bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping
Authors: Horne Elsie , Tibble Holly , Sheikh Aziz , Tsanas Athanasios 
Published: May 2020

Background
In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.

Objective
This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.

Methods
We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process.

Results
Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification.

Conclusions
This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.
A Retrospective Analysis of Provider-to-Patient Secure Messages: How Much Are They Increasing, Who Is Doing the Work, and Is the Work Happening After Hours?
Authors: North Frederick , Luhman Kristine E , Mallmann Eric A , Mallmann Toby J , Tulledge-Scheitel Sidna M , North Emily J , Pecina Jennifer L 
Published: May 2020

Background
Patient portal registration and the use of secure messaging are increasing. However, little is known about how the work of responding to and initiating patient messages is distributed among care team members and how these messages may affect work after hours.

Objective
This study aimed to examine the growth of secure messages and determine how the work of provider responses to patient-initiated secure messages and provider-initiated secure messages is distributed across care teams and across work and after-work hours.

Methods
We collected secure messages sent from providers from January 1, 2013, to March 15, 2018, at Mayo Clinic, Rochester, Minnesota, both in response to patient secure messages and provider-initiated secure messages. We examined counts of messages over time, how the work of responding to messages and initiating messages was distributed among health care workers, messages sent per provider, messages per unique patient, and when the work was completed (proportion of messages sent after standard work hours).

Results
Portal registration for patients having clinic visits increased from 33% to 62%, and increasingly more patients and providers were engaged in messaging. Provider message responses to individual patients increased significantly in both primary care and specialty practices. Message responses per specialty physician provider increased from 15 responses per provider per year to 53 responses per provider per year from 2013 to 2018, resulting in a 253% increase. Primary care physician message responses increased from 153 per provider per year to 322 from 2013 to 2018, resulting in a 110% increase. Physicians, nurse practitioners, physician assistants, and registered nurses, all contributed to the substantial increases in the number of messages sent.

Conclusions
Provider-sent secure messages at a large health care institution have increased substantially since implementation of secure messaging between patients and providers. The effort of responding to and initiating messages to patients was distributed across multiple provider categories. The percentage of message responses occurring after hours showed little substantial change over time compared with the overall increase in message volume.
Use of Smartphones to Detect Diabetic Retinopathy: Scoping Review and Meta-Analysis of Diagnostic Test Accuracy Studies
Authors: Tan Choon Han , Kyaw Bhone Myint , Smith Helen , Tan Colin S , Tudor Car Lorainne 
Published: May 2020

Background
Diabetic retinopathy (DR), a common complication of diabetes mellitus, is the leading cause of impaired vision in adults worldwide. Smartphone ophthalmoscopy involves using a smartphone camera for digital retinal imaging. Utilizing smartphones to detect DR is potentially more affordable, accessible, and easier to use than conventional methods.

Objective
This study aimed to determine the diagnostic accuracy of various smartphone ophthalmoscopy approaches for detecting DR in diabetic patients.

Methods
We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for literature published from January 2000 to November 2018. We included studies involving diabetic patients, which compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting DR to an accurate or commonly employed reference standard, such as indirect ophthalmoscopy, slit-lamp biomicroscopy, and tabletop fundus photography. Two reviewers independently screened studies against the inclusion criteria, extracted data, and assessed the quality of included studies using the Quality Assessment of Diagnostic Accuracy Studies–2 tool, with disagreements resolved via consensus. Sensitivity and specificity were pooled using the random effects model. A summary receiver operating characteristic (SROC) curve was constructed. This review is reported in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies guidelines.

Results
In all, nine studies involving 1430 participants were included. Most studies were of high quality, except one study with limited applicability because of its reference standard. The pooled sensitivity and specificity for detecting any DR was 87% (95% CI 74%-94%) and 94% (95% CI 81%-98%); mild nonproliferative DR (NPDR) was 39% (95% CI 10%-79%) and 95% (95% CI 91%-98%); moderate NPDR was 71% (95% CI 57%-81%) and 95% (95% CI 88%-98%); severe NPDR was 80% (95% CI 49%-94%) and 97% (95% CI 88%-99%); proliferative DR (PDR) was 92% (95% CI 79%-97%) and 99% (95% CI 96%-99%); diabetic macular edema was 79% (95% CI 63%-89%) and 93% (95% CI 82%-97%); and referral-warranted DR was 91% (95% CI 86%-94%) and 89% (95% CI 56%-98%). The area under SROC curve ranged from 0.879 to 0.979. The diagnostic odds ratio ranged from 11.3 to 1225.

Conclusions
We found heterogeneous evidence showing that smartphone ophthalmoscopy performs well in detecting DR. The diagnostic accuracy for PDR was highest. Future studies should standardize reference criteria and classification criteria and evaluate other available forms of smartphone ophthalmoscopy in primary care settings.
Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study
Authors: Rawtaer Iris , Mahendran Rathi , Kua Ee Heok , Tan Hwee Pink , Tan Hwee Xian , Lee Tih-Shih , Ng Tze Pin 
Published: May 2020

Background
Dementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.

