scholarly journals M99. INVESTIGATING THE BEST PREDICTIVE CLINICAL FEATURES OF ANTI-N-METHYL-D-ASPARTATE RECEPTOR (NMDAR) ENCEPHALITIS IN THE 2010 AUSTRALIAN NATIONAL SURVEY OF HIGH IMPACT PSYCHOSIS (SHIP) COHORT

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S172-S173
Author(s):  
Susan Rossell ◽  
Denny Meyer ◽  
Cyndi Shannon Weickert ◽  
Andrea Phillipou ◽  
Cherrie Galletly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical History ◽  
Clinical Features ◽  
Clinical Data ◽  
Psychiatric Symptoms ◽  
Learning Algorithm ◽  
Psychotic Symptoms ◽  
Gradient Boosting ◽  
Aspartate Receptor ◽  
Nmdar Encephalitis

Abstract Background Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, a recently reported autoimmune disorder, can be mistakenly diagnosed as a psychotic disorder, especially schizophrenia, as patients can present with prominent psychotic symptoms, in particular persecutory ideation, hallucinations and disturbed speech. In this study we used machine learning of the clinical data in a large cohort of persons with a positive psychosis history to ascertain whether we could predict NMDAR-positive cases, and which variables most accurately distinguished between NMDAR-positive and -negative cases. Methods SHIP collected nationally representative data from 1825 individuals with a psychotic illness. Plasma samples were available for n=472. To investigate the prevalence of NMDAR autoantibodies a recombinant indirect immunofluorescence test was performed (EuroImmun AG, Lübeck, Germany), with NMDAR transfected human embryonic kidney (HEK) 293 cells quantified using NIS Elements software. NMDAR-positive cases were estimated. Gradient boosting machine learning (the data were randomly split: 60% for initial ascertainment and 40% for validation) was subsequently performed using the clinical data available: 120 variables in total across various domains of sociodemographic, medical history, psychiatric diagnosis and current psychiatric symptoms. Only the variables found to have significant (or near significant) association with being NMDAR-positive were used to develop rules for identifying cases. Results There were 38 NMDAR-positive cases. They were more likely to be associated with a schizophrenia /schizoaffective and a depressive psychosis diagnosis, and less likely to be associated with a bipolar diagnosis, than antibody-negative cases. They were also more likely to be associated with a single episode with good recovery, and with anxiety symptoms and dizziness in the prior 12 months (which included light headedness, feeling faint and unsteady). For the present state symptoms, restricted affect was more likely to be present whereas poverty of speech was rare. Initial insomnia and a medical history that included epilepsy were not present for any of the NMDAR-positive cases. The machine learning algorithm was able to successfully classify 94% of cases to the correct antibody group. Discussion In this significant Australian epidemiological cohort, we have identified key clinical features associated with anti-NMDAR encephalitis, including diagnosis, and symptoms and clinical course. The novel and insightful analyses afforded by using machine learning should be replicated in other samples to confirm the important clinical findings reported in the current work.

scholarly journals Who Was at Risk for COVID-19 Late in the US Pandemic? Insights From a Population Health Machine Learning Model

2021 ◽  
Author(s):  
Elijah A. Adeoye ◽  
Yelena Rozenfeld ◽  
Jennifer Beam ◽  
Karen Boudreau ◽  
Emily Cox ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medical History ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
The United States ◽  
Gradient Boosting ◽  
Latino Community ◽  
Machine Learning Model ◽  
The Us ◽  
History Of

Abstract Background: Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the United States. The purpose of this study was estimate likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on health care data.Methods: Patient records were extracted for all COVID-19 nasal swab PCR tests performed within the Providence St. Joseph Health system from February to October of 2020. Several different machine learning models were tested to evaluate effects of sociodemographic, environmental, and medical history factors on risk of initial COVID-19 infection.Results: A total of 316,599 participants were included in this study and approximately 7.7% (n = 24,358) tested positive for COVID-19. A gradient boosting model, LightGBM (LGBM), predicted risk of initial infection with an area under the receiver operating characteristic curve of 0.819. Factors that predicted infection were cough, fever, being a member of the Hispanic or Latino community, being Spanish speaking, having a history of diabetes or dementia, and living in a neighborhood with housing insecurity. Conclusion: A model trained on sociodemographic, environmental, and medical history data performed well in predicting risk of a positive COVID-19 test. This model could be used to tailor education, public health policy, and resources for communities that are at the greatest risk of infection.

