Am J Clin Nutr. 2024 Feb;119(2):444-455. doi: 10.1016/j.ajcnut.2023.12.009. Epub 2023 Dec 20.

ABSTRACT

BACKGROUND: Preterm, low-birth weight (LBW) and small-for-gestational age (SGA) newborns have a higher frequency of adverse health outcomes, including linear and ponderal growth impairment.

OBJECTIVE: To describe the growth trajectories and to estimate catch-up growth during the first 5 y of life of small newborns according to 3 vulnerability phenotypes (preterm, LBW, SGA).

METHODS: Longitudinal study using linked data from the 100 Million Brazilian Cohort baseline, the Brazilian National Live Birth System (SINASC), and the Food and Nutrition Surveillance System (SISVAN) from 2011 to 2017. We estimated the length/height-for-age (L/HAZ) and weight-for-age z-score (WAZ) trajectories from children of 6-59 mo using the linear mixed model for each vulnerable newborn phenotype. Growth velocity for both L/HAZ and WAZ was calculated considering the change (Δ) in the mean z-score between 2 time points. Catch-up growth was defined as a change in z-score > 0.67 at any time during follow-up.

RESULTS: We analyzed 2,021,998 live born children and 8,726,599 observations. The prevalence of at least one of the vulnerable phenotypes was 16.7% and 0.6% were simultaneously preterm, LBW, and SGA. For those born at term, all phenotypes had a period of growth recovery from 12 mo. For preterm infants, the onset of L/HAZ growth recovery started later at 24 mo and the growth trajectories appear to be lower than those born at term, a condition aggravated among children with the 3 phenotypes. Preterm and female infants seem to experience slower growth recovery than those born at term and males. The catch-up growth occurs at 24-59 mo for males preterm: preterm + AGA + NBW (Δ = 0.80), preterm + AGA + LBW (Δ = 0.88), and preterm + SGA + LBW (Δ = 1.08); and among females: term + SGA + NBW (Δ = 0.69), term + AGA + LBW (Δ = 0.72), term + SGA + LBW (Δ = 0.77), preterm + AGA + LBW (Δ = 0.68), and preterm + SGA + LBW (Δ = 0.83).

CONCLUSIONS: Children born preterm seem to reach L/HAZ and WAZ growth trajectories lower than those attained by children born at term, a condition aggravated among the most vulnerable.

PMID:38128734 | DOI:10.1016/j.ajcnut.2023.12.009

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2017 Nov;2017:1280-1283. doi: 10.1109/bibm.2017.8217844. Epub 2017 Dec 18.

ABSTRACT

Emotions influence our perceptions and decisions and are often felt more strongly in situations related to healthcare. Therefore, it is important to understand how both providers and patients express their emotions in face-to-face scenarios. An ontology is a way to represent domain concepts and the relationships between them in a polyarchical manner. We have created an ontological model called the Visualized Emotion Ontology (VEO) that expresses the semantic definitions and visualizations of 25 emotions based on published research. With VEO, we can augment patient-facing software tools, like embodied conversational agents, to improve patient-provider interaction in clinical environments.

PMID:38125740 | PMC:PMC10732717 | DOI:10.1109/bibm.2017.8217844

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2019 Nov;2019:1177-1178. doi: 10.1109/bibm47256.2019.8983220. Epub 2020 Feb 6.

ABSTRACT

The Consumer Health Vocabulary has been an important contribution to the health informatics field since its introduction in 2006. Many studies have utilized the vocabulary for various scientific research to bridge the gap between consumers and health experts. Given the flat file format of the Consumer Health Vocabulary dataset, we developed a SKOS-based ontology of the dataset. As an ontology, this dataset can be semantically linked to other resources to provide consumer-level meaning. In addition with this artifact, we plan to further expand the terminology.

PMID:38125584 | PMC:PMC10732699 | DOI:10.1109/bibm47256.2019.8983220

Pharmacoepidemiol Drug Saf. 2024 Jan;33(1):e5734. doi: 10.1002/pds.5734. Epub 2023 Dec 19.

ABSTRACT

PURPOSE: Observational studies assessing effects of medical products on suicidal behavior often rely on health record data to account for pre-existing risk. We assess whether high-dimensional models predicting suicide risk using data derived from insurance claims and electronic health records (EHRs) are superior to models using data from insurance claims alone.

