Understanding disease through remote monitoring technology:A mobile health perspective on disease and diagnosis in three conditions: stress, epilepsy, and COVID-19

Mobile systems and wearable technology have developed substantially over the last decade and provide a unique long-term and continuous insight and monitoring into medical condi- tions in health research. The opportunities afforded by mobile health in access, scale, and round-the-clock recording are counterbalanced by pronounced issues in areas like participant engagement, labelling, and dataset size. Throughout this thesis the different aspects of an mHealth study are addressed, from software development and study design to data collection and analysis. Three medically relevant fields are investigated: detection of stress from physiological signals, seizure detection in epilepsy and the characterisation and monitoring of COVID-19 through mobile health techniques.The first two analytical chapters of the thesis focus on models for acute stress and epileptic seizure detection, two conditions with autonomic and physiological manifestations. Firstly, a multi-modal machine learning pipeline is developed targetting focal and general motor seizures in patients with epilepsy. The heterogenity and inter-individual differences present in this study motivated the investigation of methods to personalise models with relatively little data. I subsequently consider meta-learning for few-shot model personalisation within acute stress classification, finding increased performance compared to standard methods.As the COVID-19 pandemic gripped the world the work of this thesis reoriented around using mHealth to understand the disease. Firstly, the study design and software development of Covid Collab, a crowdsourced, remote-enrollment COVID-19 study, are examined. Within these chapters, the patterns of participant enrolment and adherence in Covid Col- lab are also considered. Adherence could impact scientific interpretations if not properly accounted for. While basic drop-out and percent completion are often considered, a more dynamic view of a participant’s behaviour can also be important. A hidden Markov model approach is used to compare participant engagement over time.Secondly, the long-term effects of COVID are investigated through data collected in the Covid Collab study, giving insight into prevalence, risk factors, and symptom manifestation with respect to wearable-recorded physiological signals. Long-term and historical data accessed retrospectively facilitated the findings of significant correlations between development of long-COVID and mHealth-derived fitness and behaviour

Phenotyping with Partially Labeled, Partially Observed Data

Identifying a group of individuals that share a common set of characteristics is a conceptually simple task, which is often difficult in practice. Such phenotyping problems emerge in various settings, including the analysis of clinical data. In this setting, phenotyping is often stymied by persistent data quality issues. These include a lack of reliable labels to indicate the presence of absence of characteristics of interest, and significant missingness in observed variables. This dissertation introduces methods for learning phenotypes when the data contain missing values (partially observed) and labels are scarce (partially labeled). Aim 1 utilizes an unsupervised probabilistic graphical model to learn phenotypes from partially observed data. Aim 2 introduces a related semi-supervised probabilistic graphical model for learning phenotypes from partially labeled clinical data. Finally, Aim 3 describes a method for training deep generative models when the training data contain missing values. The algorithm is then applied in a semi-supervised setting where it accounts for partially labeled data as well

On the neurobiology of apathy and depression in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a cerebrovascular pathology that affects the small vessels of the brain, resulting in heterogeneous brain tissue changes. These can lead to neuropsychiatric symptoms such as apathy, a loss of motivation, and depression, which is characterised by low mood and a loss of pleasure. Apathy and depression are both prevalent symptoms in SVD, but an understanding of the relationship between underlying disease processes and the expression of these neuropsychiatric symptoms remains poor. This thesis uses magnetic resonance imaging techniques to examine the neurobiological basis of apathy and depression in SVD. We show that apathy is related to focal grey matter damage and distributed white matter microstructural change. These microstructural changes underlie large-scale white matter network disruption, which is related to apathy, but not depression. We then show that depression, as a construct, can be dissociated into distinct symptoms which are associated with overlapping and distinct areas of cortical atrophy over time. This suggests that depression as a general syndrome may be characterised by atrophy in core structures, while different symptoms are associated with atrophy in more specialised areas. Consistent with these patterns of overarching tissue damage, we find that apathy, but not depression, predicts conversion to dementia in patients with SVD. Our findings suggest that different types of SVD-related pathology lead to apathy and depression. Diffuse white matter damage may lead to widespread network disruption, resulting in apathy and cognitive impairment. In contrast, depressive symptoms are associated with focal patterns of grey matter atrophy over time. This highlights the importance of differentiating neuropsychiatric symptoms, and paves the way for targeted treatment approaches.Cambridge International Scholarship (Cambridge Trust)

Effect of ADHD Medication on Criminality and Injuries : Quasi-Experimental Evidence for Patients on the Margin of Treatment

