Bruk av naturlig språkprosessering i psykiatri: En systematisk kartleggingsoversikt

Bakgrunn: Bruk av kunstig intelligens (AI) har et stadig økende fokus, også i helsevesenet. En metode som virker lovende, er naturlig språkprosessering (NLP), som kan brukes til analysering av skriftlig tekst, for eksempel tekst i elektroniske pasientjournaler. Denne undersøkelsen har som formål å undersøke forskning som er gjort på bruk av naturlig språkprosessering for analysering av elektroniske journaler fra pasienter med alvorlige psykiske lidelser, som affektive lidelser og psykoselidelser. Den overordnete hensikten med dette, er å få et inntrykk av om noe av forskningen som er gjort har fokus på forbedring av pasientenes helsesituasjon. Materiale og metode: Det ble gjennomført en systematisk kartleggingsoversikt («scoping review»). Litteratursøket ble gjort i én database for medisinsk forskning, PubMed, med søketermene «psychiatry», «electronic medical records» og «natural language processing». Søket var ikke avgrenset i tid. For at en artikkel skulle bli inkludert i undersøkelsen måtte den være empirisk, ha utført analyser på journaldata i fritekst, ha brukt elektroniske journaler fra psykiatriske pasienter med psykoselidelser og/eller affektive lidelser og være skrevet på engelsk språk. Resultater: Litteratursøket resulterte i totalt 211 unike artikler, av disse oppfylte 37 artikler inklusjonskriteriene i kartleggingsoversikten, og ble undersøkt videre. De fleste av studiene var gjennomført i Storbritannia og USA. Størrelsen på studiepopulasjonen varierte mye, fra noen hundre til flere hundre tusen inkluderte pasienter i studiene. Det var lite av forskningen som var gjort på spesifikke dokumenttyper fra pasientjournal, som for eksempel epikriser eller innkomstjournaler. Hensikten for studiene varierte mye, men kunne deles inn i noen felles kategorier: 1) identifisering av informasjon fra journal, 2) kvantitative undersøkelser av populasjonen eller journalene, 3) seleksjon av pasienter til kohorter og 4) vurdering av risiko. Fortolkning: Det trengs mer grunnforskning før teknologi for naturlig språkprosessering til analyse av elektronisk journal vil bidra med forbedring av psykiatriske pasienters helsesituasjon

Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models

Identification and Evaluation of Endophenotypes and Biomarkers of Schizophrenia and Bipolar Disorder: Genomic Dissection of the Psychosis Phenotype

Background: Psychotic disorders affect approximately 3% of the population. Over 100 genetic variants have been associated with schizophrenia and about 50 with bipolar disorder. Each of them individually has a small effect on disease risk but combined in a cumulative polygenic risk score (PRS), they have a major impact. Copy number variants (CNVs) have also been associated with schizophrenia. However, little is known about their functional effects. The investigation of endophenotypes, which fall in the genotype to phenotype pathway, could help us understand the role of genetic variants and their mechanisms. Methods: In chapter 1 of my thesis, I reviewed the literature on endophenotypes, and genetic variants associated with psychosis, which revealed that the interrelationships between several well-established cognitive, neuroimaging and electrophysiological psychosis endophenotypes, and the joint contributions of CNV burden and polygenic risk scores on psychosis risk have not been studied yet. I investigated those topics in chapters 3 and 4 respectively. In chapter 2 I carried out a scoping review of CNVs associated with neurodevelopmental disorders, psychosis and cognition and carried out a meta-analysis of 16p11.2 distal deletion in schizophrenia. I also investigated the influences of CNV size on schizophrenia risk for 53 CNVs. For all the analyses, I used CNVcatalog, which is a new repository me and my supervisors created, incorporating data from published studies examining associations of CNV loci with several clinical phenotypes, including schizophrenia. Finally, in chapter 5 I summarise the main findings of my thesis and I discuss the strengths, limitations and clinical implications of my research. Results: Chapter 2: The meta-analysis of 16p11.2 distal deletion in schizophrenia revealed that carriers of that CNV had higher risk of developing schizophrenia compared to non carriers. I also found that larger CNV size was associated with larger effect sizes when examining all CNVs together (both deletions and duplications) and CNV deletions. However, the size was not significanly associated with disease risk for CNV duplications. Chapter 3: All the cognitive endophenotypes were associated with each other. Endophenotypes across imaging, cognitive and electrophysiological domains did not show a correlation. The relationships between pairs of endophenotypes were consistent in all three participant groups (cases with psychosis, their unaffected relatives and healthy controls), differing for some of the cognitive pairings only in the strengths of the relationships. Chapter 4: I examined the joint contributions of CNV burden and polygenic risk scores on psychosis risk. I analysed two datasets separately and then combined them by meta-analysis. CNV burden and PRS could explain 11.8% and 10.8% of the variance in disease risk in each dataset. The classification accuracy of my models was 81%, 83% and 77% for the comparisons of all psychosis cases vs controls, schizophrenia cases vs controls and bipolar cases vs controls respectively. The addition of CNV burden to the models increased the variance explained only by 0.1% for MPL dataset and by 0.08% in the PEIC dataset. Discussion: Findings from my thesis contribute to our current knowledge on psychosis endophenotypes and on the genetic influences in psychoses. Deciphering the genetic architecture of psychotic disorders could hopefully in the future improve the lives of affected individuals

