In Psychophysiology

Wetherell Mark A, Lau Shing-Hon, Maxion Roy A

2023-Mar-20

Individual differences, cardiovascular, heart rate variability, motor control

In Journal of evidence-based medicine

Ren Xue, Guo Yan, Wang Heyuan, Gao Xiang, Chen Wei, Wang Tengjiao

2023-Mar-20

Traditional Chinese Medicine, artificial intelligence, pediatric asthma

In Work (Reading, Mass.)

BACKGROUND : The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety.

OBJECTIVE : This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention.

METHODS : This study examined 73 scientific articles through bibliometric research and descriptive analysis.

RESULTS : The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine - SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data).

CONCLUSION : The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management.

Cavalcanti Marília, Lessa Luciano, Vasconcelos Bianca M

2023-Mar-13

Construction industry, algorithms, machine , safety management

In Cancer medicine

BACKGROUND : Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance.

METHODS : A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques.

RESULTS : By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis.

CONCLUSIONS : The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.

Silvestri Marco, Nghia Vu Trung, Nichetti Federico, Niger Monica, Di Cosimo Serena, De Braud Filippo, Pruneri Giancarlo, Pawitan Yudi, Calza Stefano, Cappelletti Vera

2023-Mar-20

bioinformatics, cholangiocarcinoma, next generation sequencing, transcriptomics, tumor-infiltrating immune cells

In Journal of Alzheimer's disease : JAD

BACKGROUND : Driving behavior as a digital behavior marker and recent developments in blood-based biomarkers show promise as a widespread solution for the early identification of Alzheimer's disease (AD).

OBJECTIVE : This study used artificial intelligence methods to evaluate the association between naturalistic driving behavior and blood-based biomarkers of AD.

METHODS : We employed an artificial neural network (ANN) to examine the relationship between everyday driving behavior and plasma biomarker of AD. The primary outcome was plasma Aβ 42/Aβ 40, and Aβ 42/Aβ 40 <  0.1013 was used to define amyloid positivity. Two ANN models were trained and tested for predicting the outcome. The first model architecture only includes driving variables as input, whereas the second architecture includes the combination of age, APOE ɛ4 status, and driving variables.

RESULTS : All 142 participants (mean [SD] age 73.9 [5.2] years; 76 [53.5%] men; 80 participants [56.3% ] with amyloid positivity based on plasma Aβ 42/Aβ 40) were cognitively normal. The six driving features, included in the ANN models, were the number of trips during rush hour, the median and standard deviation of jerk, the number of hard braking incidents and night trips, and the standard deviation of speed. The F1 score of the model with driving variables alone was 0.75 [0.023] for predicting plasma Aβ 42/Aβ 40. Incorporating age and APOE ɛ4 carrier status improved the diagnostic performance of the model to 0.80 [>0.051].

CONCLUSION : Blood-based AD biomarkers offer a novel opportunity to establish the efficacy of naturalistic driving as an accessible digital marker for AD pathology in driving research.

Bayat Sayeh, Roe Catherine M, Schindler Suzanne, Murphy Samantha A, Doherty Jason M, Johnson Ann M, Walker Alexis, Ances Beau M, Morris John C, Babulal Ganesh M

2023-Mar-13

Alzheimer’s disease, amyloid, artificial intelligence, naturalistic, plasma biomarkers

In Journal of Alzheimer's disease : JAD

BACKGROUND : Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge.

OBJECTIVE : To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD.

METHODS : Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model.

RESULTS : Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11).

CONCLUSION : A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Dolcet-Negre Marta M, Imaz Aguayo Laura, de Eulate Reyes García, Martí-Andrés Gloria, Matarrubia Marta Fernández, Domínguez Pablo, Fernández Seara Mará A, Riverol Mario

2023-Mar-13

Alzheimer’s disease, classification, machine learning, mild cognitive impairment, subjective cognitive decline