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.

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.

It's time to change our documentation philosophy: writing better neurology notes without the burnout.

Succinct clinical documentation is vital to effective twenty-first-century healthcare. Recent changes in outpatient and inpatient evaluation and management (E/M) guidelines have allowed neurology practices to make changes that reduce the documentation burden and enhance clinical note usability. Despite favorable changes in E/M guidelines, some neurology practices have not moved quickly to change their documentation philosophy. We argue in favor of changes in the design, structure, and implementation of clinical notes that make them shorter yet still information-rich. A move from physician-centric to team documentation can reduce work for physicians. Changing the documentation philosophy from "bigger is better" to "short but sweet" can reduce the documentation burden, streamline the writing and reading of clinical notes, and enhance their utility for medical decision-making, patient education, medical education, and clinical research. We believe that these changes can favorably affect physician well-being without adversely affecting reimbursement.

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.

Tau kinetics in Alzheimer's disease.

The cytoskeletal protein tau is implicated in the pathogenesis of Alzheimer's disease which is characterized by intra-neuronal neurofibrillary tangles containing abnormally phosphorylated insoluble tau. Levels of soluble tau are elevated in the brain, the CSF, and the plasma of patients with Alzheimer's disease. To better understand the causes of these elevated levels of tau, we propose a three-compartment kinetic model (brain, CSF, and plasma). The model assumes that the synthesis of tau follows zero-order kinetics (uncorrelated with compartmental tau levels) and that the release, absorption, and clearance of tau is governed by first-order kinetics (linearly related to compartmental tau levels). Tau that is synthesized in the brain compartment can be released into the interstitial fluid, catabolized, or retained in neurofibrillary tangles. Tau released into the interstitial fluid can mix with the CSF and eventually drain to the plasma compartment. However, losses of tau in the drainage pathways may be significant. The kinetic model estimates half-life of tau in each compartment (552 h in the brain, 9.9 h in the CSF, and 10 h in the plasma). The kinetic model predicts that an increase in the neuronal tau synthesis rate or a decrease in tau catabolism rate best accounts for observed increases in tau levels in the brain, CSF, and plasma found in Alzheimer's disease. Furthermore, the model predicts that increases in brain half-life of tau in Alzheimer's disease should be attributed to decreased tau catabolism and not to increased tau synthesis. Most clearance of tau in the neuron occurs through catabolism rather than release to the CSF compartment. Additional experimental data would make ascertainment of the model parameters more precise.

Heterogeneity in Blood Biomarker Trajectories After Mild TBI Revealed by Unsupervised Learning.

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.

Subtypes of relapsing-remitting multiple sclerosis identified by network analysis.

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis.

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.

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.

Blood biomarkers for mild traumatic brain injury: a selective review of unresolved issues.

BACKGROUND: The use of blood biomarkers after mild traumatic brain injury (mTBI) has been widely studied. We have identified eight unresolved issues related to the use of five commonly investigated blood biomarkers: neurofilament light chain, ubiquitin carboxy-terminal hydrolase-L1, tau, S100B, and glial acidic fibrillary protein. We conducted a focused literature review of unresolved issues in three areas: mode of entry into and exit from the blood, kinetics of blood biomarkers in the blood, and predictive capacity of the blood biomarkers after mTBI. FINDINGS: Although a disruption of the blood brain barrier has been demonstrated in mild and severe traumatic brain injury, biomarkers can enter the blood through pathways that do not require a breach in this barrier. A definitive accounting for the pathways that biomarkers follow from the brain to the blood after mTBI has not been performed. Although preliminary investigations of blood biomarkers kinetics after TBI are available, our current knowledge is incomplete and definitive studies are needed. Optimal sampling times for biomarkers after mTBI have not been established. Kinetic models of blood biomarkers can be informative, but more precise estimates of kinetic parameters are needed. Confounding factors for blood biomarker levels have been identified, but corrections for these factors are not routinely made. Little evidence has emerged to date to suggest that blood biomarker levels correlate with clinical measures of mTBI severity. The significance of elevated biomarker levels thirty or more days following mTBI is uncertain. Blood biomarkers have shown a modest but not definitive ability to distinguish concussed from non-concussed subjects, to detect sub-concussive hits to the head, and to predict recovery from mTBI. Blood biomarkers have performed best at distinguishing CT scan positive from CT scan negative subjects after mTBI.

A Kinetic Model for Blood Biomarker Levels After Mild Traumatic Brain Injury.

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as to the trajectories of blood biomarker levels over time after mild TBI. Modeling the kinetic behavior of these biomarkers could be informative. We propose a one-compartment kinetic model for S100B, UCH-L1, NF-L, GFAP, and tau biomarker levels after mild TBI based on accepted pharmacokinetic models for oral drug absorption. We approximated model parameters using previously published studies. Since parameter estimates were approximate, we did uncertainty and sensitivity analyses. Using estimated kinetic parameters for each biomarker, we applied the model to an available post-concussion biomarker dataset of UCH-L1, GFAP, tau, and NF-L biomarkers levels. We have demonstrated the feasibility of modeling blood biomarker levels after mild TBI with a one compartment kinetic model. More work is needed to better establish model parameters and to understand the implications of the model for diagnostic use of these blood biomarkers for mild TBI.

Exploratory Analysis of Concussion Recovery Trajectories using Multi-modal Assessments and Serum Biomarkers.