Information extraction framework for disability determination using a mental functioning use-case

Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the electronic health record (EHR). However, information technologies for mental health have consistently lagged because of the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This paper provides a framework for the advanced application of NLP methods to identify, extract, and organize information on mental health and functioning to inform the decision-making process applied to assessing mental health. We present a use-case related to work disability, guided by the disability determination process of the US Social Security Administration (SSA). From this perspective, the following questions must be addressed about each problem that leads to a disability benefits claim: When did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? and What is the source of the evidence about the problem? Our framework includes 4 dimensions of medical information that are central to assessing disability—temporal sequence and duration, severity, context, and information source. We describe key aspects of each dimension and promising approaches for application in mental functioning. For example, to address temporality, a complete functional timeline must be created with all relevant aspects of functioning such as intermittence, persistence, and recurrence. Severity of mental health symptoms can be successfully identified and extracted on a 4-level ordinal scale from absent to severe. Some NLP work has been reported on the extraction of context for specific cases of wheelchair use in clinical settings. We discuss the links between the task of information source assessment and work on source attribution, coreference resolution, event extraction, and rule-based methods. Gaps were identified in NLP applications that directly applied to the framework and in existing relevant annotated data sets. We highlighted NLP methods with the potential for advanced application in the field of mental functioning. Findings of this work will inform the development of instruments for supporting SSA adjudicators in their disability determination process. The 4 dimensions of medical information may have relevance for a broad array of individuals and organizations responsible for assessing mental health function and ability. Further, our framework with 4 specific dimensions presents significant opportunity for the application of NLP in the realm of mental health and functioning beyond the SSA setting, and it may support the development of robust tools and methods for decision-making related to clinical care, program implementation, and other outcomes

A clinicopathological investigation of brainstem nuclei in Lewy body dementia

Introduction: Lewy Body Dementias (LBD) - Dementia with Lewy Bodies (DLB) and Parkinson’s Disease Dementia (PDD) - are clinical diagnoses based on the one-year rule and varied symptom onset. Previously, degeneration of the locus coeruleus (LC) and dorsal raphe nucleus (DRN) in LBD has been well established. However, the precise relationship between underlying neuropathology and clinical presentation remains to be determined. Methods: Immunohistochemical and image analysis techniques have been performed to examine neuronal loss and protein pathologies in the noradrenergic and serotonergic systems of 20 PD, 20 PD-MCI, 20 PDD, 20 DLB cases and 20 controls. RNAscope technology was used to decipher the role of cell-surface receptors in LBD pathophysiology. Possible associations between administration of pharmacological agents with LBD pathology and disease duration was also examined. Results: The hippocampus, thalamus and cingulate cortex - crucial components of the Papez circuit - were most affected by the proteinopathies, particularly deposition that correlated with the onset of some DLB symptomatology and non-motor symptoms. LC noradrenergic neurons were reduced in LBD compared to PD. The 5-HT2A receptor seemed to be more abundant than the α2A-adrenergic receptor (AR) and serotonin transporter (SERT) in the frontal cortex of a PD patient than a PDD or DLB patient. Conclusion: LBD phenotypes may be differentiated through their limbic involvement in the Papez circuit, where α-syn accumulation may contribute to non-motor symptoms. The behaviour of each protein type may be extremely heterogenous within each region of the noradrenergic and serotonergic systems, such that it correlates with the onset of different symptoms. There may be lower expression of receptors in LBD than PD patients, perhaps due to end-stage disease and more widespread degeneration. Hence, this study may have provided further insights into LBD pathophysiology and possibly assist clinical trials in future therapeutic interventions.Open Acces

On the role of the human amygdala : Mapping functions and individual variations using fMRI

