Dataset Information

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

ABSTRACT: Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate "literacy profiles" as automated indicators of patients' health literacy to facilitate a non-intrusive, economic and more comprehensive characterization of health literacy among a health care delivery system's membership. To this end, three literacy profiles were generated based on natural language processing (combining computational linguistics and machine learning) using a sample of 283,216 secure messages sent from 6,941 patients to their primary care physicians. All patients were participants in Kaiser Permanente Northern California's DISTANCE Study. Performance of the three literacy profiles were compared against a gold standard of patient self-reported health literacy. Associations were analyzed between each literacy profile and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, poor adherence and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61-0.74. Relations between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles indicative of limited health literacy: (a) were older and more likely of minority status; (b) had poorer medication adherence and glycemic control; and (c) exhibited higher rates of hypoglycemia, comorbidities and healthcare utilization. This represents the first successful attempt to employ natural language processing to estimate health literacy. Literacy profiles can offer an automated and economical way to identify patients with limited health literacy and greater vulnerability to poor health outcomes.

SUBMITTER: Balyan R

PROVIDER: S-EPMC6386302 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Balyan Renu R Crossley Scott A SA Brown William W Karter Andrew J AJ McNamara Danielle S DS Liu Jennifer Y JY Lyles Courtney R CR Schillinger Dean D

PloS one 20190222 2

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate "literacy profiles" as automated indicators of patients' health literacy to facilitate a non-intrusive, economic and more comprehensive characterization of health ...[more]

PMID: 30794616

Similar Datasets

Project description:BackgroundMachine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.ObjectiveThe primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.MethodsThis systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.ResultsA total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.ConclusionsMachine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Project description:BackgroundUnstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of written text into interpretable datasets that can be analysed by statistical and machine learning (ML) models. The purpose of this paper is to provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software.MethodsWe performed three NLP experiments using publicly-available data obtained from medicine review websites. First, we conducted lexicon-based sentiment analysis on open-text patient reviews of four drugs: Levothyroxine, Viagra, Oseltamivir and Apixaban. Next, we used unsupervised ML (latent Dirichlet allocation, LDA) to identify similar drugs in the dataset, based solely on their reviews. Finally, we developed three supervised ML algorithms to predict whether a drug review was associated with a positive or negative rating. These algorithms were: a regularised logistic regression, a support vector machine (SVM), and an artificial neural network (ANN). We compared the performance of these algorithms in terms of classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity and specificity.ResultsLevothyroxine and Viagra were reviewed with a higher proportion of positive sentiments than Oseltamivir and Apixaban. One of the three LDA clusters clearly represented drugs used to treat mental health problems. A common theme suggested by this cluster was drugs taking weeks or months to work. Another cluster clearly represented drugs used as contraceptives. Supervised machine learning algorithms predicted positive or negative drug ratings with classification accuracies ranging from 0.664, 95% CI [0.608, 0.716] for the regularised regression to 0.720, 95% CI [0.664,0.776] for the SVM.ConclusionsIn this paper, we present a conceptual overview of common techniques used to analyse large volumes of text, and provide reproducible code that can be readily applied to other research studies using open-source software.

