COPD medications. This tool was developed in collaboration with family physicians and patients using a framework inspired by user-centered design principles. As part of a feasibility study, e-MEDRESP was subsequently implemented in electronic medical records across several family medicine clinics in Quebec (346 patients, 19 physicians). Findings showed that its integration within physician workflow was feasible. Physicians reported that the tool helped to: 1) better evaluate their patients’ medication adherence; and 2) adjust prescribed therapies, with mean ± sd ratings (5-point Likert scale) of 4.8±0.7 and 4.3±0.9, respectively. A pre-post analysis did not reveal improvement in adherence among patients whose physician consulted e-MEDRESP during a medical visit. However, significant improvements in adherence for inhaled corticosteroids (Proportion of days covered (PDC): 26.4% (95% CI: 14.3-39.3%)) and long-acting muscarinic agents (PDC: 26.4% (95% CI: 12.4-40.2%)) were observed among patients whose adherence level was less than 80% in the 6-month period prior to the medical visit. The second part of this research program consisted of two studies which laid the groundwork to estimate the association between medication adherence and treatment escalation in asthma using Canadian healthcare administrative data, a phenomenon that is currently under-explored in the literature. Prior to embarking in this study, it is important to ensure that healthcare administrative databases can be used to identify asthma patients and treatment escalations in an adequate manner. First, a systematic review was conducted to obtain an overview of the available evidence supporting the validity of algorithms to identify asthma patients in healthcare administrative databases. The algorithm developed by Gershon et al. (Canadian Respiratory Journal, 2009;16(6):183-188) comprising ≥2 ambulatory medical visits or ≥1 hospitalization for asthma over two years had the best trade-off between sensitivity (84 %) and specificity (77%). Second, an operational definition of treatment escalation was developed through a Delphi study that incorporated an expert consensus process. This definition includes 7 steps and was inspired by the 2020 Global for Initiative for Asthma treatment guidelines. I plan to integrate the definitions obtained from these two studies in a future cohort study which aims to examine the association between medication adherence and treatment escalation in asthma. My research provides compelling evidence on the importance of developing and evaluating the feasibility of implementing tools which can aid physicians in assessing medication adherence in clinical practice and extends the literature on treatment escalation in asthma.L’adhésion aux médicaments chez les patients présentant un asthme ou une maladie pulmonaire obstructive chronique (MPOC) est reconnue pour être faible. Pour intervenir efficacement, les médecins de famille doivent évaluer de manière précise l’adhésion aux médicaments. Ne pas détecter la non-adhésion peut réduire davantage la maîtrise de la maladie, entraîner une intensification non-nécessaire du traitement, mener à des schémas pharmacologiques plus complexes et coûteux et par conséquent, augmenter le risque d’événements indésirables. La présente thèse vise à approfondir les connaissances sur l'usage secondaire des données médico-administratives afin d’optimiser l’adhésion et l’usage des médicaments chez les patients atteints de maladies respiratoires chronique, au moyen d’une approche méthodologique mixte de recherche. Plusieurs questions