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BackgroundClozapine 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.AimsTo examine the clinical characteristics of clozapine-induced myocarditis and to identify which signs and symptoms distinguish true myocarditis from other clozapine adverse effects.MethodA 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.ResultsOf 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.ConclusionsSuspicion 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.

Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model’s sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.

Artificial intelligence (AI) has been developing in a variety of methods over the past decade. However most AI experts worried to build a deep or wide network because the accuracy of AI models depends heavily on the depth of the network. In general deep and wide networks are better at learning than those that are less deep and wide and wide. On the other hand deeper networks are more complex and have many disadvantages such as computational cost and system specification dependency. We propose a novel method to improve the average recall rate for small objects in the deep convolutional network in the paper. The proposed method added pre-processing layer before the network rather than stacking the networks deeper or wide. The presented pre-processing layer consists of two major parts: up-sampling and down-sampling of the data. The overall objective of up-sampling and down-sampling is to enhance the resolution of small objects in the input image. The pre-processing network improves the average recall rate of the base network to 3.56%. This experiment result depicts that the proposed method outperforms the small object detection performance. CCS CONCEPTS • Computing methodologies • Object detection

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.

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.

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.

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.

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.

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.

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.

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.

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.

Background Polypharmacy is common in real clinical practice and in pharma-sponsored clinical trials. Chlorpromazine equivalents do not take into account pharmacodynamic interactions of drug combinations. If there is a sufficiently deep calibration set available, bio-informatics approaches can build classifiers for clinical phenotypes. However, this is not always the case which severely limits the generalizability of the predictions. Methods We applied a mechanism-based computer model of a cortico-striatal-thalamocortical loop of the dorsal motor circuit that has been calibrated with clinical data on the prevalence of extrapyramidal symptoms after antipsychotic treatment in schizophrenia patients and therapeutic interventions in Parkinson’s patients[1]. The Quantitative Systems Pharmacology (QSP) model is based on the appropriate connections between basal ganglia regions and consists of 220 neurons (8 different cell types), 3500 synapses and implementations of 32 CNS active targets, based on their unique locations and coupling with intracellular pathways. Modulation of the various CNS targets were calculated on simulating the competition between the endogenous neurotransmitter and the two drugs at their appropriate concentrations and affinity. The model was challenged to blindly predict the extrapyramidal symptoms liability of 1,124 patients prescribed two antipsychotics for six or more months (772 unique combinations). Anonymized data were derived from South London and Maudsley NHS Foundation Trust (SLAM) electronic health records (EHR). Extrapyramidal side effects were captured and identified using a combination of Natural Language Processing and a bespoke algorithm [2]. Only names and doses of the two drugs were made available without any calibration set. Results Blind prediction of the outcomes using a Receiver Operating Characteristic curve with the QSP model resulted in an Area-Under-the Curve of 0.64 (p

Background: Understanding the drivers of heterogeneous progression in dementia has huge implications for recruitment to clinical trails and care planning. One way to investigate the factors contributing to this heterogeneity is to stratify subjects by similar patterns of change and compare the characteristics of the sub-populations identified. We applied this approach to routinely collected electronic health records from the South London and Maudesly NHS Foundation Trust (SLAM). Methods: This retrospective study includes 3441 patients with at least three MMSE scores recorded and available for research through the SLAM clinical record interactive search system (CRIS). A Latent Class Growth Analysis was used to identify key trajectories of decline. Information on age, gender, ethnicity, qualification levels, cohabiting status, retirement status, Health of Nation Outcome Scales (HoNOS) and medications were obtained to describe characteristics of the identified trajectory sub-populations in a multivariable multinomial regression analysis. Results: We identified six trajectories of cognitive decline. Four of these trajectories differed in initial MMSE score, and showed increased rate of decline with lower initial MMSE. Two trajectories had very similar initial MMSE scores but differed in the rate of decline. Exploring these trajectories further, the severity of cognitive problems at baseline and prescription of Donepezil and Amlodipine was significantly higher in the slower declining trajectory. In the faster declining trajectory, severity of behavioral problems and prescription of sertraline was significantly higher. Conclusions: Most of the trajectories differed by initial MMSE and thus likely represented patients at different disease stages, however differences in behavioral problems, antihypertensive, antidepressant and dementia medication prescription may be indicators of future rate of cognitive decline. Further information is required on depression and hypertensive comorbidities to explore these findings in greater detail.

