Self-initialized active contours for microscopic cell image segmentation.

Level set models are suitable for processing topological changes in different regions of images while performing segmentation. Active contour models require an empirical setting for initial parameters, which is tedious for the end-user. This study proposes an incremental level set model with the automatic initialization of contours based on local and global fitting energies that enable it to capture image regions containing intensity corruption or other light artifacts. The region-based area and the region-based length terms use signed pressure force (SPF) to strengthen the balloon force. SPF helps to achieve a smooth version of the gradient descent flow in terms of energy minimization. The proposed model is tested on multiple synthetic and real images. Our model has four advantages: first, there is no need for the end user to initialize the parameters; instead, the model is self-initialized. Second, it is more accurate than other methods. Third, it shows lower computational complexity. Fourth, it does not depend on the starting position of the contour. Finally, we evaluated the performance of our model on microscopic cell images (Coelho et al., in: 2009 IEEE international symposium on biomedical imaging: from nano to macro, IEEE, 2009) to confirm that its performance is superior to that of other state-of-the-art models.

Motor signs in Alzheimer's disease and vascular dementia: Detection through natural language processing, co-morbid features and relationship to adverse outcomes.

BACKGROUND: Motor signs in patients with dementia are associated with a higher risk of cognitive decline, institutionalisation, death and increased health care costs, but prevalences differ between studies. The aims of this study were to employ a natural language processing pipeline to detect motor signs in a patient cohort in routine care; to explore which other difficulties occur co-morbid to motor signs; and whether these, as a group and individually, predict adverse outcomes. METHODS: A cohort of 11,106 patients with dementia in Alzheimer's disease, vascular dementia or a combination was assembled from a large dementia care health records database in Southeast London. A natural language processing algorithm was devised in order to establish the presence of motor signs (bradykinesia, Parkinsonian gait, rigidity, tremor) recorded around the time of dementia diagnosis. We examined the co-morbidity profile of patients with these symptoms and used Cox regression models to analyse associations with survival and hospitalisation, adjusting for twenty-four potential confounders. RESULTS: Less than 10% of patients were recorded to display any motor sign, and tremor was most frequently detected. Presence of motor signs was associated with younger age at diagnosis, neuropsychiatric symptoms, poor physical health and higher prescribing of psychotropics. Rigidity was independently associated with a 23% increased mortality risk after adjustment for confounders (p = 0.014). A non-significant trend for a 15% higher risk of hospitalisation was detected in those with a recorded Parkinsonian gait (p = 0.094). CONCLUSIONS: With the exception of tremor, motor signs appear to be under-recorded in routine care. They are part of a complex clinical picture and often accompanied by neuropsychiatric and functional difficulties, and thereby associated with adverse outcomes. This underlines the need to establish structured examinations in routine clinical practice via easy-to-use tools.

Clozapine-induced myocarditis: electronic health register analysis of incidence, timing, clinical markers and diagnostic accuracy.

BACKGROUND: Clozapine is associated with increased risk of myocarditis. However, many common side-effects of clozapine overlap with the clinical manifestations of myocarditis. As a result, there is uncertainty about which signs, symptoms and investigations are important in distinguishing myocarditis from benign adverse effects of clozapine. Clarity on this issue is important, since missing a diagnosis of myocarditis or discontinuing clozapine unnecessarily may both have devastating consequences. AIMS: To examine the clinical characteristics of clozapine-induced myocarditis and to identify which signs and symptoms distinguish true myocarditis from other clozapine adverse effects. METHOD: A retrospective analysis of the record database for 247 621 patients was performed. A natural language processing algorithm identified the instances of patients in which myocarditis was suspected. The anonymised case notes for the patients of each suspected instance were then manually examined, and those whose instances were ambiguous were referred for an independent assessment by up to three cardiologists. Patients with suspected instances were classified as having confirmed myocarditis, myocarditis ruled out or undetermined. RESULTS: Of 254 instances in 228 patients with suspected myocarditis, 11.4% (n = 29 instances) were confirmed as probable myocarditis. Troponin and C-reactive protein (CRP) had excellent diagnostic value (area under the curve 0.975 and 0.896, respectively), whereas tachycardia was of little diagnostic value. All confirmed instances occurred within 42 days of clozapine initiation. CONCLUSIONS: Suspicion of myocarditis can lead to unnecessary discontinuation of clozapine. The 'critical period' for myocarditis emergence is the first 6 weeks, and clinical signs including tachycardia are of low specificity. Elevated CRP and troponin are the best markers for the need for further evaluation.

The side effect profile of Clozapine in real world data of three large mental health hospitals.

OBJECTIVE: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. MATERIAL AND METHODS: We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where possible, we compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER). RESULTS: Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) our chi-square tests show a significant association between most of the ADRs and smoking status and hospital admission, and some in gender, ethnicity and age groups in all trusts hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs. CONCLUSION: A better understanding of how drugs work in the real world can complement clinical trials.

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach.

BACKGROUND: Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results. OBJECTIVE: The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. METHODS: We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste," which collectively impede efficient model reuse. We propose a phenotype embedding-based approach to minimize these sources of waste without the need for labelled data from new settings. RESULTS: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in "blind" model-adaptation approaches. CONCLUSIONS: Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.

