Cerebral Embolism in the Michael Reese Stroke Registry

Infarction Secondary to Cerebral Embolism Was Diagnosed in 127 (23.5%) of 540 Patients in the Michael Reese Stroke Registry. Coronary Artery Disease, Atrial Fibrillation, Valvular Heart Disease, Mitral Annulus Calcification, and Cardiomyopathy Were the Commonest Etiologies. Echocardiography Documented a Potential Embolic Source in 7 Patients Without Previously Known Heart Disease and Clarified the Cardiac Pathology in Many of the Patients with Known Heart Disease. the Left Anterior Circulation Was Affected in 48%, Right Anterior in 37%, and Posterior Circulation in 15% of patients. CT Was Abnormal in 71% of the Patients and Was Approximately Equally Helpful in All Locations. Nineteen Percent of Emboli Presented with a Deficit that Was Other Than Maximal at Onset. Concurrent Systemic Embolism Was Unusual (2.3%). Prognosis Was Somewhat Worse Than in Thrombotic Stroke. Grouping of Patients According to Embolic Source (Intra-Arterial, Cardiac, and Uncertain Source) Showed No Differences in Activity at Onset, Early Course, or in Subsequent Course of the Illness

High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years, progress toward making high throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping of physician notes.Comment: Submitted to IEEE EMBS Summer conference 202

Current Concepts of Cerebrovascular Disease - Stroke: Stroke and Drug Abuse

This Review Summarizes Available Information Concerning Cerebral Vascular Complications of the Most Commonly Abused Substances and Discusses Possible Mechanisms of Vascular Injury and Cerebral Damage. Although Alcohol is Frequently Abused and May Have Important Cerebrovascular Effects, its Consideration is Beyond the Scope of This Review

Computerised Evaluation of Cognitive and Motor Function

In This Paper, We Present a Clinical Study of Computerized Tracking in the Evaluation of Cognitive and Motor Function. We Investigate its Use in the Assessment of Effectiveness of Antiepileptic Drugs (AEDs) as Well as in the Process of Following the Progress of Alzheimer\u27s Disease (AD). to Simplify the Experiments, We Introduce Real-Time Adaptation of the Target Speed. in the Study with Epileptic Patients, Three Result Groups Are Compared: Blood Levels of AEDs, Scores on Standard Neuropsychological Tests, and Scores on Computerized Tracking and Reaction Time Tests. It is Found that the Computerized Tests Are Repeatable, Reliable and Sensitive and May Therefore Be Useful in the Evaluation of Epilepsy Treatment. for Example, While the Blood Levels Associated with AEDs Lie in the Therapeutic Range, Variations in the Optimal Speed (OS) between 0.9 and 1.1 (Expressed in Relative Units) Are Recorded. to Significantly Simplify the Protocol for AD Patients While Preserving its Main Features, We Introduce Signal-Processing Techniques into the Data Analysis. Local Signal Property Characteristics for AD Are Found Which Indicate that the Preview Tracking of an AD Patient is Similar to the Non-Preview Tracking of a Healthy Control. This Result is Expected Since the Working Memory, Which is Involved in Movement Planning, is Impaired in AD. in Non-Preview Tracking, Healthy Control Subjects Are Mostly in Tracking Mode 1 and Have a Mean Mode Duration of 600 Ms. in Preview Tracking, AD Patients Are Mostly in Mode 2 with a Mean Mode Duration of 600 Ms

The visualization of Orphadata neurology phenotypes

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of neurologic disease is represented as a list of concepts of variable length where the concepts are selected from a restricted ontology. Visualizations of these concept lists are not provided. We address this limitation by using subsumption to reduce the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length vectors. Phenotype vectors were aggregated into matrices and visualized as heat maps that allowed side-by-side disease comparisons. Individual diseases (representing a row in the matrix) were visualized as word clouds. We illustrate the utility of this approach by visualizing the neuro-phenotypes of 32 dystonic diseases from Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds

Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records

The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting signs and symptoms from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators

High Throughput Neurological Phenotyping with MetaMap

The phenotyping of neurological patients involves the conversion of signs and symptoms into machine readable codes selected from an appropriate ontology. The phenotyping of neurological patients is manual and laborious. MetaMap is used for high throughput mapping of the medical literature to concepts in the Unified Medical Language System Metathesaurus (UMLS). MetaMap was evaluated as a tool for the high throughput phenotyping of neurological patients. Based on 15 patient histories from electronic health records, 30 patient histories from neurology textbooks, and 20 clinical summaries from the Online Mendelian Inheritance in Man repository, MetaMap showed a recall of 61-89%, a precision of 84-93%, and an accuracy of 56-84% for the identification of phenotype concepts. The most common cause of false negatives (failure to recognize a phenotype concept) was an inability of MetaMap to find concepts that were represented as a description or a definition of the concept. The most common cause of false positives (incorrect identification of a concept in the text) was a failure to recognize that a concept was negated. MetaMap shows potential for high throughput phenotyping of neurological patients if the problems of false negatives and false positives can be solved