Methods of Information in Medicine最新文献

{"title":"A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials.","authors":"Ryan J Urbanowicz,&nbsp;John H Holmes,&nbsp;Dina Appleby,&nbsp;Vanamala Narasimhan,&nbsp;Stephen Durborow,&nbsp;Nadine Al-Naamani,&nbsp;Melissa Fernando,&nbsp;Steven M Kawut","doi":"10.1055/s-0041-1739361","DOIUrl":"https://doi.org/10.1055/s-0041-1739361","url":null,"abstract":"<p><strong>Objective: </strong>Data harmonization is essential to integrate individual participant data from multiple sites, time periods, and trials for meta-analysis. The process of mapping terms and phrases to an ontology is complicated by typographic errors, abbreviations, truncation, and plurality. We sought to harmonize medical history (MH) and adverse events (AE) term records across 21 randomized clinical trials in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension.</p><p><strong>Methods: </strong>We developed and applied a semi-automated harmonization pipeline for use with domain-expert annotators to resolve ambiguous term mappings using exact and fuzzy matching. We summarized MH and AE term mapping success, including map quality measures, and imputation of a generalizing term hierarchy as defined by the applied Medical Dictionary for Regulatory Activities (MedDRA) ontology standard.</p><p><strong>Results: </strong>Over 99.6% of both MH (<i>N</i> = 37,105) and AE (<i>N</i> = 58,170) records were successfully mapped to MedDRA low-level terms. Automated exact matching accounted for 74.9% of MH and 85.5% of AE mappings. Term recommendations from fuzzy matching in the pipeline facilitated annotator mapping of the remaining 24.9% of MH and 13.8% of AE records. Imputation of the generalized MedDRA term hierarchy was unambiguous in 85.2% of high-level terms, 99.4% of high-level group terms, and 99.5% of system organ class in MH, and 75% of high-level terms, 98.3% of high-level group terms, and 98.4% of system organ class in AE.</p><p><strong>Conclusion: </strong>This pipeline dramatically reduced the burden of manual annotation for MH and AE term harmonization and could be adapted to other data integration efforts.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 1-02","pages":"3-10"},"PeriodicalIF":1.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9978994/pdf/nihms-1873072.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10276427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security and Privacy in Distributed Health Care Environments.","authors":"Stephen V Flowerday,&nbsp;Christos Xenakis","doi":"10.1055/a-1768-2966","DOIUrl":"https://doi.org/10.1055/a-1768-2966","url":null,"abstract":"N.A.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 1-02","pages":"1-2"},"PeriodicalIF":1.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9608470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Identification of Immunocompromised Status in Critically Ill Children.","authors":"Swaminathan Kandaswamy, Evan W. Orenstein, Elizabeth Quincer, A. Fernandez, Mark D. Gonzalez, LY Lu, R. Kamaleswaran, I. Banerjee, P. Jaggi","doi":"10.1055/a-1817-7208","DOIUrl":"https://doi.org/10.1055/a-1817-7208","url":null,"abstract":"BACKGROUND\u0000Easy identification of immunocompromised hosts (ICH) would allow for stratification of culture results based on host type.\u0000\u0000\u0000METHODS\u0000We utilized antimicrobial stewardship (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status.\u0000\u0000\u0000RESULTS\u0000We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as an ICH. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded sensitivity of 0.87 (08.6-0.88), specificity of 0.93 (0.92-0.93), and positive predictive value (PPV) of 0.74 (0.73-0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98 - 0.98) and PPV of 0.9 (0.88 - 0.91), but with decreased sensitivity 0.77 (0.76 - 0.79). There were 77 bacteremia episodes during the study period identified and a host specific visualization was created.\u0000\u0000\u0000CONCLUSIONS\u0000An EHR phenotype based on notes, diagnoses and medications identifies ICH in the PICU with high specificity.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43013005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A methodological approach to validate pneumonia encounters from radiology reports using Natural Language Processing (NLP).","authors":"A. Panny, H. Hegde, I. Glurich, F. Scannapieco, J. Vedre, J. Vanwormer, J. Miecznikowski, A. Acharya","doi":"10.1055/a-1817-7008","DOIUrl":"https://doi.org/10.1055/a-1817-7008","url":null,"abstract":"INTRODUCTION\u0000Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format.\u0000\u0000\u0000OBJECTIVE\u0000The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format.\u0000\u0000\u0000METHODS\u0000A pneumonia-specific Natural Language Processing (NLP) pipeline was strategically developed applying cTAKES to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: \"positive\", \"negative\" or \"not classified: requires manual review\" based on tagged concepts that support or refute diagnostic codes.\u0000\u0000\u0000RESULTS\u0000A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest x-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as 'Pneumonia-positive', 19% as (15401/81,707) as 'Pneumonia-negative' and 48% (39,209/81,707) as ''episode classification pending further manual review'. NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%).