Cocaine Dependence Produce Psychiatric Symptoms - a Case Report

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
M. Stankovic ◽  
S. Vucetic-Arsic ◽  
S. Alcaz ◽  
J. Cvejic
Keyword(s):  
Clinical Features ◽  
Male Patient ◽  
Psychiatric Symptoms ◽  
Cocaine Dependence ◽  
Psychotic Symptoms ◽  
Personal Contact ◽  
Outpatient Unit ◽  
Special Hospital ◽  
First Time ◽  
The Brain

Aim:We want to present a polymorphic clinical features like: hallutinations, paranoid ideas, agitation and violence as a result of prolonged cocaine intranasal consumption.Methods:We exposed a 30-year old male patient with ICD-X diagnostic criteria for cocaine dependence (intranasal consumption) that treated in the outpatient unit of Special Hospital of Addicitons, Belgrade, Serbia from April to July 2008. We used the medical records, psychical examination, psychiatric interwievs, standard blood sampling and cocaine urine detections sample (positive).Results:Observations a specific and polymorphic clinical features with presence of psychotic symptoms after cocaine consumptions in our male patient, for the first time after 5 years of cocaine dependence: auditory hallucinations (two- voice speakers), paranoid persecution ideas and suspiciousness, agitation with appearance of vegetative symptomatology (palpitations, sweating, pupil dilatation), extremely violence behavior to other people, complete social reductions (“armed to the outside world”, refused any personal contact and isolated from friends and family, permanent outdoor checking). There was an intensive fear too and impaired judgment.Conclusions:Permanent cocaine consumption can result with produce a numerous of psychiatric symptoms and syndromes as our experience does. It is similar to the findings of other studies and papers reviewed. It is suppose that cocaine has numerous effects on important neurotransmitters in the brain, such as increase as well as the release of dopamine and it related with aggressiveness, hallucinations and other psychiatric symptoms.

Frequency and temporal sequence of clinical features in adults with anti-NMDA receptor encephalitis presenting with psychiatric symptoms

2018 ◽  
Vol 49 (16) ◽  
pp. 2709-2716 ◽  
Author(s):  
Ronald J. Gurrera
Keyword(s):  
Nmda Receptor ◽  
Clinical Features ◽  
Psychiatric Symptoms ◽  
Autoimmune Encephalitis ◽  
Signs And Symptoms ◽  
Nmda Receptor Encephalitis ◽  
Nmdar Encephalitis ◽  
Unexplained Fever ◽  
Study Inclusion

AbstractBackgroundAnti-NMDA receptor (NMDAr) encephalitis is the most common autoimmune encephalitis in adults. It mimics psychiatric disorders so often that most patients are initially referred to a psychiatrist, and many are misdiagnosed. Without prompt and effective treatment, patients are likely to suffer a protracted course with significant residual disability, or death. This study focuses on the frequency and chronology of salient clinical features in adults with anti-NMDAr encephalitis who are likely to be first evaluated by a psychiatrist because their presentation suggests a primary psychiatric disorder.MethodsA systematic search of PubMed and EMBASE databases identified published reports of anti-NMDAr encephalitis associated with prominent behavioral or psychiatric symptoms. After eliminating redundancies, the frequencies and relative timing of clinical features were tabulated. Signs and symptoms were assigned temporal ranks based on the timing of their first appearance relative to the first appearance of other signs and symptoms in each patient; median ranks were used to compare temporal sequencing of both individual features and major symptom domains.ResultsTwo hundred thirty unique cases (185 female) met study inclusion criteria. The most common features were seizures (60.4%), disorientation/confusion (42.6%), orofacial dyskinesias (39.1%), and mutism/staring (37.4%). Seizures, fever, and cognitive dysfunction were often the earliest features to emerge, but psychiatric features predominated and sequencing varied greatly between individuals.ConclusionsClinicians should consider anti-NMDAr encephalitis when new psychiatric symptoms are accompanied by a recent viral prodrome, seizures or unexplained fever, or when the quality of the psychiatric symptoms is unusual (e.g. non-verbal auditory hallucinations).