Clinicians need better tools to assess severity, prognosis, and recovery from mild Traumatic Brain Injury (mTBI), which can cause long term impairment. To enable better mTBI outcome prediction, an initial step is to analyze the trajectory of recovery metrics over time. This study provides an assessment of recovery trajectories of mTBI while incorporating heterogeneity of individual responses. We analyze the trajectories over multiple discrete time points from baseline to 6 months post injury using a combination of neurocognitive and postural stability assessments and serum biomarkers. The data, obtained from FITBIR, consists of concussed subjects and a matched control group, to allow for comparison in prognostic assessment. Outcomes derived from this exploratory analysis will aid future studies in developing a mTBI recovery timeline model.Clinical relevance- This study further informs clinicians as to the recovery trajectory of clinical measures and biomarkers after mTBI to support return to play decisions. GFAP biomarker and measures related to balance, memory, orientation, and concentration were significantly different than controls early after mTBI.

Evaluation of standard and semantically-augmented distance metrics for neurology patients.

BACKGROUND: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks. METHODS: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by four different metrics (cosine distance, a semantically augmented cosine distance, Jaccard distance, and a semantically augmented bipartite distance). Semantic augmentation for two of the metrics depended on concept similarities from a hierarchical neuro-ontology. For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features. We assessed classification accuracy for four classifiers and cluster quality for two clustering algorithms for each of the distance metrics. RESULTS: Inter-patient distances were smaller when the distance metric was semantically augmented. Classification accuracy and cluster quality were not significantly different by distance metric. CONCLUSION: Although semantic augmentation reduced inter-patient distances, we did not find improved classification accuracy or improved cluster quality with semantically augmented patient distance metrics when applied to a dataset of neurology patients. Further work is needed to assess the utility of semantically augmented patient distances.

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.

Two algorithms for the reorganisation of the problem list by organ system.

OBJECTIVE: Long problem lists can be challenging to use. Reorganisation 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 reorganising 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 reorganisation 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' 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: Reorganisation 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.

Health information technology and electronic health records in neurologic practice.

The tipping point for electronic health records (EHR) has been reached and universal adoption in the United States is now inevitable. Neurologists will want to choose their electronic health record prudently. Careful selection, contracting, planning, and training are essential to successful implementation. Neurologists need to examine their workflow carefully and make adjustments to ensure that efficiency is increased. Neurologists will want to achieve a significant return on investment and qualify for all applicable financial incentives from payers, including CMS. EHRs are not just record-keeping tools but play an important role in quality improvement, evidence-based medicine, pay for performance, patient education, bio-surveillance, data warehousing, and data exchange.

Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance.

OBJECTIVE: The problem list is a key and required element of the electronic medical record (EMR). Problem lists may contribute substantially to patient safety and quality of care. Physician documentation of the problem list is often lower than desired. Methods are needed to improve accuracy and completeness of the problem list. DESIGN: An automated clinical decision support (CDS) intervention was designed utilizing a commercially available EMR with computerized physician order entry (CPOE) and CDS. The system was based on alerts delivered during inpatient medication CPOE that prompted clinicians to add a diagnosis to the problem list. Each alert was studied for a 2-month period after implementation. MEASUREMENTS: Measures included alert validity, alert yield, and accuracy of problem list additions. RESULTS: At a 450 bed teaching hospital, the number of medication orders which triggered alerts during all 2-month study periods was 1011. For all the alerts, the likelihood of a valid alert (an alert that occurred in patients with one of the predefined diagnoses) was 96+/-1%. The alert yield, defined as occuring when an alert led to addition of a problem to the problem list, was 76+/-2%. Accurate problem list additions, defined as additions of problems when the problem was determined to be present by expert review, was 95+/-1%. CONCLUSION: The CDS problem list mechanism was integrated into the process of medication order placement and promoted relatively accurate addition of problems to the EMR problem list.

Reduced cortical surface area in multiple sclerosis.

OBJECTIVE: To assess utility of cortical surface area as a measure of disease progression in multiple sclerosis (MS). METHODS: We measured two-dimensional flattened cortical surface area on high-resolution magnetic resonance imaging (MRI) scans obtained in 15 subjects with clinically definite MS and ten normal subjects. RESULTS AND DISCUSSION: Single hemisphere cortical area was reduced in MS patients compared with controls (96,451 versus 71,710 mm(2)). We found no significant relationship of cortical surface area to disability or disease duration. T2 lesion load was negatively correlated with two-dimensional cortical surface area (r=-0.62). CONCLUSION: Cortical surface area is decreased in MS and may be a useful measure of disease progression.

Motor reorganization in multiple sclerosis.

OBJECTIVE: To assess intra-hemispheric and interhemispheric reorganization of motor activation in multiple sclerosis (MS). Motor reorganization may contribute to minimizing motor deficits after demyelination in MS. METHODS: We used surface-based analysis to study functional organization for motor function in ten healthy controls and in 15 MS subjects. RESULTS AND DISCUSSION: In MS subjects, activation in the right hemisphere (ipsilateral to the hand moved) was significantly increased compared with control subjects. We interpreted this increase as interhemispheric reorganization of motor activation. The increases in right hemisphere activation were the greatest in the pre-motor cortex (Brodmann area 6) and the cognitive areas. Within the left hemisphere, contralateral to the right hand, total motor activation was not increased and the centroid of activation was not displaced when MS subjects were compared with controls. However, we found that MS subjects with high MS plaque loads showed an anterior shift of the focus of motor activation with right hand movement when compared with the low MS plaque load subjects (p<0.05). Furthermore, there was more activation in pre-motor cortex (Brodmann area 6) in the high plaque load group and less activation in sensory areas (Brodmann areas 1, 2 and 3). CONCLUSION: Functional magnetic resonance imaging (fMRI) provides evidence that both interhemispheric and intra-hemispheric motor reorganization occur in MS.