\u0000\u0000\u0000CONCLUSION\u0000The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43521863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Pneumonia Sub-types from Electronic Health Records Using Rule-based Algorithms.","authors":"H. Hegde, I. Glurich, A. Panny, J. Vedre, J. Vanwormer, R. Berg, F. Scannapieco, J. Miecznikowski, A. Acharya","doi":"10.1055/a-1801-2718","DOIUrl":"https://doi.org/10.1055/a-1801-2718","url":null,"abstract":"BACKGROUND\u0000International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations, where pneumonia is standardly subtyped by settings, exposures and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHR), frequently in non-structured formats including radiological interpretation or clinical notes that complicate electronic classification.\u0000\u0000\u0000OBJECTIVE\u0000The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR.\u0000\u0000\u0000METHODS\u0000Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for 'rule of two' pneumonia-related codes or one ICD code and radiologically-confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support sub-classification based on features including symptomatic patient point of entry into the healthcare system timing of pneumonia emergence and identification of clinical, laboratory or medication orders that informed definition of the pneumonia sub-classification algorithm.\u0000\u0000\u0000RESULTS\u0000Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following NLP classification of pneumonia status as 'negative' or 'unknown'. Subtyping of 83,387 episodes identified: community acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), healthcare-acquired (5%), ventilator-associated (0.4%) cases, and 9.4% were not classifiable by the algorithm.\u0000\u0000\u0000CONCLUSION\u0000Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44828042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the Representation of Network Assets in Health Care Environments Using Ontologies.","authors":"Lucía Prieto Santamaría,&nbsp;David Fernández Lobón,&nbsp;Antonio Jesús Díaz-Honrubia,&nbsp;Ernestina Menasalvas Ruiz,&nbsp;Sokratis Nifakos,&nbsp;Alejandro Rodríguez-González","doi":"10.1055/s-0041-1735621","DOIUrl":"https://doi.org/10.1055/s-0041-1735621","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies.</p><p><strong>Methods: </strong>Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network.</p><p><strong>Results: </strong>As a result of this work, the \"Software Defined Networking Description Language-CUREX Asset Discovery Tool Ontology\" (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc. CONCLUSION:  The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 S 02","pages":"e89-e102"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9b/d5/10-1055-s-0041-1735621.PMC8714298.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ontology Engineering for Gastric Dystemperament in Persian Medicine.","authors":"Hassan Shojaee-Mend,&nbsp;Haleh Ayatollahi,&nbsp;Azam Abdolahadi","doi":"10.1055/s-0041-1735168","DOIUrl":"https://doi.org/10.1055/s-0041-1735168","url":null,"abstract":"<p><strong>Objective: </strong>Developing an ontology can help collecting and sharing information in traditional medicine including Persian medicine in a well-defined format. The present study aimed to develop an ontology for gastric dystemperament in the Persian medicine.</p><p><strong>Methods: </strong>This was a mixed-methods study conducted in 2019. The first stage was related to providing an ontology requirements specification document. In the second stage, important terms, concepts, and their relationships were identified via literature review and expert panels. Then, the results derived from the second stage were refined and validated using the Delphi method in three rounds. Finally, in the fourth stage, the ontology was evaluated in terms of consistency and coherence.</p><p><strong>Results: </strong>In this study, 241 concepts related to different types of gastric dystemperament, diagnostic criteria, and treatments in the Persian medicine were identified through literature review and expert panels, and 12 new concepts were suggested during the Delphi study. In total, after performing three rounds of the Delphi study, 233 concepts were identified. Finally, an ontology was developed with 71 classes, and the results of the evaluation study revealed that the ontology was consistent and coherent.</p><p><strong>Conclusion: </strong>In this study, an ontology was created for gastric dystemperament in the Persian medicine. This ontology can be used for designing future systems, such as case-based reasoning and expert systems. Moreover, the use of other evaluation methods is suggested to construct a more complete and precise ontology.