METHODS: Data were from seven large health systems identified outpatient mental health visits by patients aged 11 or older between 1/1/2009 and 9/30/2017. Data for the 5 years prior to each visit identified potential predictors of suicidal behavior typically available from insurance claims (e.g., mental health diagnoses, procedure codes, medication dispensings) and additional potential predictors available from EHRs (self-reported race and ethnicity, responses to Patient Health Questionnaire or PHQ-9 depression questionnaires). Nonfatal self-harm events following each visit were identified from insurance claims data and fatal self-harm events were identified by linkage to state mortality records. Random forest models predicting nonfatal or fatal self-harm over 90 days following each visit were developed in a 70% random sample of visits and validated in a held-out sample of 30%. Performance of models using linked claims and EHR data was compared to models using claims data only.

RESULTS: Among 15 845 047 encounters by 1 574 612 patients, 99 098 (0.6%) were followed by a self-harm event within 90 days. Overall classification performance did not differ between the best-fitting model using all data (area under the receiver operating curve or AUC = 0.846, 95% CI 0.839-0.854) and the best-fitting model limited to data available from insurance claims (AUC = 0.846, 95% CI 0.838-0.853). Competing models showed similar classification performance across a range of cut-points and similar calibration performance across a range of risk strata. Results were similar when the sample was limited to health systems and time periods where PHQ-9 depression questionnaires were recorded more frequently.

CONCLUSION: Investigators using health record data to account for pre-existing risk in observational studies of suicidal behavior need not limit that research to databases including linked EHR data.

PMID:38112287 | PMC:PMC10843611 | DOI:10.1002/pds.5734

Orthop J Sports Med. 2023 Dec 15;11(12):23259671231215820. doi: 10.1177/23259671231215820. eCollection 2023 Dec.

ABSTRACT

BACKGROUND: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets.

PURPOSE/HYPOTHESIS: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements.

STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2.

METHODS: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images.

RESULTS: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds.

CONCLUSION: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

PMID:38107846 | PMC:PMC10725654 | DOI:10.1177/23259671231215820

Fertil Steril. 2024 Feb;121(2):314-322. doi: 10.1016/j.fertnstert.2023.11.008. Epub 2023 Dec 12.

ABSTRACT

OBJECTIVE: To study the fertility treatment pathways used by women with and without polycystic ovary syndrome (PCOS) and which pathways were more likely to result in a birth.

DESIGN: This retrospective national community-based cohort study used longitudinal self-report survey data (collected 1996-2022; aged 18-49 years) from women born in 1973-1978 who are participants in the Australian Longitudinal Study on Women's Health. The study also used linked administrative data on fertility treatments (1996-2021).

PATIENTS: Of the 8,463 eligible women, 1,109 accessed fertility treatment and were included.

EXPOSURE: Polycystic ovary syndrome diagnosis was self-reported.

MAIN OUTCOME MEASURE: use of ovulation induction (OI), intrauterine insemination, and/or in vitro fertilization (IVF) was established through linked administrative data. Births were self-reported.

RESULTS: One in 10 of the eligible participants had PCOS (783/7,987, 10%) and 1 in 4 of the women who used fertility treatment had PCOS (274/1,109, 25%). Women with PCOS were 3 years younger on average at first fertility treatment (M = 31.4 years, SD = 4.18) than women without PCOS (M = 34.2 years, SD = 4.56). Seven treatment pathways were identified and use differed by PCOS status. Women with PCOS were more likely to start with OI (71%; odds ratio [OR] 4.20, 95% confidence interval [CI]: 2.91, 6.07) than women without PCOS (36%). Of the women with PCOS who started with OI, 46% required additional types of treatment. More women without PCOS ended up in IVF (72% vs. 51%). Overall, 63% (701/1,109) had an attributed birth, and in adjusted regressions births did not vary by last type of treatment (IVF: 67%, reference; intrauterine insemination: 67%, OR 0.94 95% CI: 0.56, 1.58; OI: 61%, OR 0.71, 95% CI: 0.52, 0.98), or by PCOS status (OR 1.27, 95% CI: 0.91, 1.77). By age, 74% of women under 35 years (471/639) and 49% of women 35 years or older had a birth.

CONCLUSION: More women with PCOS used fertility treatment but births were equivalent to women without PCOS. Most women followed clinical recommendations. Births did not differ between pathways, so there was no disadvantage in starting with less invasive treatments (although there may be financial or emotional disadvantages).

PMID:38099868 | DOI:10.1016/j.fertnstert.2023.11.008

Seizure. 2023 Dec 7;114:84-89. doi: 10.1016/j.seizure.2023.11.022. Online ahead of print.

