Natural language processing in low back pain and spine diseases: A systematic review
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SUBMITTER: Bacco L
PROVIDER: S-EPMC9329654 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:RATIONALE AND OBJECTIVES:To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. MATERIALS AND METHODS:We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N?=?178,333 reports, we randomly selected N?=?871 to form a reference-standard dataset, consisting of N?=?413 x-ray reports and N?=?458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS:The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa?>?0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). CONCLUSIONS:Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC.
| S-EPMC6162177 | biostudies-literature
Project description:BackgroundNovel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset.ObjectiveThe goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives.MethodsPreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using "clinical notes," "natural language processing," and "chronic disease" and their variations as keywords to maximize coverage of the articles.ResultsOf the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes.ConclusionsEfforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
| S-EPMC6528438 | biostudies-literature
Project description:BackgroundMost patients in the secondary care sector consulting for low back pain (LBP) seem to have a more or less constant course of pain during the ensuing year. Fewer patients with LBP in the primary care sector report continual pain over a one-year period. However, not much is known about the long-term course of LBP in the general population. A systematic critical literature review was undertaken in order to study the natural course of LBP over time in the general population.MethodsA search of articles was performed in Pubmed, Cinahl and Psychinfo using the search terms 'epidemiology'; 'low back pain' or 'back pain'; 'prospective study' or 'longitudinal study'; 'follow-up', 'natural course', 'course' or 'natural history'; 'general population' or 'working population'. Inclusion criteria were that one of the objectives was to study the course of (L)BP in the adult population, that the period of follow-up was at least 3 months, and that there were three points of observation or more. The review was undertaken by two independent reviewers using three checklists relating to description of studies, quality and outcomes. The course of LBP was established in relation to those who, at baseline, were reported not to have LBP or to have LBP. Would this course be stable, fluctuating, worsening, or improving over time? A synthesis of results in relation to common patterns was presented in a table and interpreted in a narrative form.ResultsEight articles were included. Articles were different on time span, the number of surveys, and the definition of LBP. In six of the seven relevant studies, for those with no LBP at baseline, relatively substantial stable subgroups of people who continued to be LBP free were identified. In six of the seven relevant studies, definite stable subgroups of continued LBP were noted and improvement (becoming pain free) was never reported to be a common finding.ConclusionThe status of LBP in individuals of the general population appears to be relatively stable over time, perhaps particularly so for those without LBP at baseline.
| S-EPMC3599187 | biostudies-literature
Project description:Background:Taking the natural course of recurrent and fluctuating low back pain (LBP) seen in longitudinal studies of adults into consideration, the aetiology and development of LBP in children and adolescents also needs to be reflected in a long-term course. Therefore, a systematic critical literature review was undertaken to assess the natural course of LBP in the general population from childhood through adolescence to young adulthood. Methods:A systematic literature search was conducted in MEDLINE, EMBASE, CINAHL and PsycINFO with synonyms of search terms for 1) low back pain; 2) natural course; 3) cohort study and 4) children. Records in English, German, French, Danish, Swedish, and Norwegian were included. To assess the methodological quality of the studies, the NIH quality assessment checklist for cohort studies was adapted and risk of bias was assessed on a study level. Two authors independently reviewed selected studies, assessed quality, and extracted data. A synthesis of results in relation to the natural course of LBP was created. Results:Totally, 3373 records were identified, eight articles were included for quality assessment, and finally, four studies of good to fair quality were included for synthesis of results. Indication of three common patterns of LBP were identified across studies and labelled as 1) ´children and adolescents with no LBP or low probability of LBP´ (49 to 53%), 2) ´children and adolescents with fluctuation of LBP´ (16 to 37%) and 3) ´children and adolescents with repeated reporting of LBP´ (< 1 to 10%). Conclusion:Although methodological heterogeneity, mainly due to different age ranges, an indication of a natural course of LBP was seen across studies. The majority of children and adolescents repeatedly reporting no or low probability of LBP. With recall periods between one week to three months and sampling rates ranging from one to four years, a very low rate repeatedly reported LBP, and approximately one-fifth to one-third of children and adolescents had fluctuating reports of LBP. A need of future research of LBP trajectories with short reporting period lengths and narrower sampling windows in a long-term perspective is emphasized in order to study childhood influences on the development of LBP throughout life.
| S-EPMC6425623 | biostudies-literature
Project description:ObjectiveTo assess the effect of multidisciplinary biopsychosocial rehabilitation on clinically relevant outcomes in patients with chronic low back pain.DesignSystematic literature review of randomised controlled trials.ParticipantsA total of 1964 patients with disabling low back pain for more than three months.Main outcome measuresPain, function, employment, quality of life, and global assessments.ResultsTen trials reported on a total of 12 randomised comparisons of multidisciplinary treatment and a control condition. There was strong evidence that intensive multidisciplinary biopsychosocial rehabilitation with functional restoration improves function when compared with inpatient or outpatient non-multidisciplinary treatments. There was moderate evidence that intensive multidisciplinary biopsychosocial rehabilitation with functional restoration reduces pain when compared with outpatient non-multidisciplinary rehabilitation or usual care. There was contradictory evidence regarding vocational outcomes of intensive multidisciplinary biopsychosocial intervention. Some trials reported improvements in work readiness, but others showed no significant reduction in sickness leaves. Less intensive outpatient psychophysical treatments did not improve pain, function, or vocational outcomes when compared with non-multidisciplinary outpatient therapy or usual care. Few trials reported effects on quality of life or global assessments.ConclusionsThe reviewed trials provide evidence that intensive multidisciplinary biopsychosocial rehabilitation with functional restoration reduces pain and improves function in patients with chronic low back pain. Less intensive interventions did not show improvements in clinically relevant outcomes.
