Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

Surgery Surgery

Derivation and Validation of a Machine Learning Algorithm for Predicting Venous Thromboembolism in Injured Children.

In Journal of pediatric surgery ; h5-index 38.0

BACKGROUND : Venous thromboembolism (VTE) causes significant morbidity in pediatric trauma patients. We applied machine learning algorithms to the Trauma Quality Improvement Program (TQIP) database to develop and validate a risk prediction model for VTE in injured children.

METHODS : Patients ≤18 years were identified from TQIP (2017-2019, n = 383,814). Those administered VTE prophylaxis ≤24 h and missing the outcome (VTE) were removed (n = 347,576). Feature selection identified 15 predictors: intubation, need for supplemental oxygen, spinal injury, pelvic fractures, multiple long bone fractures, major surgery (neurosurgery, thoracic, orthopedic, vascular), age, transfusion requirement, intracranial pressure monitor or external ventricular drain placement, and low Glasgow Coma Scale score. Data was split into training (n = 251,409) and testing (n = 118,175) subsets. Machine learning algorithms were trained, tested, and compared.

RESULTS : Low-risk prediction: For the testing subset, all models outperformed the baseline rate of VTE (0.15%) with a predicted rate of 0.01-0.02% (p < 2.2e-16). 88.4-89.4% of patients were classified as low risk by the models.

HIGH-RISK PREDICTION : All models outperformed baseline with a predicted rate of VTE ranging from 1.13 to 1.32% (p < 2.2e-16). The performance of the 3 models was not significantly different.

CONCLUSION : We developed a predictive model that differentiates injured children for development of VTE with high discrimination and can guide prophylaxis use.

LEVEL OF EVIDENCE : Prognostic, Level II.

TYPE OF STUDY : Retrospective, Cross-sectional.

Papillon Stephanie C, Pennell Christopher P, Master Sahal A, Turner Evan M, Arthur L Grier, Grewal Harsh, Aronoff Stephen C

2023-Feb-18

Machine learning, Pediatric trauma, TQIP, Venous thromboembolism

General General

Artificial intelligence evaluation of COVID-19 restrictions and speech therapy effects on the autistic children's behavior.

In Scientific reports ; h5-index 158.0

In the present study, we aimed to quantify the effects of COVID-19 restrictions and speech treatment approaches during lockdowns on autistic children using CBCL and neuro-fuzzy artificial intelligence method. In this regard, a survey including CBCL questionnaire is prepared using online forms. In total, 87 children with diagnosed Autism spectrum disorders (ASD) participated in the survey. The influences of three treatment approaches of in-person, telehealth and public services along with no-treatment condition during lockdown were the main factors of the investigation. The main output factors were internalized and externalized problems in general and their eight subcategory syndromes. We examined the reports by parents/caregivers to find correlation between treatments and CBCL listed problems. Moreover, comparison of the eight syndromes rating scores from pre-lockdown to post-lockdown periods were performed. In addition, artificial intelligence method were engaged to find the influence of speech treatment during restrictions on the level of internalizing and externalizing problems. In this regard, a fully connected adaptive neuro fuzzy inference system is employed with type and duration of treatments as input and T-scores of the syndromes are the output of the network. The results indicate that restrictions alleviate externalizing problems while intensifying internalizing problems. In addition, it is concluded that in-person speech therapy is the most effective and satisfactory approach to deal with ASD children during stay-at-home periods.

Sabzevari Fereshteh, Amelirad Omid, Moradi Zohre, Habibi Mostafa

2023-Mar-15

General General

Research on Supply Chain Financial Risk Prevention Based on Machine Learning.

In Computational intelligence and neuroscience

Artificial intelligence (AI) proves decisive in today's rapidly developing society and is a motive force for the evolution of financial technology. As a subdivision of artificial intelligence research, machine learning (ML) algorithm is extensively used in all aspects of the daily operation and development of the supply chain. Using data mining, deductive reasoning, and other characteristics of machine learning algorithms can effectively help decision-makers of enterprises to make more scientific and reasonable decisions by using the existing financial index data. At present, globalization uncertainties such as COVID-19 are intensifying, and supply chain enterprises are facing bankruptcy risk. In the operation process, practical tools are needed to identify and opportunely respond to the threat in the supply chain operation promptly, predict the probability of business failure of enterprises, and take scientific and feasible measures to prevent a financial crisis in good season. Artificial intelligence decision-making technology can help traditional supply chains to transform into intelligent supply chains, realize smart management, and promote supply chain transformation and upgrading. By applying machine learning algorithms, the supply chain can not only identify potential risks in time and adopt scientific and feasible measures to deal with the crisis but also strengthen the connection and cooperation between different enterprises with the advantage of advanced technology to provide overall operation efficiency. On account of this, the paper puts forward an artificial intelligence-based corporate financial-risk-prevention (FRP) model, which includes four stages: data preprocessing, feature selection, feature classification, and parameter adjustment. Firstly, relevant financial index data are collected, and the quality of the selected data is raised through preprocessing; secondly, the chaotic grasshopper optimization algorithm (CGOA) is used to simulate the behavior of grasshoppers in nature to build a mathematical model, and the selected data sets are selected and optimized for features. Then, the support vector machine (SVM) performs classification processing on the quantitative data with reduced features. Empirical risk is calculated using the hinge loss function, and a regular operation is added to optimize the risk structure. Finally, slime mould algorithm (SMA) can optimize the process to improve the efficiency of SVM, making the algorithm more accurate and effective. In this study, Python is used to simulate the function of the corporate business finance risk prevention model. The experimental results show that the CGOA-SVM-SMA algorithm proposed in this paper achieves good results. After calculation, it is found that the prediction and decision-making capabilities are good and better than other comparative models, which can effectively help supply chain enterprises to prevent financial risks.