BACKGROUND: Computer-modelling approaches have the potential to predict the interactions between different antipsychotics and provide guidance for polypharmacy. AIMS: To evaluate the accuracy of the quantitative systems pharmacology platform to predict parkinsonism side-effects in patients prescribed antipsychotic polypharmacy. METHODS: Using anonymized data from South London and Maudsley NHS Foundation Trust electronic health records we applied quantitative systems pharmacology, a neurophysiology-based computer model of humanized neuronal circuits, to predict the risk for parkinsonism symptoms in patients with schizophrenia prescribed two concomitant antipsychotics. The performance of the quantitative systems pharmacology model was compared with the performance of simple parameters such as: combination of affinity constants (1/Ksum); sum of D2R occupancies (D2R) and chlorpromazine equivalent dose. RESULTS: We identified 832 patients with schizophrenia who were receiving two antipsychotics for six or more months, between 1 January 2007 and 31 December 2014. The area under the receiver operating characteristic (AUROC) curve for the quantitative systems pharmacology model was 0.66 ( p = 0.01), while AUROCs for D2R, 1/Ksum and chlorpromazine equivalent dose were 0.52 ( p = 0.350), 0.53 ( p = 0.347) and 0.52 ( p = 0.330) respectively. CONCLUSION: Our results indicate that quantitative systems pharmacology has the potential to predict the risk of parkinsonism associated with antipsychotic polypharmacy from minimal source information, and thus might have potential decision-support applicability in clinical settings.

Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records.

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials.

ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records.

Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals. The pipeline uses characteristics specific to Psychiatric EHRs to guide the annotation process, and distinguishes: a) the temporal value associated with the ADE mention (whether it is historical or present), b) the categorical value of the ADE (whether it is assertive, hypothetical, retrospective or a general discussion) and c) the implicit contextual value where the status of the ADE is deduced from surrounding indicators, rather than explicitly stated. We manually created the rulebase in collaboration with clinicians and pharmacists by studying ADE mentions in various types of clinical notes. We evaluated the open-source Adverse Drug Event annotation Pipeline (ADEPt) using 19 ADEs specific to antipsychotics and antidepressants medication. The ADEs chosen vary in severity, regularity and persistence. The average F-measure and accuracy achieved by our tool across all tested ADEs were 0.83 and 0.83 respectively. In addition to annotation power, the ADEPT pipeline presents an improvement to the state of the art context-discerning algorithm, ConText.

Trajectories of dementia-related cognitive decline in a large mental health records derived patient cohort.

BACKGROUND: Modeling trajectories of decline can help describe the variability in progression of cognitive impairment in dementia. Better characterisation of these trajectories has significant implications for understanding disease progression, trial design and care planning. METHODS: Patients with at least three Mini-mental State Examination (MMSE) scores recorded in the South London and Maudsley NHS Foundation Trust Electronic Health Records, UK were selected (N = 3441) to form a retrospective cohort. Trajectories of cognitive decline were identified through latent class growth analysis of longitudinal MMSE scores. Demographics, Health of Nation Outcome Scales and medications were compared across trajectories identified. RESULTS: Four of the six trajectories showed increased rate of decline with lower baseline MMSE. Two trajectories had similar initial MMSE scores but different rates of decline. In the faster declining trajectory of the two, a higher incidence of both behavioral problems and sertraline prescription were present. CONCLUSIONS: We find suggestive evidence for association of behavioral problems and sertraline prescription with rate of decline. Further work is needed to determine whether trajectories replicate in other datasets.

Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register.

OBJECTIVES: Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify instances of Adverse Drug Events (ADEs) from free text psychiatric EHRs. METHODS: We used the GATE Natural Language Processing (NLP) software to mine instances of ADEs from free text content within the Clinical Record Interactive Search (CRIS) system, a de-identified psychiatric case register developed at the South London and Maudsley NHS Foundation Trust, UK. The tool was built around a set of four movement disorders (extrapyramidal side effects [EPSEs]) related to antipsychotic therapy and rules were then generalised such that the tool could be applied to additional ADEs. We report the frequencies of recorded EPSEs in patients diagnosed with a Severe Mental Illness (SMI) and then report performance in identifying eight other unrelated ADEs. RESULTS: The tool identified EPSEs with >0.85 precision and >0.86 recall during testing. Akathisia was found to be the most prevalent EPSE overall and occurred in the Asian ethnic group with a frequency of 8.13%. The tool performed well when applied to most of the non-EPSEs but least well when applied to rare conditions such as myocarditis, a condition that appears frequently in the text as a side effect warning to patients. CONCLUSIONS: The developed tool allows us to accurately identify instances of a potential ADE from psychiatric EHRs. As such, we were able to study the prevalence of ADEs within subgroups of patients stratified by SMI diagnosis, gender, age and ethnicity. In addition we demonstrated the generalisability of the application to other ADE types by producing a high precision rate on a non-EPSE related set of ADE containing documents. AVAILABILITY: The application can be found at http://git.brc.iop.kcl.ac.uk/rmallah/dystoniaml.