Methods of Information in Medicine最新文献

{"title":"One Digital Health for more FAIRness.","authors":"Oscar Tamburis,&nbsp;Arriel Benis","doi":"10.1055/a-1938-0533","DOIUrl":"https://doi.org/10.1055/a-1938-0533","url":null,"abstract":"<p><strong>Background: </strong>One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.</p><p><strong>Objectives: </strong>This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.</p><p><strong>Methods: </strong>An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.</p><p><strong>Results: </strong>A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.</p><p><strong>Conclusions: </strong>ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e116-e124"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/85/69/10-1055-a-1938-0533.PMC9788917.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254115","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":"The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.","authors":"Toralf Kirsten,&nbsp;Frank Meineke,&nbsp;Henry Löffler-Wirth,&nbsp;Alexandr Uciteli,&nbsp;Christoph Beger,&nbsp;Sebastian Stäubert,&nbsp;Matthias Löbe,&nbsp;Rene Hänsel,&nbsp;Franziska G Rauscher,&nbsp;Judith Christina Schuster,&nbsp;Thomas Peschel,&nbsp;Heinrich Herre,&nbsp;Jonas Wagner,&nbsp;Silke Zachariae,&nbsp;Christoph Engel,&nbsp;Markus Scholz,&nbsp;Erhard Rahm,&nbsp;Hans Binder,&nbsp;Markus Löffler","doi":"10.1055/a-1914-1985","DOIUrl":"https://doi.org/10.1055/a-1914-1985","url":null,"abstract":"<p><strong>Background: </strong>Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains.</p><p><strong>Objective: </strong>The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects.</p><p><strong>Methods: </strong>Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups.</p><p><strong>Results: </strong>The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e103-e115"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bf/4e/10-1055-a-1914-1985.PMC9788914.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9308245","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":"FAIR Aspects of a Health Information Protection and Management System.","authors":"Jaime Delgado,&nbsp;Silvia Llorente","doi":"10.1055/s-0042-1758765","DOIUrl":"https://doi.org/10.1055/s-0042-1758765","url":null,"abstract":"<p><strong>Background: </strong>Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects.</p><p><strong>Objectives: </strong>The two main objectives of the presented work are the definition of a secure and interoperable modular architecture to manage different kinds of medical content (xIPAMS [x, for Any kind of content, Information Protection And Management System] and HIPAMS [Health Information Protection And Management System]), and the application of FAIR principles to that architecture in such a way that privacy and security are compatible with FAIR.</p><p><strong>Methods: </strong>We propose the concept of xIPAMS as a modular architecture, following standards for interoperability, which defines mechanisms for privacy, protection, storage, search, and access to health-related information.</p><p><strong>Results: </strong>xIPAMS provides FAIR principles and preserves patient's privacy. For each module, we identify how FAIR principles apply.</p><p><strong>Conclusions: </strong>We have analyzed how xIPAMS, and in particular HIPAMS (Health content), support the FAIR principles focusing on security and privacy. We have identified the FAIR principles supported by the different xIPAMS modules, concluding that the four principles are supported. Our analysis has also considered a possible implementation based on the concept of DACS (Document Access and Communication System), a system storing medical documents in a private and secure way. In addition, we have analyzed security aspects of the FAIRification process and how they are provided by xIPAMS modules.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e172-e182"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/99/13/10-1055-s-0042-1758765.PMC9788908.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9609042","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":"Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction.","authors":"William Hsu, Jim Warren, Patricia Riddle","doi":"10.1055/s-0042-1758687","DOIUrl":"10.1055/s-0042-1758687","url":null,"abstract":"<p><strong>Background: </strong>Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series.</p><p><strong>Objective: </strong>The aim of this study is to examine whether by explicitly modelling the temporal dimension of patient history event prediction may be improved.</p><p><strong>Methods: </strong>This study investigates methods for multivariate sequential modelling with a particular emphasis on long short-term memory (LSTM) recurrent neural networks. Data from a CVD decision support tool is linked to routinely collected national datasets including pharmaceutical dispensing, hospitalization, laboratory test results, and deaths. The study uses a 2-year observation and a 5-year prediction window. Selected methods are applied to the linked dataset. The experiments performed focus on CVD event prediction. CVD death or hospitalization in a 5-year interval was predicted for patients with history of lipid-lowering therapy.</p><p><strong>Results: </strong>The results of the experiments showed temporal models are valuable for CVD event prediction over a 5-year interval. This is especially the case for LSTM, which produced the best predictive performance among all models compared achieving AUROC of 0.801 and average precision of 0.425. The non-temporal model comparator ridge classifier (RC) trained using all quarterly data or by aggregating quarterly data (averaging time-varying features) was highly competitive achieving AUROC of 0.799 and average precision of 0.420 and AUROC of 0.800 and average precision of 0.421, respectively.