Understanding the interplay among the multiple factors leading to Adverse Drug Reactions (ADRs) is crucial to increasing drug effectiveness, individualising drug therapy and reducing incurred cost. In this paper, we propose a flexible encoding mechanism that can effectively capture the dynamics of multiple medication episodes of a patient at any given time. We enrich the encoding with a drug ontology and patient demographics data and use it as a base for an ADR prediction model. We evaluate the resulting predictive approach under different settings using real anonymised patient data obtained from the EHR of the South London and Maudsley (SLaM), the largest mental health provider in Europe. Using the profiles of 38,000 mental health patients, we identified 240,000 affirmative mentions of dry mouth, constipation and enuresis and 44,000 negative ones. Our approach achieved 93 % prediction accuracy and 93 % F-Measure.

Monomorphic adenoma is a rare benign salivary gland epithelial tumor representing

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.

Schwannoma is a slow-growing benign tumor that is derived from the Schwann cells. It presents as a well-defined radiolucency in the soft tissues where it commonly occurs, and it is also found in the bones. Although schwannoma occurs as a single lesion, it presents as multiple lesion when associated with neurofibromatosis. The most preferred treatment is the conservative surgical removal. The rate of recurrence and malignant transformation are not seen very often. The histological patterns of the lesions reveal that they are well encapsulated and consist of spindle cells showing either an Antoni A (spindle cells arranged in palisaded whorls and waves) or Antoni B (spindle cells with more haphazard appearance). In this article, we review the literature on intraoral schwannoma and describe a case occurring on the lower lip of a 30-year-old male patient.

Aims and Objectives: To successfully evaluate the difficulty of third molar extraction prior to the surgical procedure, clinical and radiographic findings must be taken into account. The purpose of this research oriented study was to evaluate the importance of variables (patient variables, dental and operative variables) such as radio graphically assessed dental factors i.e., tooth morphology, position of the tooth, proximity to the inferior alveolar canal which are of clinical significance posing difficulty in removal of the impacted third molars. Materials and Methods: This prospective study was conducted in 3 years period (January 2012-December 2014) on 200 patients (140 males, 60 females) who had undergone third molar surgery under local anesthesia at Oral and Maxillofacial Surgery Department, Tatyasaheb Kore Dental College and Research Centre, Kolhapur, Maharashtra, India. Certain variables such as preoperative radiographic assessment, surgeon′s perspective were assessed accordingly. A variety of data was gathered for each patient including age, gender of the patient, bone impaction, unfavorable root formation, operation time, etc. surgical difficulty was evaluated preoperatively using a parant scale. At the end of each surgery, the clinician recorded the technique used and the duration of each operation. Results and Observations: A total of 200 impacted third molars were removed surgically (140 males, 60 females) over a 3 years period and it was observed that increased age, gender predisposition, unfavorable root pattern, depth of impaction, close relationship to inferior alveolar nerve increased the operative time which was statistically significant. The mean operative time was 37 ± 5 minutes. Conclusion: This study demonstrates that preoperative radiological assessment should be taken into account while planning difficult third molar surgery. Although variables such as patient parameters, dental and surgeon′s parameters should be evaluated accordingly.

Supplemental material, jop-2018-3453-File002 for Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare by Giouliana Kadra, Athan Spiros, Hitesh Shetty, Ehtesham Iqbal, Richard D Hayes, Robert Stewart and Hugo Geerts in Journal of Psychopharmacology

Supplemental material, jop-2018-3453-File002 for Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare by Giouliana Kadra, Athan Spiros, Hitesh Shetty, Ehtesham Iqbal, Richard D Hayes, Robert Stewart and Hugo Geerts in Journal of Psychopharmacology