| S-EPMC33389 | biostudies-literature
Project description:BackgroundLumbar spine injuries in fast bowlers account for the greatest missed playing time in cricket. A range of extrinsic and intrinsic variables are hypothesised to be associated with low back pain and lumbar spine injury in fast bowlers, and an improved understanding of intrinsic variables is necessary as these may alter load tolerance and injury risk associated with fast bowling. This review critically evaluated studies reporting intrinsic variables associated with low back pain and lumbar spine injury in fast bowlers and identified areas for future investigation.MethodsOVID Medline, EMBASE, SPORTDiscus, CINAHL, Web of Science and SCOPUS databases were last searched on 3 June 2022 to identify studies investigating intrinsic variables associated with low back pain and lumbar spine injury in cricket fast bowlers. Terms relevant to cricket fast bowling, and intrinsic variables associated with lumbar spine injury and low back pain in fast bowlers were searched. 1,503 abstracts were screened, and 118 full-text articles were appraised to determine whether they met inclusion criteria. Two authors independently screened search results and assessed risk of bias using a modified version of the Quality in Prognostic Studies tool.ResultsTwenty-five studies met the inclusion criteria. Overall, no included studies demonstrated a low risk of bias, two studies were identified as moderate risk, and twenty-three studies were identified as high risk. Conflicting results were reported amongst studies investigating associations of fast bowling kinematics and kinetics, trunk and lumbar anatomical features, anthropometric traits, age, and neuromuscular characteristics with low back pain and lumbar spine injury.ConclusionInconsistencies in results may be related to differences in study design, injury definitions, participant characteristics, measurement parameters, and statistical analyses. Low back pain and lumbar spine injury occurrence in fast bowlers remain high, and this may be due to an absence of low bias studies that have informed recommendations for their prevention. Future research should employ clearly defined injury outcomes, analyse continuous datasets, utilise models that better represent lumbar kinematics and kinetics during fast bowling, and better quantify previous injury, lumbar anatomical features and lumbar maturation.Trial registrationOpen Science Framework https://doi.org/10.17605/OSF.IO/ERKZ2 .
| S-EPMC10512628 | biostudies-literature
Project description:BackgroundNatural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.MethodsWe conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.ResultsWe present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.ConclusionsAutomated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.
| S-EPMC8176715 | biostudies-literature
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.
| S-EPMC8132982 | biostudies-literature
Project description:BackgroundDecision support systems (DSSs) for suggesting optimal treatments for individual patients with low back pain (LBP) are currently insufficiently accurate for clinical application. Most of the input provided to train these systems is based on patient-reported outcome measures. However, with the appearance of electronic health records (EHRs), additional qualitative data on reasons for referrals and patients' goals become available for DSSs. Currently, no decision support tools cover a wide range of biopsychosocial factors, including referral letter information to help clinicians triage patients to the optimal LBP treatment.ObjectiveThe objective of this study was to investigate the added value of including qualitative data from EHRs and referral letters to the accuracy of a quantitative DSS for patients with LBP.MethodsA retrospective study was conducted in a clinical cohort of Dutch patients with LBP. Patients filled out a baseline questionnaire about demographics, pain, disability, work status, quality of life, medication, psychosocial functioning, comorbidity, history, and duration of pain. Referral reasons and patient requests for help (patient goals) were extracted via natural language processing (NLP) and enriched in the data set. For decision support, these data were considered independent factors for triage to neurosurgery, anesthesiology, rehabilitation, or minimal intervention. Support vector machine, k-nearest neighbor, and multilayer perceptron models were trained for 2 conditions: with and without consideration of the referral letter content. The models' accuracies were evaluated via F1-scores, and confusion matrices were used to predict the treatment path (out of 4 paths) with and without additional referral parameters.ResultsData from 1608 patients were evaluated. The evaluation indicated that 2 referral reasons from the referral letters (for anesthesiology and rehabilitation intervention) increased the F1-score accuracy by up to 19.5% for triaging. The confusion matrices confirmed the results.ConclusionsThis study indicates that data enriching by adding NLP-based extraction of the content of referral letters increases the model accuracy of DSSs in suggesting optimal treatments for individual patients with LBP. Overall model accuracies were considered low and insufficient for clinical application.
| S-EPMC10865208 | biostudies-literature
Project description:Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015-2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.
| S-EPMC9046466 | biostudies-literature