A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

BIOMEDICAL ONTOLOGIES: EXAMINING ASPECTS OF INTEGRATION ACROSS BREAST CANCER KNOWLEDGE DOMAINS

The key ideas developed in this thesis lie at the intersection of epistemology, philosophy of molecular biology, medicine, and computer science. I examine how the epistemic and pragmatic needs of agents distributed across particular scientific disciplines influence the domain-specific reasoning, classification, and representation of breast cancer. The motivation to undertake an interdisciplinary approach, while addressing the problems of knowledge integration, originates in the peculiarity of the integrative endeavour of sciences that is fostered by information technologies and ontology engineering methods. I analyse what knowledge integration in this new field means and how it is possible to integrate diverse knowledge domains, such as clinical and molecular. I examine the extent and character of the integration achieved through the application of biomedical ontologies. While particular disciplines target certain aspects of breast cancer-related phenomena, biomedical ontologies target biomedical knowledge about phenomena that is often captured within diverse classificatory systems and domain-specific representations. In order to integrate dispersed pieces of knowledge, which is distributed across assorted research domains and knowledgebases, ontology engineers need to deal with the heterogeneity of terminological, conceptual, and practical aims that are not always shared among the domains. Accordingly, I analyse the specificities, similarities, and diversities across the clinical and biomedical domain conceptualisations and classifications of breast cancer. Instead of favouring a unifying approach to knowledge integration, my analysis shows that heterogeneous classifications and representations originate from different epistemic and pragmatic needs, each of which brings a fruitful insight into the problem. Thus, while embracing a pluralistic view on the ontologies that are capturing various aspects of knowledge, I argue that the resulting integration should be understood in terms of a coordinated social effort to bring knowledge together as needed and when needed, rather than in terms of a unity that represents domain-specific knowledge in a uniform manner. Furthermore, I characterise biomedical ontologies and knowledgebases as a novel socio-technological medium that allows representational interoperability across the domains. As an example, which also marks my own contribution to the collaborative efforts, I present an ontology for HER2+ breast cancer phenotypes that integrates clinical and molecular knowledge in an explicit way. Through this and a number of other examples, I specify how biomedical ontologies support a mutual enrichment of knowledge across the domains, thereby enabling the application of molecular knowledge into the clinics

Patient triage by topic modelling of referral letters: Feasibility study

Background: Musculoskeletal conditions are managed within primary care but patients can be referred to secondary care if a specialist opinion is required. The ever increasing demand of healthcare resources emphasizes the need to streamline care pathways with the ultimate aim of ensuring that patients receive timely and optimal care. Information contained in referral letters underpins the referral decision-making process but is yet to be explored systematically for the purposes of treatment prioritization for musculoskeletal conditions. Objective: This study aims to explore the feasibility of using natural language processing and machine learning to automate triage of patients with musculoskeletal conditions by analyzing information from referral letters. Specifically, we aim to determine whether referral letters can be automatically assorted into latent topics that are clinically relevant, i.e. considered relevant when prescribing treatments. Here, clinical relevance is assessed by posing two research questions. Can latent topics be used to automatically predict the treatment? Can clinicians interpret latent topics as cohorts of patients who share common characteristics or experience such as medical history, demographics and possible treatments? Methods: We used latent Dirichlet allocation to model each referral letter as a finite mixture over an underlying set of topics and model each topic as an infinite mixture over an underlying set of topic probabilities. The topic model was evaluated in the context of automating patient triage. Given a set of treatment outcomes, a binary classifier was trained for each outcome using previously extracted topics as the input features of the machine learning algorithm. In addition, qualitative evaluation was performed to assess human interpretability of topics. Results: The prediction accuracy of binary classifiers outperformed the stratified random classifier by a large margin giving an indication that topic modelling could be used to predict the treatment thus effectively supporting patient triage. Qualitative evaluation confirmed high clinical interpretability of the topic model. Conclusions: The results established the feasibility of using natural language processing and machine learning to automate triage of patients with knee and/or hip pain by analyzing information from their referral letters