ABSTRACT

OBJECTIVE: A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions.

METHOD: Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and spoken interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Inspired by previous qualitative research three spoken language based feature sets were designed to assess: (1) formulation effort, (2) the proportion of words from different semantic categories, and (3) verb, adverb, and adjective usage.

RESULTS: 76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8 % of all diagnoses, but the inclusion of the language features increased the accuracy to 85.5 % by improving the differential diagnosis between epilepsy and FDS.

CONCLUSION: These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as ensuring appropriate referral to cardiological versus neurological investigation and management pathways).

PMID:38091849 | DOI:10.1016/j.seizure.2023.11.022

JMIR Res Protoc. 2023 Dec 13;12:e50797. doi: 10.2196/50797.

ABSTRACT

BACKGROUND: There are still unanswered questions regarding effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning. Additionally, understanding how this process can be analyzed and assessed is crucial, particularly considering the rapid growth of natural language processing in artificial intelligence.

OBJECTIVE: The aim of this study is to map educational strategies to promote the transformation and articulation of clinical data among students and health care professionals and to explore the methods used to assess these individuals' transformation and articulation of clinical data.

METHODS: This scoping review follows the Joanna Briggs Institute framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist for the analysis. A literature search was performed in November 2022 using 5 databases: CINAHL (EBSCOhost), MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), and Web of Science (Clarivate). The protocol was registered on the Open Science Framework in November 2023. The scoping review will follow the 9-step framework proposed by Peters and colleagues of the Joanna Briggs Institute. A data extraction form has been developed using key themes from the research questions.

RESULTS: After removing duplicates, the initial search yielded 6656 results, and study selection is underway. The extracted data will be qualitatively analyzed and presented in a diagrammatic or tabular form alongside a narrative summary. The review will be completed by February 2024.

CONCLUSIONS: By synthesizing the evidence on semantic transformation and articulation of clinical data during clinical reasoning education, this review aims to contribute to the refinement of educational strategies and assessment methods used in academic and continuing education programs. The insights gained from this review will help educators develop more effective semantic approaches for teaching or learning clinical reasoning, as opposed to fragmented, purely symptom-based or probabilistic approaches. Besides, the results may suggest some ways to address challenges related to the assessment of clinical reasoning and ensure that the assessment tasks accurately reflect learners' developing competencies and educational progress.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50797.

PMID:38090795 | DOI:10.2196/50797

Front Artif Intell. 2023 Nov 28;6:1319844. doi: 10.3389/frai.2023.1319844. eCollection 2023.

NO ABSTRACT

PMID:38089711 | PMC:PMC10715242 | DOI:10.3389/frai.2023.1319844

Behav Res Methods. 2023 Dec 12. doi: 10.3758/s13428-023-02296-x. Online ahead of print.

ABSTRACT

Analyzing data from the verbal fluency task (e.g., "name all the animals you can in a minute") is of interest to both memory researchers and clinicians due to its broader implications for memory search and retrieval. Recent work has proposed several computational models to examine nuanced differences in search behavior, which can provide insights into the mechanisms underlying memory search. A prominent account of memory search within the fluency task was proposed by Hills et al. (2012), where mental search is modeled after how animals forage for food in physical space. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open-source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming. To remove this barrier to studying search patterns in the fluency task, we created forager, a Python package ( https://github.com/thelexiconlab/forager ) and web interface ( https://forager.research.bowdoin.edu/ ). forager provides multiple automated methods to designate clusters and switches within a fluency list, implements a novel set of computational models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search using semantic embeddings, and also enables researchers to evaluate relative model performance at the individual and group level. The package and web interface cater to users with various levels of programming experience. In this work, we introduce forager's basic functionality and use cases that demonstrate its utility with pre-existing behavioral and clinical data sets of the semantic fluency task.

PMID:38087144 | DOI:10.3758/s13428-023-02296-x

mSystems. 2024 Jan 23;9(1):e0100423. doi: 10.1128/msystems.01004-23. Epub 2023 Dec 11.

