Two Algorithms for the Reorganisation of the Problem List by Organ System

Objective Long Problem Lists Can Be Challenging to Use. Reorganization of the Problem List by Organ System is a Strategy for Making Long Problem Lists More Manageable. Methods in a Small-Town Primary Care Setting, We Examined 4950 Unique Problem Lists over 5 Years (24 033 Total Problems and 2170 Unique Problems) from Our Electronic Health Record. All Problems Were Mapped to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) and SNOMED CT Codes. We Developed Two Different Algorithms for Reorganizing the Problem List by Organ System based on Either the ICD-10-CM or the SNOMED CT Code. Results the Mean Problem List Length Was 4.9±4.6 Problems. the Two Reorganization Algorithms Allocated Problems to One of 15 Different Categories (12 Aligning with Organ Systems). 26.2% of Problems Were Assigned to a More General Category of  € Signs and Symptoms\u27 that Did Not Correspond to a Single Organ System. the Two Algorithms Were Concordant in Allocation by Organ System for 90% of the Unique Problems. Since ICD-10-CM is a Monohierarchic Classification System, Problems Coded by ICD-10-CM Were Assigned to a Single Category. Since SNOMED CT is a Polyhierarchical Ontology, 19.4% of Problems Coded by SNOMED CT Were Assigned to Multiple Categories. Conclusion Reorganization of the Problem List by Organ System is Feasible using Algorithms based on Either ICD-10-CM or SNOMED CT Codes, and the Two Algorithms Are Highly Concordant

Deriving Clinical Prediction Rules from Stroke Outcome Research

Background and Purpose. Our Purpose Was to Determine Whether Clinical Prediction Rules Could Be Derived from Current Stroke Outcome Research. Summary of Report. We Reviewed 92 Articles on Stroke Outcome Research to Determine their Suitability for Implementation as a Clinical Prediction Rule. Methodological Problems in Many of These Studies Made Implementation of their Results as a Clinical Prediction Rule Difficult. Conclusions. Implementation of Stroke Outcome Research as Clinical Prediction Rules Would Be Facilitated by Description of Patient Population Demographics; Precise Definitions of Predictor and Outcome Measures; Stratification of Patients by Stroke Mechanism; Use of Adequate Patient Sample Sizes; and Description of the Mathematical Methods Used, Including Coding Schemes, Cut points, Beta Coefficients, Constant Terms, and a Priori Probabilities. © 1991 American Heart Association, Inc

A Neuro-Ontology for the Neurological Examination

Background: The Use of Clinical Data in Electronic Health Records for Machine-Learning or Data Analytics Depends on the Conversion of Free Text into Machine-Readable Codes. We Have Examined the Feasibility of Capturing the Neurological Examination as Machine-Readable Codes based on UMLS Metathesaurus Concepts. Methods: We Created a Target Ontology for Capturing the Neurological Examination using 1100 Concepts from the UMLS Metathesaurus. We Created a Dataset of 2386 Test-Phrases based on 419 Published Neurological Cases. We Then Mapped the Test-Phrases to the Target Ontology. Results: We Were Able to Map All of the 2386 Test-Phrases to 601 Unique UMLS Concepts. a Neurological Examination Ontology with 1100 Concepts Has Sufficient Breadth and Depth of Coverage to Encode All of the Neurologic Concepts Derived from the 419 Test Cases. using Only Pre-Coordinated Concepts, Component Ontologies of the UMLS, Such as HPO, SNOMED CT, and OMIM, Do Not Have Adequate Depth and Breadth of Coverage to Encode the Complexity of the Neurological Examination. Conclusion: An Ontology based on a Subset of UMLS Has Sufficient Breadth and Depth of Coverage to Convert Deficits from the Neurological Examination into Machine-Readable Codes using Pre-Coordinated Concepts. the Use of a Small Subset of UMLS Concepts for a Neurological Examination Ontology Offers the Advantage of Improved Manageability as Well as the Opportunity to Curate the Hierarchy and Subsumption Relationships

Subsumption Reduces Dataset Dimensionality Without Decreasing Performance of a Machine Learning Classifier