Ambient air pollution and transportation noise : how they affect mental health in older adults

Whether environmental factors are associated with mental health issues among older adults remains unclear. This doctoral thesis aimed to determine the extent to which air pollution and transportation noise affect mental health in older adults. We used data from the Swedish National study on Aging and Care-Kungsholmen (SNAC-K). Study I PM2.5 was not linearly associated with faster cognitive decline over 12 years of follow-up. A significantly increased risk of faster cognitive decline was observed for low levels of PM2.5 (<8.6μg/m3) among the oldest-old group (OR 1.81; 95% CI: 1.02–3.22). The existence of cerebrovascular diseases further enlarged the risk. Study II During follow-up, 15% of cognitively intact participants developed CIND, and 19% of cognitively impaired participants developed dementia. We observed 75%, 8%, and 18% increased risk of CIND onset corresponding to PM2.5, PM10 (both per 1μg/m3), and NOx (per 10μg/m3), respectively. Similarly, a higher hazard of progression from CIND to dementia was observed for exposure to higher levels of air pollution. Study III Out of a total of 2812 participants, 137 initially depression-free participants were diagnosed with depression during follow-up. Exposure to higher levels of PM2.5 and PM10 (per 1μg/m3) and NOx (per 10μg/m3) were associated with 53% (HR 1.53; 95% CI: 1.22–1.93), 7% (HR 1.07; 95% CI: 0.98–1.18), and 26% (HR 1.26; 95% CI: 1.01–1.58) increased risk of depression, accordingly. Importantly, the hazardous effects of air pollution were attenuated among participants with high social activity. Study IV A higher level of aircraft noise was associated with a faster annual rate of cognitive decline (β −0.007; 95% CI −0.012 to −0.001) over 16 years of follow-up. Higher levels of railway and aircraft noise exposure were associated with a 25% (HR 1.25; 95% CI 1.01–1.55) and 16% (HR 1.16; 95% CI: 0.91–1.49) higher hazard of developing CIND. However, no association was found between road traffic noise and cognitive outcomes. Conclusions Long-term exposure to air pollution was associated with an increased risk of faster cognitive decline, cognitive impairment, and its progression to dementia, as well as depression incidence in older adults. Aircraft noise may be associated with worsening global cognition and cognitive impairment. Railway noise was associated with an increased risk of cognitive impairment. No evidence supported the relationship between road traffic noise and cognitive outcomes. These findings suggest air pollution and transportation noise may be risk factors impacting the mental well-being of older adults