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 5-06","pages":"162-170"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39371833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Status of AI-Enabled Clinical Decision Support Systems Implementations in China.","authors":"Mengting Ji,&nbsp;Xiaoyun Chen,&nbsp;Georgi Z Genchev,&nbsp;Mingyue Wei,&nbsp;Guangjun Yu","doi":"10.1055/s-0041-1736461","DOIUrl":"https://doi.org/10.1055/s-0041-1736461","url":null,"abstract":"<p><strong>Background: </strong>AI-enabled Clinical Decision Support Systems (AI + CDSSs) were heralded to contribute greatly to the advancement of health care services. There is an increased availability of monetary funds and technical expertise invested in projects and proposals targeting the building and implementation of such systems. Therefore, understanding the actual system implementation status in clinical practice is imperative.</p><p><strong>Objectives: </strong>The aim of the study is to understand (1) the current situation of AI + CDSSs clinical implementations in Chinese hospitals and (2) concerns regarding AI + CDSSs current and future implementations.</p><p><strong>Methods: </strong>We investigated 160 tertiary hospitals from six provinces and province-level cities. Descriptive analysis, two-sided Fisher exact test, and Mann-Whitney <i>U</i>-test were utilized for analysis.</p><p><strong>Results: </strong>Thirty-eight of the surveyed hospitals (23.75%) had implemented AI + CDSSs. There were statistical differences on grade, scales, and medical volume between the two groups of hospitals (implemented vs. not-implemented AI + CDSSs, <i>p</i> <0.05). On the 5-point Likert scale, 81.58% (31/38) of respondents rated their overall satisfaction with the systems as \"just neutral\" to \"satisfied.\" The three most common concerns were system functions improvement and integration into the clinical process, data quality and availability, and methodological bias.</p><p><strong>Conclusion: </strong>While AI + CDSSs were not yet widespread in Chinese clinical settings, professionals recognize the potential benefits and challenges regarding in-hospital AI + CDSSs.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"60 5-06","pages":"123-132"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39569421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids.","authors":"Linyi Li,&nbsp;Adela Grando,&nbsp;Abeed Sarker","doi":"10.1055/s-0041-1740358","DOIUrl":"https://doi.org/10.1055/s-0041-1740358","url":null,"abstract":"<p><strong>Background: </strong>Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors.</p><p><strong>Objectives: </strong>The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets.</p><p><strong>Methods: </strong>We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F₁-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors.</p><p><strong>Results: </strong>Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F₁-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets-while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients.</p><p><strong>Conclusion: </strong>The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"e111-e119"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ac/c9/10-1055-s-0041-1740358.PMC8716187.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39771919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement Performance of Activity Measurements with Newer Generation of Apple Watch in Wheelchair Users with Spinal Cord Injury.","authors":"Nils-Hendrik Benning,&nbsp;Petra Knaup,&nbsp;Rüdiger Rupp","doi":"10.1055/s-0041-1740236","DOIUrl":"https://doi.org/10.1055/s-0041-1740236","url":null,"abstract":"<p><strong>Background: </strong>The level of physical activity (PA) of people with spinal cord injury (SCI) has an impact on long-term complications. Currently, PA is mostly assessed by interviews. Wearable activity trackers are promising tools to objectively measure PA under everyday conditions. The only off-the-shelf, wearable activity tracker with specific measures for wheelchair users is the Apple Watch.</p><p><strong>Objectives: </strong>This study analyzes the measurement performance of Apple Watch Series 4 for wheelchair users and compares it with an earlier generation of the device.</p><p><strong>Methods: </strong>Fifteen participants with subacute SCI during their first in-patient phase followed a test course using their wheelchair. The number of wheelchair pushes was counted manually by visual inspection and with the Apple Watch. Difference between the Apple Watch and the rater was analyzed with mean absolute percent error (MAPE) and a Bland-Altman plot. To compare the measurement error of Series 4 and an older generation of the device a <i>t</i>-test was calculated using data for Series 1 from a former study.</p><p><strong>Results: </strong>The average of differences was 12.33 pushes (<i>n</i> = 15), whereas participants pushed the wheelchair 138.4 times on average (range 86-271 pushes). The range of difference and the Bland-Altman plot indicate an overestimation by Apple Watch. MAPE is 9.20% and the <i>t</i>-test, testing for an effect of Series 4 on the percentage of error compared with Series 1, was significant with <i>p</i> < 0.05.</p><p><strong>Conclusion: </strong>Series 4 shows a significant improvement in measurement performance compared with Series 1. Series 4 can be considered as a promising data source to capture the number of wheelchair pushes on even grounds. Future research should analyze the long-term measurement performance during everyday conditions of Series 4.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"e103-e110"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39938638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}