méthodologiques cruciales concernant l’étude de l’intensification du traitement en asthme ont également été abordées. Le premier axe porte sur le développement de l’outil e-MEDRESP, qui s’appuie sur les renouvellements d’ordonnances et qui est conçu pour donner rapidement accès aux médecins de famille à une mesure objective et facilement interprétable de l’adhésion aux médicaments utilisés dans le traitement de l’asthme et de la MPOC. L’outil a été développé en collaboration avec des médecins de famille et des patients à l’aide de groupes de discussion et d’entrevues individuelles. Dans le cadre d’une étude de faisabilité, l’outil e-MEDRESP a été par la suite implanté dans les dossiers médicaux électroniques de plusieurs cliniques de médecine familiale au Québec (346 patients, 19 médecins). Les résultats ont montré que l’intégration de d’e-MEDRESP dans le flux de travail des médecins était faisable. Les médecins ont indiqué que l’outil leur a permis de : 1) mieux évaluer l’adhésion aux médicaments de leurs patients (cote moyenne et écart-type sur une échelle de Likert à 5 points [perception d’accord] de 4,8±0,7); et 2) ajuster les traitements prescrits (4,8±0,7 et 4.3±0,9). Une analyse pré-post n’a pas révélé d’amélioration au niveau de l’adhésion aux médicaments chez les patients dont le médecin a consulté e-MEDRESP lors d’une visite médicale. Toutefois, une amélioration statistiquement significative a été observée chez les patients dont le niveau d’adhésion était inférieur à 80 % au cours de la période de six mois précédant la visite et qui étaient traités par des corticostéroïdes inhalés (Proportion of days covered (PDC) = 26,4 % (IC à 95 % : 14,3-39,3 %) ou des antagonistes muscariniques à action prolongée (PDC = 26,9 % (IC à 95 % : 12,4-40,2 %)). Le deuxième axe présente des travaux préparatoires à la conduite d’une cohorte qui sera réalisée à partir de bases de données médico-administratives et qui aura comme objectif d’estimer l’association entre l’adhésion aux médicaments et l’intensification du traitement de l’asthme, une question peu explorée à ce jour. Avant de débuter une telle étude, il est important de s’assurer que les bases de données médico-administratives peuvent être utilisées pour identifier de manière adéquate les patients asthmatiques et l’intensification du traitement. Dans un premier temps, une revue systématique a été effectuée pour identifier les données probantes disponibles concernant la validité des algorithmes permettant d’identifier les patients asthmatiques dans les bases de données médico-administratives. L’algorithme qui a été développé par Gershon et coll. (Revue canadienne de pneumologie, 2009; vol. 16, no 6, p. 183-188), qui comprenait deux visites médicales ambulatoires ou une hospitalisation pour asthme sur deux ans, présentait le meilleur compromis entre la sensibilité (84 %) et la spécificité (77 %). Dans un second temps, une définition opérationnelle de l’intensification du traitement a été élaborée dans le cadre d’une étude Delphi qui incorporait un processus consensuel d’experts. Cette définition comprend sept étapes et s’inspire des lignes directrices 2020 de l'initiative mondiale de lutte contre l'asthme. Les définitions obtenues à partir de ces deux études seront intégrées dans l’étude de cohorte. Les études constituant cette thèse démontrent l’importance de développer des outils qui permettent aux médecins d’évaluer l’adhésion aux médicaments dans leur pratique clinique, en plus d’enrichir la littérature scientifique médicale sur l’intensification du traitement chez les patients asthmatiques