</p><p><strong>Conclusion: </strong>This study provides evidence that the use of deep temporal models particularly LSTM in clinical decision support for chronic disease would be advantageous with LSTM significantly improving on commonly used regression models such as logistic regression and Cox proportional hazards on the task of CVD event prediction.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e149-e171"},"PeriodicalIF":1.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/af/00/10-1055-s-0042-1758687.PMC9788915.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247803","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":"DxGenerator: An Improved Differential Diagnosis Generator for Primary Care Based on MetaMap and Semantic Reasoning.","authors":"Ali Sanaeifar,&nbsp;Saeid Eslami,&nbsp;Mitra Ahadi,&nbsp;Mohsen Kahani,&nbsp;Hassan Vakili Arki","doi":"10.1055/a-1905-5639","DOIUrl":"https://doi.org/10.1055/a-1905-5639","url":null,"abstract":"<p><strong>Background: </strong>In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list.</p><p><strong>Objective: </strong>This study aimed to design and evaluate a novel practical web-based differential diagnosis generator solution in primary care.</p><p><strong>Methods: </strong>In this research, a new online clinical decision support system, called DxGenerator, was designed to improve diagnostic accuracy; to this end, an attempt was made to converge a semantic database with the unified medical language system (UMLS) knowledge base, using MetaMap tool and natural language processing. In this regard, 120 diseases of gastrointestinal organs causing abdominal pain were modeled into the database. After designing an inference engine and a pseudo-free-text interactive interface, 172 patient vignettes were inputted into DxGenerator and ISABEL, the most accurate similar system. The Wilcoxon signed ranked test was used to compare the position of correct diagnoses in DxGenerator and ISABEL. The α level was defined as 0.05.</p><p><strong>Results: </strong>On a total of 172 vignettes, the mean and standard deviation of correct diagnosis positions improved from 4.2 ± 5.3 in ISABEL to 3.2 ± 3.9 in DxGenerator. This improvement was significant in the subgroup of uncommon diseases (<i>p</i>-value < 0.05).</p><p><strong>Conclusion: </strong>Using UMLS knowledge base and MetaMap Tools can improve the accuracy of diagnostic systems in which terms are entered in a free text manner. Applying these new methods will help the medical community accept medical diagnostic systems better.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 5-06","pages":"174-184"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9253490","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":"Use of Machine Learning to Identify Clinical Variables in Pregnant and Non-Pregnant Women with SARS-CoV-2 Infection.","authors":"Itamar D Futterman,&nbsp;Rodney McLaren,&nbsp;Hila Friedmann,&nbsp;Nael Musleh,&nbsp;Shoshana Haberman","doi":"10.1055/s-0042-1756282","DOIUrl":"https://doi.org/10.1055/s-0042-1756282","url":null,"abstract":"<p><strong>Objective: </strong>The aim of the study is to identify the important clinical variables found in both pregnant and non-pregnant women who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, using an artificial intelligence (AI) platform.</p><p><strong>Methods: </strong>This was a retrospective cohort study of all women between the ages of 18 to 45, who were admitted to Maimonides Medical Center between March 10, 2020 and December 20, 2021. Patients were included if they had nasopharyngeal PCR swab positive for SARS-CoV-2. Safe People Artificial Intelligence (SPAI) platform, developed by Gynisus, Inc., was used to identify key clinical variables predicting a positive test in pregnant and non-pregnant women. A list of mathematically important clinical variables was generated for both non-pregnant and pregnant women.</p><p><strong>Results: </strong>Positive results were obtained in 1,935 non-pregnant women and 1,909 non-pregnant women tested negative for SARS-CoV-2 infection. Among pregnant women, 280 tested positive, and 1,000 tested negative. The most important clinical variable to predict a positive swab result in non-pregnant women was age, while elevated D-dimer levels and presence of an abnormal fetal heart rate pattern were the most important clinical variable in pregnant women to predict a positive test.</p><p><strong>Conclusion: </strong>In an attempt to better understand the natural history of the SARS-CoV-2 infection we present a side-by-side analysis of clinical variables found in pregnant and non-pregnant women who tested positive for COVID-19. These clinical variables can help stratify and highlight those at risk for SARS-CoV-2 infection and shed light on the individual patient risk for testing positive.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"61-67"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33462790","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":"An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives.","authors":"Kavya Ramisetty,&nbsp;Jabez Christopher,&nbsp;Subhrakanta Panda,&nbsp;Baktha Singh Lazarus,&nbsp;Julie Dayalan","doi":"10.1055/s-0042-1756650","DOIUrl":"https://doi.org/10.1055/s-0042-1756650","url":null,"abstract":"<p><strong>Background: </strong>Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.</p><p><strong>Objectives: </strong>This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.</p><p><strong>Methods: </strong>The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.</p><p><strong>Results: </strong>Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.