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We examine the feasibility of a machine learning approach to identification of foot examination (FE) findings from the unstructured text of clinical reports. A Support Vector Machine (SVM) based system was constructed to process the text of physical examination sections of in- and out-patient clinical notes to identify if the findings of structural, neurological, and vascular components of a FE revealed normal or abnormal findings or were not assessed. The system was tested on 145 randomly selected patients for each FE component using 10-fold cross validation. The accuracy was 80%, 87% and 88% for structural, neurological, and vascular component classifiers, respectively. Our results indicate that using machine learning to identify FE findings from clinical reports is a viable alternative to manual review and warrants further investigation. This application may improve quality and safety by providing inexpensive and scalable methodology for quality and risk factor assessments at the point of care. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Human classification of diagnoses is a labor intensive process that consumes significant resources. Most medical practices use specially trained medical coders to categorize diagnoses for billing and research purposes. METHODS: We have developed an automated coding system designed to assign codes to clinical diagnoses. The system uses the notion of certainty to recommend subsequent processing. Codes with the highest certainty are generated by matching the diagnostic text to frequent examples in a database of 22 million manually coded entries. These code assignments are not subject to subsequent manual review. Codes at a lower certainty level are assigned by matching to previously infrequently coded examples. The least certain codes are generated by a naïve Bayes classifier. The latter two types of codes are subsequently manually reviewed. MEASUREMENTS: Standard information retrieval accuracy measurements of precision, recall and f-measure were used. Micro- and macro-averaged results were computed. RESULTS At least 48% of all EMR problem list entries at the Mayo Clinic can be automatically classified with macro-averaged 98.0% precision, 98.3% recall and an f-score of 98.2%. An additional 34% of the entries are classified with macro-averaged 90.1% precision, 95.6% recall and 93.1% f-score. The remaining 18% of the entries are classified with macro-averaged 58.5%. CONCLUSION: Over two thirds of all diagnoses are coded automatically with high accuracy. The system has been successfully implemented at the Mayo Clinic, which resulted in a reduction of staff engaged in manual coding from thirty-four coders to seven verifiers. [ABSTRACT FROM AUTHOR]

Objective: With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution., Materials and Methods: Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger., Results: This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems., Discussion: Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime., Conclusion: This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Psychosocial stress is a major risk factor for morbidity and mortality related to a wide range of health conditions and has a significant negative impact on public health. Quantifying exposure to stress in the naturalistic environment can help to better understand its health effects and identify strategies for timely intervention. The objective of the current project was to develop and test the infrastructure and methods necessary for using wearable technology to quantify individual response to stressful situations and to determine if popular and accessible fitness trackers such as Fitbit® equipped with an optical heart rate (HR) monitor could be used to detect physiological response to psychosocial stress in everyday life. The participants in this study were University of Minnesota students (n = 18) that owned a Fitbit® tracker and had at least one upcoming examination. Continuous HR and activity measurements were obtained during a 7-day observation period containing examinations self-reported by the participants. Participants responded to six ecological momentary assessment surveys per day (~ 2 hour intervals) to indicate occurrence of stressful events. We compared HR during stressful events (e.g., exams) to baseline HR during periods indicated as non-stressful using mixed effects modeling. Our results show that HR was elevated by 8.9 beats per minute during exams and by 3.2 beats per minute during non-exam stressors. These results are consistent with prior laboratory findings and indicate that consumer wearable fitness trackers could serve as a valuable source of information on exposure to psychosocial stressors encountered in the naturalistic environment., Competing Interests: The authors declare that they have no competing interests.

Epidemiological studies have linked age-related hearing loss (ARHL) with an increased risk of neurocognitive decline. Difficulties in speech perception with subsequent changes in brain morphometry, including regions important for lexical-semantic memory, are thought to be a possible mechanism for this relationship. This study investigated differences in automatic and executive lexical-semantic processes on verbal fluency tasks in individuals with acquired hearing loss. The primary outcomes were indices of automatic (clustering/word retrieval at start of task) and executive (switching/word retrieval after start of the task) processes from semantic and phonemic fluency tasks. To extract indices of clustering and switching, we used both manual and computerised methods. There were no differences between groups on indices of executive fluency processes or on any indices from the semantic fluency task. The hearing loss group demonstrated weaker automatic processes on the phonemic fluency task. Further research into differences in lexical-semantic processes with ARHL is warranted., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi-LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.com/NLPIE/clinical-sentences.