ABSTRACT

Oomycetes are fungus-like eukaryotic microorganisms which can cause catastrophic diseases in many plants. Successful infection of oomycetes depends highly on their effector proteins that are secreted into plant cells to subvert plant immunity. Thus, systematic identification of effectors from the oomycete proteomes remains an initial but crucial step in understanding plant-pathogen relationships. However, the number of experimentally identified oomycete effectors is still limited. Currently, only a few bioinformatics predictors exist to detect potential effectors, and their prediction performance needs to be improved. Here, we used the sequence embeddings from a pre-trained large protein language model (ProtTrans) as input and developed a support vector machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance with an area under the precision-recall curve of 0.804 (area under the receiver operating characteristic curve = 0.893, accuracy = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the fivefold cross-validation, considerably outperforming various combinations of popular machine learning algorithms and other commonly used sequence encoding schemes. A similar prediction performance was also observed in the independent test. Compared with the existing oomycete effector prediction methods, POOE provided very competitive and promising performance, suggesting that ProtTrans effectively captures rich protein semantic information and dramatically improves the prediction task. We anticipate that POOE can accelerate the identification of oomycete effectors and provide new hints to systematically understand the functional roles of effectors in plant-pathogen interactions. The web server of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding source codes and data sets are also available at https://github.com/zzdlabzm/POOE.IMPORTANCEIn this work, we use the sequence representations from a pre-trained large protein language model (ProtTrans) as input and develop a Support Vector Machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance in the independent test set, considerably outperforming existing oomycete effector prediction methods. We expect that this new bioinformatics tool will accelerate the identification of oomycete effectors and further guide the experimental efforts to interrogate the functional roles of effectors in plant-pathogen interaction.

PMID:38078741 | PMC:PMC10804963 | DOI:10.1128/msystems.01004-23

J Med Econ. 2024 Jan-Dec;27(1):84-96. doi: 10.1080/13696998.2023.2291266. Epub 2023 Dec 19.

ABSTRACT

OBJECTIVE: To assess the costs of treated recurrence and survival in elderly patients with early breast cancer (EBC) at high risk of recurrence using Surveillance Epidemiology and End Results (SEER) registry-Medicare linked claims data.

METHODS: This retrospective study included patients aged ≥65 years with hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-), node-positive EBC at high risk of recurrence. Treated recurrences were defined based on treatment events/procedure codes from claims. Primary outcomes were monthly total extra costs and cumulative extra costs of treated recurrence relative to patients with non/untreated recurrence. Costs were calculated using a Kaplan-Meier sampling average estimator method and inflated to 2021 US$. Secondary outcomes included analysis by recurrence type and overall survival (OS) after recurrence. Subgroup analysis evaluated costs in patients with Medicare Part D coverage.

RESULTS: Among 3,081 eligible patients [mean (SD) age at diagnosis was 74.5 (7.1) years], the majority were females (97.4%) and white (87.8%). Treated recurrence was observed in 964 patients (31.3%). The monthly extra cost of treated recurrence was highest at the beginning of the first treated recurrence episode, with 6-year cumulative cost of $117,926. Six-year cumulative extra costs were higher for patients with distant recurrences ($168,656) than for patients with locoregional recurrences ($96,465). Median OS was 4.34 years for all treated recurrences, 1.92 years for distant recurrence, and 6.78 years for locoregional recurrence. Similar cumulative extra cost trends were observed in the subgroup with Part D coverage as in the overall population.

LIMITATIONS: This study utilizes claims data to identify treated recurrence. Due to age constraints of the dataset, results may not extrapolate to a younger population where EBC is commonly diagnosed.

CONCLUSION: EBC recurrence in this elderly population has substantial costs, particularly in patients with distant recurrences. Therapies that delay or prevent recurrence may reduce long-term costs significantly.

PMID:38059275 | DOI:10.1080/13696998.2023.2291266

BMC Public Health. 2023 Dec 5;23(1):2425. doi: 10.1186/s12889-023-17351-7.

ABSTRACT

BACKGROUND: Ethiopia aims to achieve universal healthcare using health insurance. To do so, it has been implementing community-based health insurance since 2011. However, the retention of members by the scheme has not yet been evaluated nationally. The systematic review and meta-analysis aimed to evaluate the dropout rate and associated factors among the scheme's beneficiaries in Ethiopia.

METHODS: On December 19, 2022, searches were conducted in Scopus, Hinari, PubMed, Semantic Scholar, and Google Scholar. Searches were also conducted on the general web and electronic repositories, including the Ethiopian Health Insurance Service, the International Institute for Primary Health Care-Ethiopia, and various higher education institutions. The Joanna Briggs Institute's tools and the "preferred reporting items for systematic reviews and meta-analyses 2020 statement" were used to evaluate bias and frame the review, respectively. Data were analyzed using Stata 17 and RevMan 5. To assess heterogeneity, we conducted subgroup analysis and used a random model to calculate odds ratios with a p value less than 0.05 and a 95% CI.