</p><p><strong>Conclusion: </strong>The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"111-122"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33522531","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 Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow.","authors":"Carmelo Macri,&nbsp;Ian Teoh,&nbsp;Stephen Bacchi,&nbsp;Michelle Sun,&nbsp;Dinesh Selva,&nbsp;Robert Casson,&nbsp;WengOnn Chan","doi":"10.1055/s-0042-1749358","DOIUrl":"https://doi.org/10.1055/s-0042-1749358","url":null,"abstract":"<p><strong>Background: </strong>Clinical procedures are often performed in outpatient clinics without prior scheduling at the administrative level, and documentation of the procedure often occurs solely in free-text clinical electronic notes. Natural language processing (NLP), particularly named entity recognition (NER), may provide a solution to extracting procedure data from free-text electronic notes.</p><p><strong>Methods: </strong>Free-text notes from outpatient ophthalmology visits were collected from the electronic clinical records at a single institution over 3 months. The Prodigy low-code annotation tool was used to create an annotation dataset and train a custom NER model for clinical procedures. Clinical procedures were extracted from the entire set of clinical notes.</p><p><strong>Results: </strong>There were a total of 5,098 clinic notes extracted for the study period; 1,923 clinic notes were used to build the NER model, which included a total of 231 manual annotations. The NER model achieved an F-score of 0.767, a precision of 0.810, and a recall of 0.729. The most common procedures performed included intravitreal injections of therapeutic substances, removal of corneal foreign bodies, and epithelial debridement of corneal ulcers.</p><p><strong>Conclusion: </strong>The use of a low-code annotation software tool allows the rapid creation of a custom annotation dataset to train a NER model to identify clinical procedures stored in free-text electronic clinical notes. This enables clinicians to rapidly gather previously unidentified procedural data for quality improvement and auditing purposes. Low-code annotation tools may reduce time and coding barriers to clinician participation in NLP research.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"84-89"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33463215","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":"Eliciting Information Needs of Child Patients: Adapting the Kano Model to the Design of mHealth Applications.","authors":"Sune Dueholm Müller,&nbsp;Georgios Tsirozidis,&nbsp;Morten Mathiasen,&nbsp;Louise Nordenhof,&nbsp;Daniel Jakobsen,&nbsp;Birgitte Mahler","doi":"10.1055/s-0042-1749359","DOIUrl":"https://doi.org/10.1055/s-0042-1749359","url":null,"abstract":"<p><strong>Background: </strong>Health care services are increasingly being digitized, but extant literature shows that digital technologies and applications are often developed without careful consideration of user needs. Research is needed to identify and investigate best-in-class methods to support user-centered design of mHealth applications.</p><p><strong>Objectives: </strong>The article investigates how the Kano model can be adapted and used for the purpose of eliciting child patients' information needs during the design phase of mHealth application development. The aim is to demonstrate its applicability for collecting and analyzing patient-centered data that are key to designing technology-supported solutions for health management.</p><p><strong>Methods: </strong>The article is based on a mixed-methods case study, which includes interviews with 21 patients aged 6 to 18. Structured interviews are analyzed based on prescriptions of the Kano model. Semi-structured interviews about child patients' information needs are analyzed thematically.</p><p><strong>Results: </strong>The results demonstrate several improvements to the Kano model that take into account the difficulties of effectively communicating with child patients. The combination of two types of interviews offers unique insights into the <i>what</i>, <i>how</i>, and <i>why</i> of patients' needs. Adaptation of the Kano model, simplification of response options, and participation of child patients' parents in interviews facilitate data collection.</p><p><strong>Conclusion: </strong>The article shows how the Kano model can be adapted to provide an effective means of eliciting child patients' needs. Adapting the model by combining structured and semi-structured interviews makes it a powerful tool in designing mHealth applications.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":"123-138"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33522530","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 Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.","authors":"Brian L Thomas, Lawrence B Holder, Diane J Cook","doi":"10.1055/s-0042-1756649","DOIUrl":"10.1055/s-0042-1756649","url":null,"abstract":"<p><strong>Background: </strong>Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.</p><p><strong>Objective: </strong>The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.</p><p><strong>Methods: </strong>We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.</p><p><strong>Results: </strong>We validate our approach using continuous smartwatch data for <i>n</i> = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from <i>r</i> = 0.1230 to <i>r</i> = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 3-04","pages":"99-110"},"PeriodicalIF":1.3,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847015/pdf/nihms-1862055.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10616391","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}