Introduction: Our objective was to examine the association between perseverations produced on the semantic verbal fluency (SVF) task in asymptomatic individuals and the future diagnosis of cognitive impairment (CI)., Method: Participants were individuals participating in the Mayo Clinic Study of Aging (N = 1269, M age  = 79.3 years, SD = 5.1; 51% men). All were cognitively normal at baseline and were followed in 15-month intervals for up to 6 visits. Each neurocognitive assessment included SVF tasks ("animals," "fruits," and "vegetables"). Cox modeling was used to test for associations between perseverations and time to CI diagnosis., Results: Perseverations on the "animals" SVF task were associated with incident CI (hazard ratio = 1.35; 95% confidence interval, CI [1.10, 1.66]). No significant association was found with perseverations on the "fruits" or "vegetables" SVF tasks. Mixed-effects modeling in cognitively normal participants revealed that the number of perseverations at baseline is significantly associated with decline in memory and visuospatial cognitive domains but is not associated with decline in attention., Conclusions: Assessing perseverations together with standard SVF scores on the "animals" SVF task can help in early identification of asymptomatic individuals at an increased risk for CI. Perseverations are not associated with attention, but rather visual and verbal working memory mechanisms. In longitudinal settings aimed at early detection of signs of CI in presymptomatic individuals, SVF testing with scoring that includes counting of perseverations may potentially serve as a practical alternative to the more cumbersome memory tests.

Pharmacist workforce researchers are predicting a potential surplus of pharmacists in the United States that might result in pharmacists being available for engagement in new roles. The objective for this study was to describe consumer opinions regarding medication use, the health care system, and pharmacists to help identify new roles for pharmacists from the consumer perspective. Data were obtained from the 2015 and 2016 National Consumer Surveys on the Medication Experience and Pharmacist Roles. Out of the representative sample of 36,673 respondents living in the United States, 80% (29,426) submitted written comments at the end of the survey. Of these, 2178 were specifically about medicines, pharmacists or health and were relevant and usable for this study. Thematic analysis, content analysis, and computer-based text mining were used for identifying themes and coding comments. The findings showed that 66% of the comments about medication use and 82% about the health care system were negative. Regarding pharmacists, 73% of the comments were positive with many commenting about the value of the pharmacist for overcoming fears and for filling current gaps in their healthcare. We propose that these comments might be signals that pharmacists could help improve coordination and continuity for peoples' healthcare and could help guide the development of new service offerings.

Clinical notes contain rich information about supplement use that is critical for detecting adverse interactions between supplements and prescribed medications. It is important to know the context in which supplements are mentioned in clinical notes to be able to correctly identify patients that either currently take the supplement or did so in the past. We applied text mining methods to automatically classify supplement use into four status categories: Continuing (C), Discontinued (D), Started (S), and Unclassified (U). We manually classified 1,300 sentences into these categories, which were further split as training (1000 sentences) and testing (300 sentences) sets. We evaluated the 7 types of feature sets and 5 algorithms, and the best model (SVM with unigram, bigram and indicator word within certain distance) performed F-measure of 0.906, 0.913, 0.914, 0.715 for status C, D, S, U, respectively on the testing set. This study demonstrates the feasibility of using text mining methods to classify supplement use status from clinical notes.

Background: Automated methods for identifying clinically relevant new versus redundant information in electronic health record (EHR) clinical notes is useful for clinicians and researchers involved in patient care and clinical research, respectively. We evaluated methods to automatically identify clinically relevant new information in clinical notes, and compared the quantity of redundant information across specialties and clinical settings., Methods: Statistical language models augmented with semantic similarity measures were evaluated as a means to detect and quantify clinically relevant new and redundant information over longitudinal clinical notes for a given patient. A corpus of 591 progress notes over 40 inpatient admissions was annotated for new information longitudinally by physicians to generate a reference standard. Note redundancy between various specialties was evaluated on 71,021 outpatient notes and 64,695 inpatient notes from 500 solid organ transplant patients (April 2015 through August 2015)., Results: Our best method achieved at best performance of 0.87 recall, 0.62 precision, and 0.72 F-measure. Addition of semantic similarity metrics compared to baseline improved recall but otherwise resulted in similar performance. While outpatient and inpatient notes had relatively similar levels of high redundancy (61% and 68%, respectively), redundancy differed by author specialty with mean redundancy of 75%, 66%, 57%, and 55% observed in pediatric, internal medicine, psychiatry and surgical notes, respectively., Conclusions: Automated techniques with statistical language models for detecting redundant versus clinically relevant new information in clinical notes do not improve with the addition of semantic similarity measures. While levels of redundancy seem relatively similar in the inpatient and ambulatory settings in the Fairview Health Services, clinical note redundancy appears to vary significantly with different medical specialties.

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer's disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

We present the results of a study investigating the use of speech and language characteristics extracted from spontaneous spoken discourse to assess changes in cognitive function. Specifically, we investigated the use of automatic speech recognition technology to characterize spontaneous speech disfluency induced by topiramate, an anti-epileptic medication with language-related side-effects. We audio recorded spontaneous speech samples from 20 participants during several picture description tasks and analyzed the recordings automatically and manually to extract a range of spoken fluency measurements including speech discontinuities (e.g., filled pauses, false starts, and repetitions), silent pause duration, speaking rate and vowel lengthening. Our results indicate that some of these paralinguistic speech characteristics are a) sensitive to the effects of topiramate, b) are associated with topiramate concentrations in the blood, and c) complement standard neuropsychological tests typically used to investigate cognitive effects of medications. This work demonstrates the use of computational linguistic tools to assess cognitive effects in a more sensitive, objective and reproducible manner than is currently available with standard tests.