A follow-up study of the outcome of children post-craniopharyngioma surgery

The management of craniopharyngiomas in childhood remains both complex and controversial. Although histologically benign, this tumour often follows a more malignant course, not only in terms of local disease progression but also in terms of visual, neurological, neuropsychological and endocrine outcome. Seventy-five children diagnosed as having a craniopharyngioma between the ages of 1.0 and 16.4 years and treated from 1973 to early 1994 were studied to investigate the associated morbidity and mortality of this tumour and its treatment and to demonstrate which pre- and intra-operative factors were indicative of a poor outcome as defined by a quantitive assessment of morbidity. All patients had tumour surgery which entailed attempted total excision in 59 cases and subtotal resection or cyst aspiration in 16 cases. Thirty-seven children received radiotherapy, 21 following tumour recurrence. The study involved a review of clinical details and cranial imaging of all patients and a follow-up study assessment of 66 survivors - which included ophthalmological, neurological, psychological and growth and endocrine evaluation. Sixty-three patients underwent magnetic resonance imaging with a 3-dimensional volume acquisition sequence. Predictors of high morbidity included severe hydrocephalus, intra-operative adverse events and young age at presentation. Predictors of increased hypothalamic morbidity included symptoms of hypothalamic disturbance already established at diagnosis, greater height of the tumour in the mid-line, and attempts to remove adherent tumour from the region of the hypothalamus at operation. Large tumour size, young age, and severe hydrocephalus were predictors of tumour recurrence, whereas complete tumour resection (as determined by post-operative neuroimaging) and radiotherapy given electively after subtotal excision were significantly less likely to be associated with recurrent disease. Risk factors for poor cognitive outcome included complications at the time of operation and multiple surgical procedures. Treatment with radiotherapy did not significantly influence intellectual outcome. At follow-up assessment, 15% of all patients were blind, 24% had severe neurological sequelae, 56% had evidence of hypothalamic dysfunction, excluding the endocrinopathies which were almost universal, and 75% of patients had evidence of behavioural or educational difficulties. Although severe hypothalamic syndromes were uncommon (16%), they contributed significantly to morbidity and mortality and the clinical manifestations - particularly post-operative weight gain - correlated well with the extent of hypothalamic damage seen on magnetic resonance imaging. Based on these findings, it is clear that close liaison with a multidisciplinary team is essential, so that the spectra of possible sequelae can be identified early and appropriate support instituted promptly. An individualised, more flexible treatment approach is proposed whereby surgical strategies may be modified in an attempt to provide long-term tumour control with the lowest morbidity

Assessment of strength-based functioning, behavioural problems, and adaptive functioning in adolescents with autism spectrum disorders and developmental disabilities

Autism Spectrum Disorders (ASD) are characterized by marked deficits in socialization. Along this spectrum, however, intellectual functioning varies. Individuals with low-functioning autism typically function in the moderate mental retardation range (IQ between 35-50), while higher-functioning individuals have average or above-average IQs. Because daily living skills (e.g., socialization) and cognitive functioning are important considerations in the diagnosis of autism spectrum disorders, much research has focussed upon these areas in comparing ASD individuals with those individuals with developmental disabilities (DD). However, minimal research focus has been allotted to the strengths o f individuals diagnosed with these disorders as a differentiating feature. Specifically, very few studies have examined the connection between strengths, behavioural difficulties and adaptive functioning within these diagnostic groups. Comparison of individuals with these disorders with a sample of individuals with developmental disabilities may further strengthen the distinctness o f these conditions based upon behavioural difficulties, IQ and adaptive functioning, as well as provide evidence o f strengths potentially predictive o f adaptive behaviour. Thus, the purpose of this investigation was to have primary caregivers (e.g., parents/guardians) complete two strength-based questionnaires, an adaptive measure and a behavioural checklist on adolescents with four different diagnoses. These diagnoses included Low-Functioning Autism (IQ below 70), High-Functioning Autism (IQ 70 and above), Asperger syndrome, developmental disability, and a control group with no formal diagnosis. The overall focus of this thesis was exploratory, however, some specific hypotheses were also tested. Results indicated different and unique profiles for each group in terms of strengths, adaptive functioning, and behavioural difficulties. Moreover, individuals with low-functioning autism exhibited similar profiles to those with developmental disability, and individuals with high-functioning autism exhibited profiles similar to those with Asperger Syndrome. Specifically, individuals with low-functioning autism and developmental disability exhibited fewer strengths and adaptive functioning skills and greater behavioural difficulties, while those with high-functioning autism and Asperger Syndrome displayed greater strengths and adaptive functioning skills and fewer behavioural difficulties. Normal individuals also differed from the diagnostic groups in this respect, in that they exhibited far more strengths and adaptive functioning skills and fewer behavioural difficulties when compared to the diagnostic groups

Chronic Fatigue Syndrome/Myalgic Encephalomyelitis

This Special Issue on CFS/ME collects 18 papers with an interdisciplinary view on the current demographic and epidemiological data and immunological characteristics of CFS/ME and examines the different pathogenic hypotheses, as well as giving information about the latest knowledge on diagnostic investigations, pharmacological, integrative, physical, cognitive-behavioral and psychological curative approaches