RESULTS: In total, 14 articles were included in the qualitative synthesis, of which 12 were selected for the quantitative analysis. The pooled estimate revealed that the dropout rate of beneficiaries from the scheme was 34.0% (95% CI: 23-44%), provided that the renewal rate was 66.0%, and was found to be influenced by socio-demographic, health status, length of enrolment, knowledge, attitude, the scheme, and health service-related variables. The southern and Oromia regions reported the lowest and highest dropout rates, with 27.0% (95% CI: 24-29%) and 48.0% (95% CI: 18-78%), respectively. The dropout rates increased from 12.3% in 2012-2015 to 34.4% in 2020-2021.

CONCLUSION: More than one-third of the scheme's beneficiaries were found to have dropped out, and this has been found to increase over time, dictating that a community-based strategy and intervention, from the supply, insurer, and demand sides, seem indispensable in minimizing this huge dropout rate.

PMID:38053053 | DOI:10.1186/s12889-023-17351-7

J Psychiatr Res. 2023 Nov 30;169:238-246. doi: 10.1016/j.jpsychires.2023.11.013. Online ahead of print.

ABSTRACT

BACKGROUND: Disorganization symptoms are a main feature of schizophrenia, which include illogical and incoherent thinking, circumstantiality, tangentiality and loose associations. As these symptoms entail language deficits, several functional neuroimaging studies have been performed in schizophrenia using verbal tasks, producing somewhat heterogenous results. Hence, we performed a meta-analysis seeking to identify the most reliable neural alterations observed in schizophrenia patients during such tasks.

METHODS: Web of Sciences, PubMed, and EMBASE were searched for functional neuroimaging studies during verbal tasks (e.g. verbal fluency and semantic processing) in schizophrenia. Out of 795 screened articles, 33 were eligible for this meta-analysis. A coordinated-based meta-analysis was performed with the activation likelihood estimation (ALE) approach, using the cluster-level family-wise error (FWE) correction set at p < 0.05.

RESULTS: In schizophrenia, hyperactivations were observed in the left inferior frontal gyrus (IFG) and middle frontal gyrus (MFG) and hypoactivations were observed in the right IFG, the precentral gyrus and the left caudate nucleus. Another analysis pooling hyper- and hypoactivations revealed altered activations, firstly, in the left IFG and MFG, secondly, in the left precentral gyrus, IFG and insula, and, thirdly, in the left angular gyrus and precuneus. In the light of these results, not only classic language-related regions are abnormally activated during verbal tasks in schizophrenia, but also brain regions involved in executive functions, autobiographical memory and, unexpectedly, in motor functions. Further functional neuroimaging studies are needed to investigate the role of the striatum in linguistic sequencing in schizophrenia.

PMID:38048673 | DOI:10.1016/j.jpsychires.2023.11.013

Artif Intell Med. 2023 Dec;146:102701. doi: 10.1016/j.artmed.2023.102701. Epub 2023 Nov 1.

ABSTRACT

OBJECTIVE: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.

METHODS: We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.

RESULTS: Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.

CONCLUSIONS: This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.

PMID:38042599 | DOI:10.1016/j.artmed.2023.102701

Front Psychol. 2023 Nov 16;14:1279169. doi: 10.3389/fpsyg.2023.1279169. eCollection 2023.

ABSTRACT

PURPOSE: Developmental stuttering is a fluency disorder that may be caused by neurological, genetic, or familial factors. However, a general perception that stuttering is caused by psychological problems could lead to negative attitudes toward stuttering, causing prejudice or discrimination against people who stutter (PWS). Thus, our study aimed to investigate whether certain beliefs in etiology of stuttering are related to the negative perception of stuttering.

METHODS: A web-based survey of 413 native Japanese adults, aged 20-69, who did not suffer from stuttering, schizophrenia, or depression, was conducted in August 2021. The participants were recruited through the Web monitor panel. Participants were divided into three uniform groups based on their response to a 27-item questionnaire about their implicit belief regarding the etiology of stuttering: belief in the biological model (stuttering-biological group), belief in the psychological model (stuttering-psychological group), and the control group (those who responded to perception of healthy adult males). Participants were also asked to respond to 25 items of semantic differential scales about perception of stuttering or healthy adult males. Responses were summarized into several factors by factor analysis, and factor scores were compared among the three groups. The stuttering-biological group had the fewest participants, comprising 80 individuals. Overall, a total of 240 participants, 80 from each group, were included in the analysis.