Objective
The aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.

Methods
We recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered.

Results
Of the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system.

Conclusions
We demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information.
Expression of Genes Related to Lipid Handling and the Obesity Paradox in Melanoma: Database Analysis
Authors: Giampietri Claudia , Tomaipitinca Luana , Scatozza Francesca , Facchiano Antonio 
Published: May 2020

BackgroundPublicly available genomic and transcriptomic data in searchable databases allow researchers to investigate specific medical issues in thousands of patients. Many studies have highlighted the role lipids play in cancer initiation and progression and reported nutritional interventions aimed at improving prognosis and survival. Therefore, there is an increasing interest in the role that fat intake may play in cancer. It is known that there is a relationship between BMI and survival in patients with cancer, and that there is an association between a high-fat diet and increased cancer risk. In some cancers, such as colorectal cancer, obesity and high fat intake are known to increase the risk of cancer initiation and progression. On the contrary, in patients undergoing treatment for melanoma, a higher BMI unexpectedly acts as a protective factor rather than a risk factor; this phenomenon is known as the obesity paradox.ObjectiveWe aimed to identify the molecular mechanism underlying the obesity paradox, with the expectation that this could indicate new effective strategies to reduce risk factors and improve protective approaches.MethodsIn order to determine the genes potentially involved in this process, we investigated the expression values of lipid-related genes in patients with melanoma or colorectal cancer. We used available data from 2990 patients from 3 public databases (IST [In Silico Transcriptomics] Online, GEO [Gene Expression Omnibus], and Oncomine) in an analysis that involved 3 consecutive validation steps. Of this group, data from 1410 individuals were analyzed in the IST Online database (208 patients with melanoma and 147 healthy controls, as well as 991 patients with colorectal cancer and 64 healthy controls). In addition, 45 melanoma, 18 nevi, and 7 healthy skin biopsies were analyzed in another database, GEO, to validate the IST Online data. Finally, using the Oncomine database, 318 patients with melanoma (312 controls) and 435 patients with colorectal cancer (445 controls) were analyzed.ResultsIn the first and second database investigated (IST Online and GEO, respectively), patients with melanoma consistently showed significantly (P<.001) lower expression levels of 4 genes compared to healthy controls: CD36, MARCO, FABP4, and FABP7. This strong reduction was not observed in patients with colorectal cancer. An additional analysis was carried out on a DNA-TCGA data set from the Oncomine database, further validating CD36 and FABP4.ConclusionsThe observed lower expression of genes such as CD36 and FABP4 in melanoma may reduce the cellular internalization of fat and therefore make patients with melanoma less sensitive to a high dietary fat intake, explaining in part the obesity paradox observed in patients with melanoma.
Mobile App for Monitoring 3-Month Postoperative Functional Outcome After Hip Fracture: Usability Study
Authors: Geerds Merle A J , Nijmeijer Wieke S , Hegeman J H , Vollenbroek-Hutten Miriam M R 
Published: May 2020

BackgroundAs a result of an aging population, there has been an increasing incidence of hip fractures worldwide. In the Netherlands, in order to improve the quality of care for elderly patients with hip fractures, the multidisciplinary Centre for Geriatric Traumatology was established in 2008 at the Department of Trauma Surgery at Ziekenhuisgroep Twente hospital (located in Almelo and Hengelo in the Netherlands).ObjectiveThough the Dutch Hip Fracture audit is used to monitor the quality of care for patients with fractures of the hip, only 30.7% of patients complete registration in the 3-month follow-up period. Mobile apps offer an opportunity for improvement in this area. The aim of this study was to investigate the usability and acceptance of a mobile app for gathering indicators of quality of care in a 3-month follow-up period after postoperative treatment of hip fracture.MethodsFrom July 2017 to December 2017, patients who underwent surgical treatment for hip fracture were recruited. Patients and caregivers, who were collectively considered the participant cohort, were asked to download the app and answer a questionnaire. Participants were divided into two groups—those who downloaded the app and those who did not download the app. A telephone interview that was based upon the Unified Theory of Acceptance and Use of Technology was conducted with a subset of participants from each group (1:1 ratio). This study was designated as not being subject to the Dutch Medical Research Involving Human Subjects Act according to the appropriate medical research ethics committees.ResultsOf the patients and caregivers who participated, 26.4% (29/110) downloaded the app, whereas 73.6% (81/110) did not. Telephone interviews with the subset of participants (n=24 per group) revealed that 54.0% (13/24) of the group of participants who did not download the app had forgotten the study. Among the group who downloaded the app, 95.8% (23/24) had the intention of completing the questionnaire, but only 4.2% (1/24) did so. The reasons for not completing the questionnaire included technical problems, cognitive disorders, or patient dependency on caregivers. Most participants in the group who downloaded the app self-reported a high level of expertise in using a smartphone (22/24, 91.7%), and sufficient facilitating conditions for using a smartphone were self-reported in both groups (downloaded the app: 23/24, 95.8%; did not download the app: 21/24, 87.5%), suggesting that these factors were not barriers to completion.ConclusionsDespite self-reported intention to use the app, smartphone expertise, and sufficient facilitating conditions for smartphone use, implementation of the mobile app was infeasible for daily practice. This was due to a combination of technical problems, factors related to the implementation process, and the population of interest having cognitive disorders or a dependency on caregivers for mobile technology.
An Algorithm (LaD) for Monitoring Childbirth in Settings Where Tracking All Parameters in the World Health Organization Partograph Is Not Feasible: Design and Expert Validation
Authors: Balikuddembe Michael S , Wakholi Peter K , Tumwesigye Nazarius M , Tylleskar Thorkild 
Published: May 2020

