Studying Associations Between Heart Failure Self-Management and Rehospitalizations Using Natural Language Processing

2016 ◽  
Vol 39 (1) ◽  
pp. 147-165 ◽  
Author(s):  
Maxim Topaz ◽  
Kavita Radhakrishnan ◽  
Suzanne Blackley ◽  
Victor Lei ◽  
Kenneth Lai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hospital Readmissions ◽  
Discharge Summary ◽  
Self Management ◽  
Regression Analyses ◽  
Discharge Summaries ◽  
Natural Language Processing Algorithm

This study developed an innovative natural language processing algorithm to automatically identify heart failure (HF) patients with ineffective self-management status (in the domains of diet, physical activity, medication adherence, and adherence to clinician appointments) from narrative discharge summary notes. We also analyzed the association between self-management status and preventable 30-day hospital readmissions. Our natural language system achieved relatively high accuracy ( F-measure = 86.3%; precision = 95%; recall = 79.2%) on a testing sample of 300 notes annotated by two human reviewers. In a sample of 8,901 HF patients admitted to our healthcare system, 14.4% ( n = 1,282) had documentation of ineffective HF self-management. Adjusted regression analyses indicated that presence of any skill-related self-management deficit (odds ratio [OR] = 1.3, 95% confidence interval [CI] = [1.1, 1.6]) and non-specific ineffective self-management (OR = 1.5, 95% CI = [1.2, 2]) was significantly associated with readmissions. We have demonstrated the feasibility of identifying ineffective HF self-management from electronic discharge summaries with natural language processing.

Identifying Heart Failure Symptoms and Poor Self-Management in Home Healthcare: A Natural Language Processing Study

2021 ◽  
Author(s):  
Sena Chae ◽  
Jiyoun Song ◽  
Marietta Ojo ◽  
Maxim Topaz
Keyword(s):  
Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Symptom Management ◽  
Home Healthcare ◽  
Self Management ◽  
Clinical Notes ◽  
Patients With Heart Failure

The goal of this natural language processing (NLP) study was to identify patients in home healthcare with heart failure symptoms and poor self-management (SM). The preliminary lists of symptoms and poor SM status were identified, NLP algorithms were used to refine the lists, and NLP performance was evaluated using 2.3 million home healthcare clinical notes. The overall precision to identify patients with heart failure symptoms and poor SM status was 0.86. The feasibility of methods was demonstrated to identify patients with heart failure symptoms and poor SM documented in home healthcare notes. This study facilitates utilizing key symptom information and patients’ SM status from unstructured data in electronic health records. The results of this study can be applied to better individualize symptom management to support heart failure patients’ quality-of-life.

scholarly journals Developing A Deep Learning Natural Language Processing Algorithm For Automated Reporting Of Adverse Drug Reactions

2021 ◽  
Author(s):  
Christopher McMaster ◽  
Julia Chan ◽  
David FL Liew ◽  
Elizabeth Su ◽  
Albert G Frauman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Adverse Events ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Processing Algorithm ◽  
Drug Reactions ◽  
Discharge Summaries ◽  
Natural Language Processing Algorithm

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 150,000 unlabelled discharge summaries; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.934 (95% CI 0.931 - 0.955) for the task of identifying discharge summaries containing ADR mentions.

scholarly journals Natural Language Processing for Surveillance of Cervical and Anal Cancer and Precancer: Algorithm Development and Split-Validation Study (Preprint)

2020 ◽  
Author(s):  
Carlos R Oliveira ◽  
Patrick Niccolai ◽  
Anette Michelle Ortiz ◽  
Sangini S Sheth ◽  
Eugene D Shapiro ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Human Papillomavirus ◽  
Natural Language ◽  
Language Processing ◽  
Medical Records ◽  
Processing Algorithm ◽  
Accurate Identification ◽  
Pathology Reports ◽  
Manual Review ◽  
Natural Language Processing Algorithm

BACKGROUND Accurate identification of new diagnoses of human papillomavirus–associated cancers and precancers is an important step toward the development of strategies that optimize the use of human papillomavirus vaccines. The diagnosis of human papillomavirus cancers hinges on a histopathologic report, which is typically stored in electronic medical records as free-form, or unstructured, narrative text. Previous efforts to perform surveillance for human papillomavirus cancers have relied on the manual review of pathology reports to extract diagnostic information, a process that is both labor- and resource-intensive. Natural language processing can be used to automate the structuring and extraction of clinical data from unstructured narrative text in medical records and may provide a practical and effective method for identifying patients with vaccine-preventable human papillomavirus disease for surveillance and research. OBJECTIVE This study's objective was to develop and assess the accuracy of a natural language processing algorithm for the identification of individuals with cancer or precancer of the cervix and anus. METHODS A pipeline-based natural language processing algorithm was developed, which incorporated machine learning and rule-based methods to extract diagnostic elements from the narrative pathology reports. To test the algorithm’s classification accuracy, we used a split-validation study design. Full-length cervical and anal pathology reports were randomly selected from 4 clinical pathology laboratories. Two study team members, blinded to the classifications produced by the natural language processing algorithm, manually and independently reviewed all reports and classified them at the document level according to 2 domains (diagnosis and human papillomavirus testing results). Using the manual review as the gold standard, the algorithm’s performance was evaluated using standard measurements of accuracy, recall, precision, and F-measure. RESULTS The natural language processing algorithm’s performance was validated on 949 pathology reports. The algorithm demonstrated accurate identification of abnormal cytology, histology, and positive human papillomavirus tests with accuracies greater than 0.91. Precision was lowest for anal histology reports (0.87, 95% CI 0.59-0.98) and highest for cervical cytology (0.98, 95% CI 0.95-0.99). The natural language processing algorithm missed 2 out of the 15 abnormal anal histology reports, which led to a relatively low recall (0.68, 95% CI 0.43-0.87). CONCLUSIONS This study outlines the development and validation of a freely available and easily implementable natural language processing algorithm that can automate the extraction and classification of clinical data from cervical and anal cytology and histology.

Cascading Workflow of Healthcare Services

2019 ◽  
Vol 1 (1) ◽  
pp. 79-95
Author(s):  
Phillip Osial ◽  
Arnold Kim ◽  
Kalle Kauranen
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
New Technology ◽  
Healthcare Services ◽  
Discharge Summary ◽  
Healthcare Industry ◽  
Clinical Workflow ◽  
The Us ◽  
Processing Techniques

Despite rapid advancements in technology, the healthcare industry is known to lag behind when it comes to adopting new changes. Most often, when a new technology such as CPOE or EHR systems presents themselves in the healthcare industry, clinicians are left struggling to keep up with their workloads while learning to adjust a new workflow. Instead of disrupting the clinician's clinical workflow, the authors propose a system for transforming clinical narratives presented in the form of discharge summaries from the i2b2 Natural Language Processing dataset into a standardized order set. The proposed system uses natural language processing techniques based on Scala, which extracts discharge summary information about a patient and is proven to be highly scalable. The goal of this system is to increase interoperability between CPOE systems by performing further transformations on the extracted data. The authors adhere to HL7's FHIR standards and use JSON as the primary medical messaging format, which is used both in the US and international healthcare industry organizations and companies.

Sign in / Sign up

Export Citation Format

Share Document