RESULTS: Some pairs of stereotypes included in semantic differential scales revealed differences between the groups; PWS, irrespective of the participants of the biological or psychological group, were considered as having negative stereotyping properties such as being "tense," "anxious," or "afraid." Additionally, three concepts from the factor analysis of these 25 items were analyzed using an analysis of variance, and significant differences were found; the mean factor score of the "danger" stereotype was lower in the stuttering-biological group compared to the stuttering-psychological group.

CONCLUSION: Although the simplification of the biological model is not recommended, anti-stigma campaigns to educate people that stuttering is caused by multidimensional factors, not just psychological ones, could change the general public's negative perceptions of stuttering.

PMID:38034304 | PMC:PMC10687552 | DOI:10.3389/fpsyg.2023.1279169

Front Psychol. 2023 Nov 15;14:1192020. doi: 10.3389/fpsyg.2023.1192020. eCollection 2023.

ABSTRACT

INTRODUCTION: Trust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection.

METHODS: A review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (https://areen.shinyapps.io/Trust_explorer/).

RESULTS: At the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires' items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process.

DISCUSSION: The results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

PMID:38034296 | PMC:PMC10684734 | DOI:10.3389/fpsyg.2023.1192020

IEEE Trans Neural Netw Learn Syst. 2023 Nov 30;PP. doi: 10.1109/TNNLS.2023.3333542. Online ahead of print.

ABSTRACT

Multilabel image recognition (MLR) aims to annotate an image with comprehensive labels and suffers from object occlusion or small object sizes within images. Although the existing works attempt to capture and exploit label correlations to tackle these issues, they predominantly rely on global statistical label correlations as prior knowledge for guiding label prediction, neglecting the unique label correlations present within each image. To overcome this limitation, we propose a semantic and correlation disentangled graph convolution (SCD-GC) method, which builds the image-specific graph and employs graph propagation to reason the labels effectively. Specifically, we introduce a semantic disentangling module to extract categorywise semantic features as graph nodes and develop a correlation disentangling module to extract image-specific label correlations as graph edges. Performing graph convolutions on this image-specific graph allows for better mining of difficult labels with weak visual representations. Visualization experiments reveal that our approach successfully disentangles the dominant label correlations existing within the input image. Through extensive experimentation, we demonstrate that our method achieves superior results on the challenging Microsoft COCO (MS-COCO), PASCAL visual object classes (PASCAL-VOC), NUS web image dataset (NUS-WIDE), and Visual Genome 500 (VG-500) datasets. Code is available at GitHub: https://github.com/caigitrepo/SCDGC.

PMID:38032778 | DOI:10.1109/TNNLS.2023.3333542

Behav Res Methods. 2023 Nov 29. doi: 10.3758/s13428-023-02267-2. Online ahead of print.

ABSTRACT

When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in "big data." We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis.

PMID:38030924 | DOI:10.3758/s13428-023-02267-2

Front Hum Neurosci. 2023 Nov 8;17:1219737. doi: 10.3389/fnhum.2023.1219737. eCollection 2023.

ABSTRACT

The semantic variant of primary progressive aphasia (svPPA), known also as "semantic dementia (SD)," is a neurodegenerative disorder that pertains to the frontotemporal lobar degeneration clinical syndromes. There is currently no approved pharmacological therapy for all frontotemporal dementia variants. Transcranial direct current stimulation (tDCS) is a promising non-invasive brain stimulation technique capable of modulating cortical excitability through a sub-threshold shift in neuronal resting potential. This technique has previously been applied as adjunct treatment in Alzheimer's disease, while data for frontotemporal dementia are controversial. In this scoped review, we summarize and critically appraise the currently available evidence regarding the use of tDCS for improving performance in naming and/or matching tasks in patients with svPPA. Clinical trials addressing this topic were identified through MEDLINE (accessed by PubMed) and Web of Science, as of November 2022, week 3. Clinical trials have been unable to show a significant benefit of tDCS in enhancing semantic performance in svPPA patients. The heterogeneity of the studies available in the literature might be a possible explanation. Nevertheless, the results of these studies are promising and may offer valuable insights into methodological differences and overlaps, raising interest among researchers in identifying new non-pharmacological strategies for treating svPPA patients. Further studies are therefore warranted to investigate the potential therapeutic role of tDCS in svPPA.

PMID:38021245 | PMC:PMC10663282 | DOI:10.3389/fnhum.2023.1219737