When Features in a High Dimension Dataset Are Organized Hierarchically, There is an Inherent Opportunity to Reduce Dimensionality. Since More Specific Concepts Are Subsumed by More General Concepts, Subsumption Can Be Applied Successively to Reduce Dimensionality. We Tested Whether Sub-Sumption Could Reduce the Dimensionality of a Disease Dataset Without Impairing Classification Accuracy. We Started with a Dataset that Had 168 Neurological Patients, 14 Diagnoses, and 293 Unique Features. We Applied Subsumption Repeatedly to Create Eight Successively Smaller Datasets, Ranging from 293 Dimensions in the Largest Dataset to 11 Dimensions in the Smallest Dataset. We Tested a MLP Classifier on All Eight Datasets. Precision, Recall, Accuracy, and Validation Declined Only at the Lowest Dimensionality. Our Preliminary Results Suggest that When Features in a High Dimension Dataset Are Derived from a Hierarchical Ontology, Subsumption is a Viable Strategy to Reduce Dimensionality.Clinical Relevance - Datasets Derived from Electronic Health Records Are Often of High Dimensionality. If Features in the Dataset Are based on Concepts from a Hierarchical Ontology, Subsumption Can Reduce Dimensionality

Subsumption is a Novel Feature Reduction Strategy for High Dimensionality Datasets

High dataset dimensionality poses challenges for machine learning classifiers because of high computational costs and the adverse consequences of redundant features. Feature reduction is an attractive remedy to high dimensionality. Three different feature reduction strategies (subsumption, Relief F, and principal component analysis) were evaluated using four machine learning classifiers on a high dimension dataset with 474 unique features, 20 diagnoses, and 364 instances. All three feature reduction strategies proved capable of significant feature reduction while maintaining classification accuracy. At high levels of feature reduction, the principal components strategy outperformed Relief F and subsumption. Subsumption is a novel strategy for feature reduction if features are organized in a hierarchical ontology

Preface to Computational Intelligence Applications in Medicine and Biology

This special edition of the European Science Journal is devoted to applying computational intelligence methods to solving complex problems in medicine and biology

Visual Form of Alzheimer\u27s Disease and its Response to Anticholinesterase Therapy

In a 60-Year-Old Woman with the Visual Variant of Alzheimer\u27s Disease, Single Photon Emission Computed Tomography Abnormalities Were Most Marked in the Parieto-Occipital Regions of the Brain. after Treatment with Donepezil, Improvement is Noted on Neuropsychological Testing and on Brain SPECT, Including Increased Perfusion (Metabolism) in the Occipital Lobes

Enhanced Neurologic Concept Recognition using a Named Entity Recognition Model based on Transformers

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks to One based on Bidirectional Encoder Representation from Transformers. Models Were Evaluated for Accuracy of Text Span Identification on Three Text Corpora: Physician Notes from an Electronic Health Record, Case Histories from Neurologic Textbooks, and Clinical Synopses from an Online Database of Genetic Diseases. Both Models Performed Best on the Professionally-Written Clinical Synopses and Worst on the Physician-Written Clinical Notes. Both Models Performed Better When Signs and Symptoms Were Represented as Shorter Text Spans. Consistent with Prior Studies that Examined the Recognition of Diseases and Drugs, the Model based on Bidirectional Encoder Representations from Transformers Outperformed the Model based on Convolutional Neural Networks for Recognizing Signs and Symptoms. Recall for Signs and Symptoms Ranged from 59.5% to 82.0% and Precision Ranged from 61.7% to 80.4%. with Further Advances in NLP, Fully Automated Recognition of Signs and Symptoms in Electronic Health Records and the Medical Literature Should Be Feasible

Schizophrenia Classification using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level

Disrupted Functional and Structural Connectivity Measures Have Been Used to Distinguish Schizophrenia Patients from Healthy Controls. Classification Methods based on Functional Connectivity Derived from EEG Signals Are Limited by the Volume Conduction Problem. Recorded Time Series at Scalp Electrodes Capture a Mixture of Common Sources Signals, Resulting in Spurious Connections. We Have Transformed Sensor Level Resting State EEG Times Series to Source Level EEG Signals Utilizing a Source Reconstruction Method. Functional Connectivity Networks Were Calculated by Computing Phase Lag Values between Brain Regions at Both the Sensor and Source Level. Brain Complex Network Analysis Was Used to Extract Features and the Best Features Were Selected by a Feature Selection Method. a Logistic Regression Classifier Was Used to Distinguish Schizophrenia Patients from Healthy Controls at Five Different Frequency Bands. the Best Classifier Performance Was based on Connectivity Measures Derived from the Source Space and the Theta Band.The Transformation of Scalp EEG Signals to Source Signals Combined with Functional Connectivity Analysis May Provide Superior Features for Machine Learning Applications