Lei Yang, Qiaoming Hou, Tong Zhao

2023

Radiology Radiology

Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics

ArXiv Preprint

To what extent can the patient's length of stay in a hospital be predicted using only an X-ray image? We answer this question by comparing the performance of machine learning survival models on a novel multi-modal dataset created from 1235 images with textual radiology reports annotated by humans. Although black-box models predict better on average than interpretable ones, like Cox proportional hazards, they are not inherently understandable. To overcome this trust issue, we introduce time-dependent model explanations into the human-AI decision making process. Explaining models built on both: human-annotated and algorithm-extracted radiomics features provides valuable insights for physicians working in a hospital. We believe the presented approach to be general and widely applicable to other time-to-event medical use cases. For reproducibility, we open-source code and the TLOS dataset at https://github.com/mi2datalab/xlungs-trustworthy-los-prediction.

Hubert Baniecki, Bartlomiej Sobieski, Przemysław Bombiński, Patryk Szatkowski, Przemysław Biecek

2023-03-17

General General

Predicting Workplace Violence in the Emergency Department Based on Electronic Health Record Data.

In Journal of emergency nursing

INTRODUCTION : Emergency departments are extremely vulnerable to workplace violence, and emergency nurses are frequently exposed to workplace violence. We developed workplace violence prediction models using machine learning methods based on data from electronic health records.

METHODS : This study was conducted using electronic health record data collected between January 1, 2016 and December 31, 2021. Workplace violence cases were identified based on violence-related mentions in nursing records. Workplace violence was predicted using various factors related to emergency department visit and stay.

RESULTS : The dataset included 1215 workplace violence cases and 6044 nonviolence cases. Random Forest showed the best performance among the algorithms adopted in this study. Workplace violence was predicted with higher accuracy when both ED visit and ED stay factors were used as predictors (0.90, 95% confidence interval 0.898-0.912) than when only ED visit factors were used. When both ED visit and ED stay factors were included for prediction, the strongest predictor of risk of WPV was patient dissatisfaction, followed by high average daily length of stay, high daily number of patients, and symptoms of psychiatric disorders.

DISCUSSION : This study showed that workplace violence could be predicted with previous data regarding ED visits and stays documented in electronic health records. Timely prediction and mitigation of workplace violence could improve the safety of emergency nurses and the quality of nursing care. To prevent workplace violence, emergency nurses must recognize and continuously observe the risk factors for workplace violence from admission to discharge.

Lee Hyungbok, Yun Heeje, Choi Minjin, Kim Hyeoneui

2023-Mar-14

Electronic health record, Emergency department, Machine learning, Predictive modeling, Workplace aggression

General General

Non-invasive evaluation of embryos using mid-infrared attenuated total reflection spectrometry of incubation medium: a preliminary study.

In Reproductive biomedicine online ; h5-index 47.0

RESEARCH QUESTION : Can mid-infrared attenuated total reflection (MIR ATR) spectroscopy combined with machine learning methods be used as an additional tool to predict embryo quality and IVF treatment outcomes?

DESIGN : Spent culture media was collected and analysed. MIR ATR absorbance spectra were measured using an ALPHA II spectrometer equipped with an attenuated total reflection (ATR) spectrometry accessory. Patient and treatment data and results were collected and analysed in combination with machine learning techniques to identify possible correlations. The main outcome measures were to define the characteristics of absorbance spectra of spent culture media and to distinguish the difference in absorbance between top- and low-quality embryos, day 3 and day 5 embryos and implanting embryos versus non-implanting embryos.

RESULTS : Spent culture media of 227 embryos was collected and analysed. Absorbance peaks in the culture media were different between day 3 and day 5 embryos. Moreover, significant differences in P-values, spanning from 0.014 to 0.044 in absorbance peaks for day 3 embryos and 0.024 up to 0.04 for day 5 embryos, were seen between implanting and non-implanting embryos. Machine learning techniques offered a pregnancy prediction value of 84.6% for day 3 embryos.

CONCLUSIONS : MIR ATR may offer an additional parameter for better selection of embryos based on the spectrometric absorbance and secretions of metabolites in the culture media.

Aslih Nardin, Dekel Ben Zion, Malonek Dov, Michaeli Medeia, Polotov Diana, Shalom-Paz Einat

2022-Dec-21

IVF, Mid-infrared attenuated total reflection spectrometry, Morphokinetics, Spectrometry