Attention-deficit/hyperactivity disorder (ADHD) er den vanligste nevropsykiatriske lidelsen hos barn og unge. Store geografiske variasjoner i diagnostisering og medisinering av ADHD har bidratt til bekymringer om under- og overbehandling. Det er behov for mer kausal kunnskap om hvordan farmakologisk behandling av ADHD påvirker virkelige utfall for personer med mildere symptomer som behandles eller ikke behandles avhengig av behandlers preferanser. Denne avhandlingen estimerer effekter av farmakologisk behandling av ADHD på kriminalitet og ulykker, og undersøker geografisk variasjon i diagnoser og symptombelastning for ADHD basert på norske register- og surveydata. Variasjon i klinikeres behandlingspreferanse for pasienter i «gråsonen» (eller på marginen) for behandling anvendes som en kvasieksperimentell randomisering til ADHD medisin for ellers like pasienter i et instrumentvariabeldesign. Behandlingsestimatene gjelder pasienter hvor klinikeres behandlingsbeslutninger varierer, og inkluderer ikke pasienter med lav eller høy symptombelastning der det er klinisk konsensus. Jeg finner beskyttende effekter av farmakologisk behandling på vold- og orden- og integritetsrelatert kriminalitet, men ikke andre typer kriminalitet. Jeg finner ikke klar støtte for behandlingseffekter på ulykker. Videre er den geografiske variasjonen i ADHD-diagnoser betydelig større enn det som kan forklares av variasjon i symptombelastning. Avhandlingen bidrar til tre områder innen ADHD-forskningen: debatten om under- og overbehandling, kausal inferens, og langtidseffekter for kriminalitet og ulykker. Kliniske behandlingsbeslutninger er basert på en helhetlig vurdering. Avhandlingen viser at farmakologisk behandling reduserer noen typer kriminalitet, men ikke ulykker, for den understuderte pasientgruppen i «gråsonen» for behandling, og utvider dermed evidensgrunnlaget til klinikers beslutninger for to viktige utfall. Samtidig vises hvordan kvasieksperimentelle design og registerdata kan kombineres for å gi effektestimater som ikke kan oppnås med randomiserte eksperimenter grunnet etikk eller observasjonsstudier grunnet uobservert konfundering.Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children and adolescents. Large geographical variations in diagnosis and medication for ADHD have raised concerns about under- and overtreatment. There is demand for more causal knowledge about how pharmacological treatment of ADHD impacts real-life outcomes among otherwise similar patients who receive treatment due to varying provider preference for treatment. This thesis estimates effects of pharmacological treatment of ADHD on crime and injuries and examines geographical variation in diagnoses and symptom load of ADHD based on Norwegian population-wide registry and survey data. Variation in providers’ (i.e., clinicians) treatment preference for patients on the margin of treatment is used as quasi-experimental randomization to pharmacological treatment in an instrumental variable design. The treatment effects concern patients where providers differ in treatment decisions, and do not include patients with a very low or high symptom burden where there is clinical consensus. I find protective effects of pharmacological treatment on violence- and public-orderrelated crimes, but not other types of crime. I do not find clear evidence for treatment effects on overall injuries. Furthermore, the geographical variation in diagnoses of ADHD is much larger than what can be explained by variation in symptom load. The thesis contributes to three areas in ADHD research: the debate on under- and overtreatment, causal inference, and long-term effects on crime and injuries. Clinical treatment decisions are based on a holistic assessment where many outcomes are considered. This thesis shows that pharmacological treatment reduces some types of crimes, but not overall injuries, for the understudied patient group on the margin of treatment, and this expands the evidence base for clinicians’ decisions for two important real-life outcomes among people with ADHD. The methodological approach illustrates how quasi-experimental designs and registry data can be combined to estimate treatment effects that cannot be obtained in randomized experiments due to ethics nor observational studies due to unobserved confounding.Doktorgradsavhandlin

Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity, potential biases, and the need for interpretability. To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial. Explainability is usually referred to as an AI system's ability to provide a robust interpretation of its decision-making logic or the decisions themselves to human stakeholders. In clinical risk prediction, other aspects of explainability like fairness, bias, trust, and transparency also represent important concepts beyond just interpretability. In this review, we address the relationship between these concepts as they are often used together or interchangeably. This review also discusses recent progress in developing explainable models for clinical risk prediction, highlighting the importance of quantitative and clinical evaluation and validation across multiple common modalities in clinical practice. It emphasizes the need for external validation and the combination of diverse interpretability methods to enhance trust and fairness. Adopting rigorous testing, such as using synthetic datasets with known generative factors, can further improve the reliability of explainability methods. Open access and code-sharing resources are essential for transparency and reproducibility, enabling the growth and trustworthiness of explainable research. While challenges exist, an end-to-end approach to explainability in clinical risk prediction, incorporating stakeholders from clinicians to developers, is essential for success

Acute blood biomarker profiles predict cognitive deficits 6 and 12 months after COVID-19 hospitalization

Post-COVID cognitive deficits, including ‘brain fog’, are clinically complex, with both objective and subjective components. They are common and debilitating, and can affect the ability to work, yet their biological underpinnings remain unknown. In this prospective cohort study of 1,837 adults hospitalized with COVID-19, we identified two distinct biomarker profiles measured during the acute admission, which predict cognitive outcomes 6 and 12 months after COVID-19. A first profile links elevated fibrinogen relative to C-reactive protein with both objective and subjective cognitive deficits. A second profile links elevated D-dimer relative to C-reactive protein with subjective cognitive deficits and occupational impact. This second profile was mediated by fatigue and shortness of breath. Neither profile was significantly mediated by depression or anxiety. Results were robust across secondary analyses. They were replicated, and their specificity to COVID-19 tested, in a large-scale electronic health records dataset. These findings provide insights into the heterogeneous biology of post-COVID cognitive deficits

Acute blood biomarker profiles predict cognitive deficits 6 and 12 months after COVID-19 hospitalization

Post-COVID cognitive deficits, including ‘brain fog’, are clinically complex, with both objective and subjective components. They are common and debilitating, and can affect the ability to work, yet their biological underpinnings remain unknown. In this prospective cohort study of 1,837 adults hospitalized with COVID-19, we identified two distinct biomarker profiles measured during the acute admission, which predict cognitive outcomes 6 and 12 months after COVID-19. A first profile links elevated fibrinogen relative to C-reactive protein with both objective and subjective cognitive deficits. A second profile links elevated D-dimer relative to C-reactive protein with subjective cognitive deficits and occupational impact. This second profile was mediated by fatigue and shortness of breath. Neither profile was significantly mediated by depression or anxiety. Results were robust across secondary analyses. They were replicated, and their specificity to COVID-19 tested, in a large-scale electronic health records dataset. These findings provide insights into the heterogeneous biology of post-COVID cognitive deficits