Project description:ImportanceOverdose is one of the leading causes of death in the US; however, surveillance data lag considerably from medical examiner determination of the death to reporting in national surveillance reports.ObjectiveTo automate the classification of deaths related to substances in medical examiner data using natural language processing (NLP) and machine learning (ML).Design, setting, and participantsDiagnostic study comparing different natural language processing and machine learning algorithms to identify substances related to overdose in 10 health jurisdictions in the US from January 1, 2020, to December 31, 2020. Unstructured text from 35 433 medical examiner and coroners' death records was examined.ExposuresText from each case was manually classified to a substance that was related to the death. Three feature representation methods were used and compared: text frequency-inverse document frequency (TF-IDF), global vectors for word representations (GloVe), and concept unique identifier (CUI) embeddings. Several ML algorithms were trained and best models were selected based on F-scores. The best models were tested on a hold-out test set and results were reported with 95% CIs.Main outcomes and measuresText data from death certificates were classified as any opioid, fentanyl, alcohol, cocaine, methamphetamine, heroin, prescription opioid, and an aggregate of other substances. Diagnostic metrics and 95% CIs were calculated for each combination of feature extraction method and machine learning classifier.ResultsOf 35 433 death records analyzed (decedent median age, 58 years [IQR, 41-72 years]; 24 449 [69%] were male), the most common substances related to deaths included any opioid (5739 [16%]), fentanyl (4758 [13%]), alcohol (2866 [8%]), cocaine (2247 [6%]), methamphetamine (1876 [5%]), heroin (1613 [5%]), prescription opioids (1197 [3%]), and any benzodiazepine (1076 [3%]). The CUI embeddings had similar or better diagnostic metrics compared with word embeddings and TF-IDF for all substances except alcohol. ML classifiers had perfect or near perfect performance in classifying deaths related to any opioids, heroin, fentanyl, prescription opioids, methamphetamine, cocaine, and alcohol. Classification of benzodiazepines was suboptimal using all 3 feature extraction methods.Conclusions and relevanceIn this diagnostic study, NLP/ML algorithms demonstrated excellent diagnostic performance at classifying substances related to overdoses. These algorithms should be integrated into workflows to decrease the lag time in reporting overdose surveillance data.

Project description:ImportanceNonfatal gunshot injuries are the most common firearm injury, but where they frequently occur remains unclear owing to data limitations. Natural language processing can be applied to medical text narratives of gunshot injury records to classify injury location and inform prevention efforts.ObjectiveTo examine the performance of natural language processing (NLP) and machine learning models to predict nonfatal gunshot injury locations and generate new national estimates of the locations in which these injuries occur.Design, setting, and participantsCross-sectional study of data from the National Electronic Injury Surveillance System Firearm Injury Surveillance Study on nonfatal gunshot injuries that occurred in the US between January 1, 1993, and December 31, 2015. The unweighted sample included 59 025 gunshot injuries that were initially treated at emergency departments. Data were analyzed from June 1, 2019 to July 24, 2020.Main outcomes and measuresThe primary outcomes were classification of injury location and subsequent estimation of nonfatal gunshot injury location. The NLP was used to generate 6 sets of predictors, and 4 machine learning models were trained to classify the missing locations: multinomial support vector machines, lasso regression, XgBoost gradient descent, and feed-forward neural networks. For each of the 6 sets of NLP predictors, 70% of records with locations were randomly sampled to form the training set and the remaining 30% of records composed the test set. The best-performing model was validated by comparing the predicted locations were with those from existing national estimates of nonfatal gunshot injuries stratified by location and intent.ResultsThe unweighted sample included 59 025 nonfatal gunshot injuries; patients with these injuries were predominantly male (n = 52 630, [89.2%]), of Black race/ethnicity (n = 29 304 [49.6%]), and young (15-24 years; n = 27 037 [45.8%]). In total, 54 089 nonfatal gunshot injuries that were weighted to approximate national estimates were included in the analysis. Existing national estimates suggest that the most prevalent nonfatal gunshot injury location is the home (n = 14 764 [23.4%]), followed by the street or highway (n = 14 402 [22.9%]), and other public places (n = 7276 [11.6%]). After implementation of NLP classification, the most frequent gunshot injury location was the street or highway (n = 27 200 [46.1%]), followed by the home (n = 23 738 [37.7%]), and other public places (n = 10 439 [15.1%]).Conclusions and relevanceThe findings of this study suggest that NLP and machine learning models may be useful for classifying gunshot injury location and that most nonfatal gunshot injuries occur in the street or highway rather than in the home; these findings can inform future firearm injury prevention efforts.

Project description:BackgroundStroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events.ObjectiveThe aim of this study was to develop a machine learning-based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing.MethodsThe algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314).ResultsAmong the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample.ConclusionsWe developed and validated a machine learning-based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.

Dataset Information

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Publications

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets