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Journal of Safety Research Jun 2024Highly automated driving is expected to reduce the accident risk occurrence by human errors, but it can also increase driver distraction. Previous evidence shows that...
INTRODUCTION
Highly automated driving is expected to reduce the accident risk occurrence by human errors, but it can also increase driver distraction. Previous evidence shows that auditory signals can help drivers take over in critical situations. However, it is still uncertain whether the potential benefit of verbal auditory signals could be generalized to driving situations where drivers are visually and auditorily distracted.
METHOD
Our first objective was to compare the effectiveness of complementary audio messages (audio + visual condition) and visual only (visual condition) variable message signs (VMS) messages. The second objective was to explore the potential use of oral messages with traffic information to help highly-automated vehicle drivers identify critical situations. Eye-tracking data were also registered. Twenty-four volunteers participated in a driving simulator study, completing two tasks: (a) a TV series task, where they had to pay attention to an episode of a TV series while traveling along the route; and (b) a VMS task, where they had to recover the manual control of the car if the VMS message was a 'critical message.'
RESULTS
General results showed that, when the audio was available, the participants: (a) had a higher ability to discriminate the VMS messages, (b) were less conservative, (c) responded earlier, and (d) their pattern of fixations was more efficient. A complementary analysis showed that the counterbalance order was a moderating factor for the discrimination ability and the response distance measures. This evidence suggests a potential learning effect, not cancelled by counterbalancing the order of the conditions.
CONCLUSION
The processing of traffic messages may improve when provided as oral and visual messages.
PRACTICAL APPLICATIONS
These results would be of special interest for engineers designing highly automated cars, considering that the design of automated systems must ensure that the driver's attention is sufficient to take over control.
Topics: Humans; Male; Adult; Distracted Driving; Female; Attention; Young Adult; Automobile Driving; Computer Simulation; Eye-Tracking Technology; Automation; Accidents, Traffic
PubMed: 38858040
DOI: 10.1016/j.jsr.2024.01.014 -
Food Chemistry: X Jun 2024The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring...
The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.
PubMed: 38855098
DOI: 10.1016/j.fochx.2024.101507 -
Cureus May 2024The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the... (Review)
Review
The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.
PubMed: 38854295
DOI: 10.7759/cureus.59906 -
MedRxiv : the Preprint Server For... May 2024Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could...
OBJECTIVE
Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care.
METHODS
We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization.
RESULTS
The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%).
CONCLUSIONS
These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.
PubMed: 38854098
DOI: 10.1101/2024.05.27.24307973 -
MedRxiv : the Preprint Server For... May 2024Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of...
IMPORTANCE
Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment.
OBJECTIVE
To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs.
DESIGN
Multicohort study.
SETTING
Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).
PARTICIPANTS
Individuals without HF at baseline.
EXPOSURES
AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD).
MAIN OUTCOMES AND MEASURES
Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).
RESULTS
There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF.
CONCLUSIONS AND RELEVANCE
Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.
PubMed: 38854022
DOI: 10.1101/2024.05.27.24307952 -
European Journal of Case Reports in... 2024Inappropriate therapy is a frequent adverse consequence of implantable cardioverter-defibrillator. Inappropriate therapy often occurs due to the misinterpretation of...
UNLABELLED
Inappropriate therapy is a frequent adverse consequence of implantable cardioverter-defibrillator. Inappropriate therapy often occurs due to the misinterpretation of sinus tachycardia or atrial fibrillation/flutter with rapid atrioventricular conduction by the device. Current implantable cardioverter-defibrillator (ICD) mechanisms integrate various discriminators into algorithms to differentiate supraventricular tachycardia (SVT) from ventricular tachycardia (VT), to prevent such occurrences. A 40-year-old man suffered seizures and cardiac arrest abruptly, without prior complaints of chest pain. Without delay, he initiated cardiopulmonary resuscitation (CPR), resulting in the regaining of spontaneous circulation. The patient had previously received a single-chamber ICD due to recurring VT and a prior episode of cardiac arrest. The patient had a medical background of coronary artery disease with complete revascularisation and no previous occurrence of SVT. Interrogating the ICD revealed captured non-sustained ventricular tachycardia (NSVT) and SVT events but no VT episode or shock therapy. During the specified time period, the patient underwent an electrophysiological study, and no SVT was induced with the normal function of the atrioventricular and sinoatrial nodes. Various causes can lead to errors in morphology discrimination criteria in single-chamber ICDs. Extending the detection interval is highly recommended to avoid misclassification of ICDs.
LEARNING POINTS
This highlights the crucial significance of precise classification of supraventricular tachycardia (SVT) and ventricular tachycardia (VT) using a single-chamber implantable cardioverter-defibrillator (ICD) discriminator to guarantee prompt and appropriate therapy delivery.The morphology criterion used in single-chamber ICDs may have potential limits and inaccuracies, which might result in the misdiagnosis of VT as SVT.Further study and enhancement of differentiation algorithms, paired with precise programming and prolonged detection durations are essential to reduce such misclassifications and improve patient outcomes.
PubMed: 38846652
DOI: 10.12890/2024_004526 -
BMC Medical Education Jun 2024Despite the numerous advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience...
Assessing attitudes towards biostatistics education among medical students: adaptation and preliminary evaluation of the Chinese version survey of attitudes towards statistics (SATS-36).
BACKGROUND
Despite the numerous advantages of mastering biostatistics, medical students generally perceive biostatistics as a difficult and challenging subject and even experience anxiety during the courses. Evidence for the correlation between students' academic achievements and their attitudes, indicating that attitudes at the beginning of the biostatistics course may affect cognitive competence at the end of the course and subsequently influence student academic performance. However, there are current disagreements regarding the measurement and evaluation of attitudes related to statistics. Thus, there is a need for standard instruments to assess them. This study was conducted to develop a Chinese version of the Survey of Attitudes Toward Statistics (SATS-36) in order to acquire a valid instrument to measure medical students' attitudes toward biostatistics under Chinese medical educational background.
METHODS
The Chinese version SATS-36 was developed through translation and back-translation of the original scale, with subsequent revisions based on expert advice to ensure the most appropriate item content. The local adaption was performed with a cohort of 1709 Chinese-speaking medical undergraduate and graduate students enrolled in biostatistics courses. And then, the reliability, validity and discrimination of the questionnaires were evaluated through correlation coefficient calculation, factor analysis, parallel analysis and other methods.
RESULTS
The Chinese version SATS-36 consisted of 36 items and loaded a five-factor structure by factor analysis, which offered an alternative similar but not equal to that original six-factor structure. The cumulative variance contribution rate was 62.20%, the Cronbach's α coefficient was 0.908, the Guttman split-half reliability coefficient was 0.905 and the test-retest reliability coefficient was 0.752. Discriminant analysis revealed small to large significant differences in the five attitude subscales.
CONCLUSIONS
The Chinese version SATS-36 with good validity and reliability in this study can be used to evaluate the learning framework of Chinese medical students.
Topics: Humans; Students, Medical; Surveys and Questionnaires; Biostatistics; Female; China; Male; Reproducibility of Results; Education, Medical, Undergraduate; Young Adult; Attitude of Health Personnel; Adult; Psychometrics
PubMed: 38844916
DOI: 10.1186/s12909-024-05548-2 -
ENeuro Jun 2024The retrosplenial cortex (RSC) is a hub of diverse afferent and efferent projections thought to be involved in associative learning. RSC shows early pathology in mild...
The Granular Retrosplenial Cortex Is Necessary in Male Rats for Object-Location Associative Learning and Memory, But Not Spatial Working Memory or Visual Discrimination and Reversal, in the Touchscreen Operant Chamber.
The retrosplenial cortex (RSC) is a hub of diverse afferent and efferent projections thought to be involved in associative learning. RSC shows early pathology in mild cognitive impairment and Alzheimer's disease (AD), which impairs associative learning. To understand and develop therapies for diseases such as AD, animal models are essential. Given the importance of human RSC in object-location associative learning and the success of object-location associative paradigms in human studies and in the clinic, it would be of considerable value to establish a translational model of object-location learning for the rodent. For this reason, we sought to test the role of RSC in object-location learning in male rats using the object-location paired-associates learning (PAL) touchscreen task. First, increased cFos immunoreactivity was observed in granular RSC following PAL training when compared with extended pretraining controls. Following this, RSC lesions following PAL acquisition were used to explore the necessity of the RSC in object-location associative learning and memory and two tasks involving only one modality: trial-unique nonmatching-to-location for spatial working memory and pairwise visual discrimination/reversal. RSC lesions impaired both memory for learned paired-associates and learning of new object-location associations but did not affect performance in either the spatial or visual single-modality tasks. These findings provide evidence that RSC is necessary for object-location learning and less so for learning and memory involving the individual modalities therein.
Topics: Animals; Male; Memory, Short-Term; Spatial Memory; Association Learning; Rats, Long-Evans; Visual Perception; Rats; Gyrus Cinguli; Reversal Learning; Conditioning, Operant; Discrimination, Psychological; Cerebral Cortex
PubMed: 38844347
DOI: 10.1523/ENEURO.0120-24.2024 -
PloS One 2024Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities...
Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients' scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.
Topics: Humans; Frontotemporal Dementia; Alzheimer Disease; Female; Male; Aged; Speech; Middle Aged; Case-Control Studies; Biomarkers; Natural Language Processing; Machine Learning; Neuropsychological Tests; Executive Function
PubMed: 38843210
DOI: 10.1371/journal.pone.0304272 -
Frontiers in Plant Science 2024Invasive plants represent a significant global challenge as they compete with native plants for limited resources such as space, nutrients and pollinators. Here, we...
Invasive plants represent a significant global challenge as they compete with native plants for limited resources such as space, nutrients and pollinators. Here, we focused on four invasive species that are widely spread in the French Pyrenees, , , and , and analyzed their visual advertisement signals with respect to those displayed by their surrounding native species using a perceptual approach based on the neural mechanisms of bee vision given that bees are regular pollinators of these plants. We collected 543 spectral reflections from the 4 invasive species, and 66 native species and estimated achromatic and chromatic similarities to the bee eye. and were inconspicuous against the foliage background and could be hardly discriminated in terms of color from their surrounding native plants. These characteristics promote generalization, potentially attracting pollinators foraging on similar native species. Two morphs of were both highly salient in chromatic and achromatic terms and different from their surrounding native species. This distinctive identity facilitates detection and learning in association with rich nectar. While visual signals are not the only sensory cue accounting for invasive-plant success, our study reveals new elements for understanding biological invasion processes from the perspective of pollinator perceptual processes.
PubMed: 38841283
DOI: 10.3389/fpls.2024.1393204