scholarly journals Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design

2018 ◽  
Author(s):  
Jatin Kumar ◽  
Qianxiao Li ◽  
Karen Y.T. Tang ◽  
Tonio Buonassisi ◽  
Anibal L. Gonzalez-Oyarce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Point ◽  
Disordered Systems ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inverse Design ◽  
Gradient Boosting ◽  
Multiple Length Scales ◽  
The Status

<div><div><div><p>Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.</p></div></div></div>

scholarly journals Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammad Nahid Hossain ◽  
Mohammad Helal Uddin ◽  
K. Thapa ◽  
Md Abdullah Al Zubaer ◽  
Md Shafiqul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Learning Algorithm ◽  
Negative Impact ◽  
Gradient Boosting ◽  
Feature Selection Technique ◽  
Activity Data ◽  
Physical Data ◽  
Machine Learning Model

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

scholarly journals Case Report: Severe Adolescent Major Depressive Syndrome Turns Out to Be an Unusual Case of Anti-NMDA Receptor Encephalitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Alexander Moldavski ◽  
Holger Wenz ◽  
Bettina E. Lange ◽  
Cathrin Rohleder ◽  
F. Markus Leweke
Keyword(s):  
Cognitive Deficits ◽  
Psychiatric Symptoms ◽  
Previous History ◽  
Rapid Onset ◽  
Depressive Syndrome ◽  
Psychotic Symptoms ◽  
Major Depressive ◽  
Abnormal Movements ◽  
Nmdar Encephalitis ◽  
Initial Symptoms

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a neuroinflammatory condition mediated by autoantibodies against the GluN1 subunit of the receptor. Clinically, it is characterized by a complex neuropsychiatric presentation with rapidly progressive psychiatric symptoms, cognitive deficits, seizures, and abnormal movements. Isolated psychiatric manifestations of anti-NMDAR encephalitis are rare and usually dominated by psychotic symptoms. We present a case of an 18-year-old female high school student—without a previous history of psychiatric disorders—with a rapid onset severe depressive syndrome. Surprisingly, we found pleocytosis and anti-NMDAR autoantibodies in the cerebrospinal fluid (CSF), despite an otherwise unremarkable diagnostic workup, including blood test, clinical examination, and cranial magnetic resonance imaging (MRI). After intravenous immunoglobulins treatment, a complete remission of the initial symptoms was observed. In a follow-up 5 years later, the young woman did not experience any relapse or sequelae. Anti-NMDAR encephalitis can present in rare cases as an organic disorder with major depressive symptoms without distinct concomitant psychotic or neurological symptoms. A clinical presentation such as a rapid onset of symptoms, distinct disturbance in the thought process, restlessness, and cognitive deficits should prompt screening for NMDAR- and other neural autoantibodies to rule out this rare but debilitating pathology.

scholarly journals High alert drugs screening using gradient boosting classifier

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pakpoom Wongyikul ◽  
Nuttamon Thongyot ◽  
Pannika Tantrakoolcharoen ◽  
Pusit Seephueng ◽  
Piyapong Khumrin
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Drug Prescription ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Negative Consequences ◽  
Inappropriate Use ◽  
Prescription Errors ◽  
Screening Protocol

AbstractPrescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines were implemented to reduce the errors but were not found to be highly effective. Machine learning driven clinical decision support systems (CDSS) show a potential solution to address this problem. We developed a HAD screening protocol with a machine learning model using Gradient Boosting Classifier and screening parameters to identify the events of HAD prescription errors from the drug prescriptions of out and inpatients at Maharaj Nakhon Chiang Mai hospital in 2018. The machine learning algorithm was able to screen drug prescription events with a risk of HAD inappropriate use and identify over 98% of actual HAD mismatches in the test set and 99% in the evaluation set. This study demonstrates that machine learning plays an important role and has potential benefit to screen and reduce errors in HAD prescriptions.

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

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