Decades of research have demonstrated a role for the amygdala in various psychiatric and neurological disorders. Still, the functional role of this brain structure and the biological mechanisms causing individual variation in amygdala function is not fully elucidated. In the present thesis we investigated a new and alternative role for the human amygdala in encoding or calculation of an event’s relevance. Amygdala activity was measured using functional MRI while subjects performed tasks encompassing high and low relevant stimuli. Secondly, we searched for biological mechanisms, i.e. gene variants, causing individual variation in amygdala functional activity by combining genome-wide data and functional imaging phenotypes from a Norwegian sample. We have made novel and important findings which imply that amygdala is important for relevance detection. Futher, we found a genome-wide significant association with a gene variant possibly affecting the expression of monoamines (i.e. dopamine and noradrenalin) within the amygdala. Thus, the individual’s response to relevant events may depend on genetic variation within monoaminergic signaling pathways. Disproportional encoding or calculation of relevance may be an important component in the observed social and cognitive impairments among patients with amygdala pathology. If so, one could speculate that pharmacological agents, which normalize amygdala function, may also reduce these impairments

Using whole exome sequencing data to elucidate the role of structural variation in schizophrenia

Large, rare structural variants (SVs) have consistently been shown to confer liability for schizophrenia. However, almost all previous studies have been based on data derived from genotyping microarrays, which can only be used to detect a small number of SV types and have limited utility for identifying variants at the smaller end of the size spectrum (<100kb). Therefore, I assessed whether data derived from whole exome sequencing (WES) can be used to identify SVs in schizophrenia that have hitherto gone undetected. To do this, I applied two structural variant callers, CLAMMS and InDelible, to the WES data of two in-house samples for which SVs had previously been called using array data. As each caller mines a different aspect of WES data, they are sensitive to different types and sizes of SVs. The first WES dataset I applied these methods to is derived from a trios sample consisting of 616 schizophrenia probands and their parents. Both callers identified de novo SVs that were not detected in the array data, some of which overlapped genes that have been implicated in previous studies of schizophrenia or are plausible candidate risk genes. The second dataset was generated from 927 schizophrenia cases who have been extensively tested for cognitive ability. Subsets of small (<100kb), rare SVs generated by both callers were found to be associated with cognitive deficits, indicating that SVs previously undetected in the array data are implicated in schizophrenia symptomology. My thesis therefore provides evidence that WES data can be used to detect SVs under-reported in the literature that may have a role in schizophrenia

Central and peripheral molecular profiling of sex differences in schizophrenia, major depressive disorder, and controls

Schizophrenia and major depressive disorder (MDD) are disabling, poorly understood conditions with inadequate diagnostic and therapeutic technologies. They are characterized by clinical and epidemiological heterogeneity between male and female patients. Females have a higher prevalence of MDD, and in schizophrenia males have a higher incidence, earlier onset, more negative symptoms, and a worse prognosis and response to antipsychotic medication in schizophrenia. A better understanding of sex differences in these patients is needed to provide insight into biological mechanisms in these conditions and develop better diagnostic tools and therapies for males and females. This thesis sought to examine molecular sex differences in control subjects and in schizophrenia and MDD patients by profiling serum and prefrontal cortex (PFC) brain tissue. Multiplex immunoassay and microarray technologies were used to measure concentrations of proteins and small molecules in serum and gene expression in brain, respectively. Extensive differences in serum molecular concentrations were found between males and females experiencing a menstrual cycle, using oral contraceptives, and after menopause. These were involved in immune, metabolic, and endocrine processes and may play a role in driving sex differences in susceptibility and other characteristics of mental disorders. Furthermore, the hypothalamic-pituitary-gonadal (HPG) axis was altered in MDD and first onset, antipsychotic naive schizophrenia patients. Biological processes involving response to steroid hormone stimulus and estradiol were enriched among differentially expressed genes in the PFC in schizophrenia. However, investigation of sex-dependent differences in PFC gene expression in schizophrenia was limited by the small number of female samples available. Finally, higher serum concentrations of a number of immune and inflammatory molecules were associated with MDD and schizophrenia in males only and this may have consequences for immune hypotheses of these disorders. The research undertaken in this thesis has advanced the understanding of the mechanisms potentially involved in schizophrenia and MDD in males and females. Furthermore, sex and female hormonal status should routinely be taken into account during design and analysis of psychiatric molecular data to increase the reproducibility of such studies. This research shows that failing to consider these in biomarker studies can result in a high proportion of false positive findings and obscure important sex-dependent markers of mental disorders