Predictors of Postacute Sequelae of COVID-19 Development and Rehabilitation: A Retrospective Study.

OBJECTIVE: To examine the frequency of postacute sequelae of SARS-CoV-2 (PASC) and the factors associated with rehabilitation utilization in a large adult population with PASC. DESIGN: Retrospective study. SETTING: Midwest hospital health system. PARTICIPANTS: 19,792 patients with COVID-19 from March 10, 2020, to January 17, 2021. INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: Descriptive analyses were conducted across the entire cohort along with an adult subgroup analysis. A logistic regression was performed to assess factors associated with PASC development and rehabilitation utilization. RESULTS: In an analysis of 19,792 patients, the frequency of PASC was 42.8% in the adult population. Patients with PASC compared with those without had a higher utilization of rehabilitation services (8.6% vs 3.8%, P<.001). Risk factors for rehabilitation utilization in patients with PASC included younger age (odds ratio [OR], 0.99; 95% confidence interval [CI], 0.98-1.00; P=.01). In addition to several comorbidities and demographics factors, risk factors for rehabilitation utilization solely in the inpatient population included male sex (OR, 1.24; 95% CI, 1.02-1.50; P=.03) with patients on angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers 3 months prior to COVID-19 infections having a decreased risk of needing rehabilitation (OR, 0.80; 95% CI, 0.64-0.99; P=.04). CONCLUSIONS: Patients with PASC had higher rehabilitation utilization. We identified several clinical and demographic factors associated with the development of PASC and rehabilitation utilization.

Complementary and Integrative Health Information in the literature: its lexicon and named entity recognition.

OBJECTIVE: To construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to help better represent the often underrepresented physical and psychological CIH approaches in standard terminologies, and to also apply state-of-the-art natural language processing (NLP) techniques to help recognize them in the biomedical literature. MATERIALS AND METHODS: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant sources of knowledge. The Lexicon encompasses 724 unique concepts with 885 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS), and we developed and utilized BERT models comparing their efficiency in CIH named entity recognition to well-established models including MetaMap and CLAMP, as well as the large language model GPT3.5-turbo. RESULTS: Of the 724 unique concepts in CIHLex, 27.2% could be matched to at least one term in the UMLS. About 74.9% of the mapped UMLS Concept Unique Identifiers were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro-average F1-score of 0.91, surpassing other models. CONCLUSION: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts.

A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients.

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.

Extracting Complementary and Integrative Health Approaches in Electronic Health Records.

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. While the evidence bases to support them are growing, there is still a gap in understanding their effects and potential adverse events using real-world data. The overall goal of this study is to represent information pertinent to both psychological and physical CIH approaches (specifically, using examples of music therapy, chiropractic, and aquatic exercise in this study) in an electronic health record (EHR) system. We also aim to evaluate the ability of existing natural language processing (NLP) systems to identify CIH approaches. A total of 300 notes were randomly selected and manually annotated. Annotations were made for status, symptom, and frequency of each approach. This set of annotations was used as a gold standard to evaluate the performance of NLP systems used in this study (specifically BioMedICUS, MetaMap, and cTAKES) for extracting CIH concepts. Venn diagram was used to investigate the consistency of medical records searching by Current Procedural Terminology (CPT) codes and CIH approaches keywords in SQL. Since CPT codes usually do not have specific mentions of CIH approaches, the Venn diagram had less overlap with those found in clinical notes for all three CIH therapies. The three NLP systems achieved 0.41 in average lenient match F1-score in all three CIH approaches, respectively. BioMedICUS achieved the best performance in aquatic exercise with an F1-score of 0.66. This study contributes to the overall representation of CIH in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

Annotating Music Therapy, Chiropractic and Aquatic Exercise Using Electronic Health Record.

Complementary and Integrative Health (CIH) has gained increasing popularity in the past decades. The overall goal of this study is to represent information pertinent to music therapy, chiropractic and aquatic exercise in an EHR system. A total of 300 clinical notes were randomly selected and manually annotated. Annotations were made for status, symptom and frequency of each approach. This set of annotations was used as a gold standard to evaluate performance of NLP systems used in this study (specifically BioMedICUS, MetaMap and cTAKES) for extracting CIH concepts. Three NLP systems achieved an average lenient match F1-score of 0.50 in all three CIH approaches. BioMedICUS achieved the best performance in music therapy with an F1-score of 0.73. This study is a pilot to investigate CIH representation in clinical note and lays a foundation for using EHR for clinical research for CIH approaches.

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

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.

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

BACKGROUND: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness. Natural language processing (NLP) methods present a novel approach to bridge this gap. We sought to evaluate the efficacy of a novel and automated NLP pipeline to determine treatment appropriateness from a sample of prehospital EMS motor vehicle crash records. METHODS: A total of 142 records were used to extract airway procedures, intraosseous/intravenous access, packed red blood cell transfusion, crystalloid bolus, chest compression system, tranexamic acid bolus, and needle decompression. Reports were processed using four clinical NLP systems and augmented via a word2phrase method leveraging a large integrated health system clinical note repository to identify terms semantically similar with treatment indications. Indications were matched with treatments and categorized as indicated, missed (indicated but not performed), or nonindicated. Automated results were then compared with manual review, and precision and recall were calculated for each treatment determination. RESULTS: Natural language processing identified 184 treatments. Automated timeline summarization was completed for all patients. Treatments were characterized as indicated in a subset of cases including the following: 69% (18 of 26 patients) for airway, 54.5% (6 of 11 patients) for intraosseous access, 11.1% (1 of 9 patients) for needle decompression, 55.6% (10 of 18 patients) for tranexamic acid, 60% (9 of 15 patients) for packed red blood cell, 12.9% (4 of 31 patients) for crystalloid bolus, and 60% (3 of 5 patients) for chest compression system. The most commonly nonindicated treatment was crystalloid bolus (22 of 142 patients). Overall, the automated NLP system performed with high precision and recall with over 70% of comparisons achieving precision and recall of greater than 80%. CONCLUSION: Natural language processing methodologies show promise for enabling automated extraction of procedural indication data and timeline summarization. Future directions should focus on optimizing and expanding these techniques to scale and facilitate broader trauma care performance monitoring. LEVEL OF EVIDENCE: Diagnostic tests or criteria, level III.

Named Entity Recognition in Prehospital Trauma Care.

Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy. The ensemble showed increased performance over individual systems.