Background
After determining the key childbirth monitoring items from experts, we designed an algorithm (LaD) to represent the experts’ suggestions and validated it. In this paper we describe an abridged algorithm for labor and delivery management and use theoretical case to compare its performance with human childbirth experts.

Objective
The objective of this study was to describe the LaD algorithm, its development, and its validation. In addition, in the validation phase we wanted to assess if the algorithm was inferior, equivalent, or superior to human experts in recommending the necessary clinical actions during childbirth decision making.

Methods
The LaD algorithm encompasses the tracking of 6 of the 12 childbirth parameters monitored using the World Health Organization (WHO) partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts’ selections were compared to obtain pairwise sensitivity and false-positive rates (FPRs) between them and the algorithm.

Results
The mean weighted pairwise sensitivity among experts was 68.2% (SD 6.95; 95% CI 59.6-76.8), whereas that between experts and the LaD algorithm was 69.4% (SD 17.95; 95% CI 47.1-91.7). The pairwise FPR among the experts ranged from 12% to 33% with a mean of 23.9% (SD 9.14; 95% CI 12.6-35.2), whereas that between experts and the algorithm ranged from 18% to 43% (mean 26.3%; SD 10.4; 95% CI 13.3-39.3). The was a correlation (mean 0.67 [SD 0.06]) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient (α) of .91.

Conclusions
The LaD algorithm was more sensitive, but had a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer WHO-recommended parameters may not be inferior to human experts in labor and delivery clinical decision support.
The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study
Authors: Nguyen Thu T , Adams Nikki , Huang Dina , Glymour M Maria , Allen Amani M , Nguyen Quynh C 
Published: May 2020

Background
In the United States, racial disparities in birth outcomes persist and have been widening. Interpersonal and structural racism are leading explanations for the continuing racial disparities in birth outcomes, but research to confirm the role of racism and evaluate trends in the impact of racism on health outcomes has been hampered by the challenge of measuring racism. Most research on discrimination relies on self-reported experiences of discrimination, and few studies have examined racial attitudes and bias at the US national level.

Objective
This study aimed to investigate the associations between state-level Twitter-derived sentiments related to racial or ethnic minorities and birth outcomes.

Methods
We utilized Twitter’s Streaming application programming interface to collect 26,027,740 tweets from June 2015 to December 2017, containing at least one race-related term. Sentiment analysis was performed using support vector machine, a supervised machine learning model. We constructed overall indicators of sentiment toward minorities and sentiment toward race-specific groups. For each year, state-level Twitter-derived sentiment data were merged with birth data for that year. The study participants were women who had singleton births with no congenital abnormalities from 2015 to 2017 and for whom data were available on gestational age (n=9,988,030) or birth weight (n=9,985,402). The main outcomes were low birth weight (birth weight ≤2499 g) and preterm birth (gestational age <37 weeks). We estimated the incidence ratios controlling for individual-level maternal characteristics (sociodemographics, prenatal care, and health behaviors) and state-level demographics, using log binomial regression models.

Results
The accuracy for identifying negative sentiments on comparing the machine learning model to manually labeled tweets was 91%. Mothers living in states in the highest tertile for negative sentiment tweets referencing racial or ethnic minorities had greater incidences of low birth weight (8% greater, 95% CI 4%-13%) and preterm birth (8% greater, 95% CI 0%-14%) compared with mothers living in states in the lowest tertile. More negative tweets referencing minorities were associated with adverse birth outcomes in the total population, including non-Hispanic white people and racial or ethnic minorities. In stratified subgroup analyses, more negative tweets referencing specific racial or ethnic minority groups (black people, Middle Eastern people, and Muslims) were associated with poor birth outcomes for black people and minorities.

Conclusions
A negative social context related to race was associated with poor birth outcomes for racial or ethnic minorities, as well as non-Hispanic white people.