Analysis of free text in electronic health records for identification of cancer patient trajectories

With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004–2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment

Improving Syntactic Parsing of Clinical Text Using Domain Knowledge

Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information. Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction

Reducing Inappropriate Polypharmacy for Older Inpatients: Development of In-Hospital Interventions Based on Benzodiazepine Deprescribing

There are many barriers to deprescribing in the routine care of older inpatients with polypharmacy. Implementation is limited by factors related to medications, clinicians, patients, and the acute care setting. It is challenging to deprescribe medications such as benzodiazepines (BZDs) with complex reduction regimens due to their withdrawal symptoms, so the process must continue beyond the hospital discharge and involve community clinicians like general practitioners (GPs). However, there is clinical benefit in deprescribing these medications, because long term use of BZDs by older patients is associated with considerable harm including falls, delirium, and a significant financial and legal burden to society. Aims The overall aim of this thesis was to develop two specific interventions to improve deprescribing from hospital as part of a wider research program, developing a multi-strategic intervention. These are an e-learning module and a preferred language guide for communicating deprescribing decisions. The thesis is based on the hypotheses that 1) Providing education will increase multidisciplinary hospital clinician knowledge and self-efficacy of polypharmacy and deprescribing. 2) Identification of local barriers and degree of self-efficacy for deprescribing amongst hospital clinicians would assist in targeting interventions. 3) Co-design of a standardised template to communicate deprescribing decisions with both writers and receivers will lead to a balance of appropriate information being communicated. Literature Review This thesis uses BZD deprescribing as a model. A literature review of interventions for BZD deprescribing confirmed variable success rates across a heterogeneity of methodological approaches. These interventions were classified by target population (physician, pharmacist, or patients) and then classified against the Behaviour Change Wheel taxonomy. This review confirmed the benefit of multi-strategic interventions and appropriate dose reduction regimens to inform the rest of the study. Development of e-learning module, immediate impact on medical students and assessment of hospital clinician perceptions and practice on deprescribing Secondly, an e-learning module on deprescribing was developed in conjunction with the Health Education and Training Institute (HETI) of New South Wales Health, targeting all clinicians involved in care of older inpatients. Following completion of the module, eligible hospital clinicians completed a quantitative questionnaire to describe their perceptions and practice of deprescribing. Senior medical students also completed this questionnaire in a pre-post manner to describe the immediate impact of the module. Hospital clinicians and senior medical students reported limited self-efficacy in deprescribing, especially in developing plans and implementing them. Hospital clinicians also reported that they did not deprescribe frequently, despite a general awareness of polypharmacy. Differences were identified in clinical roles: Junior doctors do not perceive deprescribing as part of their clinical role whereas non-medication reviewing clinicians (nurses and allied health) have an interest in contributing more to deprescribing in hospital. Pre-post analysis of medical student responses found a small but statistically significant improvement in these areas after viewing the module. The module is likely to be a useful component of a multi-strategic intervention and information gained around local barriers and enablers will be used to inform the rest of the intervention. Developing a preferred language guide to effectively communicate in-hospital deprescribing decisions Finally, a preferred language guide to assist with communicating deprescribing decisions was developed using a qualitative approach. Semi-structured interviews and focus groups involving clinicians who send and receive discharge summaries informed development of the guide. As a result, a novel, structured, copy-and-paste, fill-in-the-blanks template was developed and integrated within a concurrently developed deprescribing guide. This aimed to keep the message specific and concise whilst still allowing the GP autonomy to carry out patient care. Conclusion Two specific interventions have been developed to address in-hospital barriers to deprescribing: an education module addressing knowledge and self-efficacy of polypharmacy and deprescribing, and a communication guide to improve communication of deprescribing decisions made in hospital at transitions of care. Further evaluation of the complete intervention is required, and consideration should be given to evaluation with an implementation science approach. The research findings suggest potential for future interventions that also target the admitted patient and non-medication reviewing clinicians, to improve in-hospital deprescribing and reduce medication related harm

Doctor of Philosophy

dissertationRapidly evolving technologies such as chip arrays and next-generation sequencing are uncovering human genetic variants at an unprecedented pace. Unfortunately, this ever growing collection of gene sequence variation has limited clinical utility without clear association to disease outcomes. As electronic medical records begin to incorporate genetic information, gene variant classification and accurate interpretation of gene test results plays a critical role in customizing patient therapy. To verify the functional impact of a given gene variant, laboratories rely on confirming evidence such as previous literature reports, patient history and disease segregation in a family. By definition variants of uncertain significance (VUS) lack this supporting evidence and in such cases, computational tools are often used to evaluate the predicted functional impact of a gene mutation. This study evaluates leveraging high quality genotype-phenotype disease variant data from 20 genes and 3986 variants, to develop gene-specific predictors utilizing a combination of changes in primary amino acid sequence, amino acid properties as descriptors of mutation severity and Naïve Bayes classification. A Primary Sequence Amino Acid Properties (PSAAP) prediction algorithm was then combined with well established predictors in a weighted Consensus sum in context of gene-specific reference intervals for known phenotypes. PSAAP and Consensus were also used to evaluate known variants of uncertain significance in the RET proto-oncogene as a model gene. The PSAAP algorithm was successfully extended to many genes and diseases. Gene-specific algorithms typically outperform generalized prediction tools. Characteristic mutation properties of a given gene and disease may be lost when diluted into genomewide data sets. A reliable computational phenotype classification framework with quantitative metrics and disease specific reference ranges allows objective evaluation of novel or uncertain gene variants and augments decision making when confirming clinical information is limited