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

General General

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients.

In World journal of emergency medicine

BACKGROUND : To promote the shared decision-making (SDM) between patients and doctors in pediatric outpatient departments, this study was designed to validate artificial intelligence (AI) -initiated medical tests for children with fever.

METHODS : We designed an AI model, named Xiaoyi, to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic. We calculated the sensitivity, specificity, and F1 score to evaluate the efficacy of Xiaoyi's recommendations. The patients were divided into the rejection and acceptance groups. Then we analyzed the rejected examination items in order to obtain the corresponding reasons.

RESULTS : We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics. The recommended examinations given by Xiaoyi for 10,636 (89.6%) patients were qualified. The average F1 score reached 0.94. A total of 58.4% of the patients accepted Xiaoyi's suggestions (acceptance group), and 41.6% refused (rejection group). Imaging examinations were rejected by most patients (46.7%). The tests being time-consuming were rejected by 2,133 patients (43.2%), including rejecting pathogen studies in 1,347 patients (68.5%) and image studies in 732 patients (31.8%). The difficulty of sampling was the main reason for rejecting routine tests (41.9%).

CONCLUSION : Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients, and is worth promoting in facilitating SDM.

Li Wei-Hua, Dong Bin, Wang Han-Song, Yuan Jia-Jun, Qian Han, Zheng Ling-Ling, Lin Xu-Lin, Wang Zhao, Liu Shi-Jian, Ning Bo-Tao, DanTian Zhao

2023

Artificial intelligence, Medical examinations, Pediatric outpatient, Shared decision- making

General General

Investigating the human spirit and spirituality in pediatric patients with kidney disease.

In Frontiers in pediatrics

Human spirit is an integral part of the medicinal art and science trifecta: body-mind-spirit, and it is contained in the World Health Organization definition of health. Human spirit is defined as our purpose in life, relationships with all living creatures or "Higher Power", and in general our place on planet Earth. Spirituality is a required part of patient care according to Joint Commission on Accreditation of Health Care Organizations. There is an abundant medical literature that documents discrepancies in the results between studies and populations, and points to the importance of cultural, ethnic, spiritual or religious differences. Validated questionnaires used in research for last several decades demonstrated an association of spirituality with clinical outcomes, coping, and quality of life in different adult chronic diseases. There are also validated scales to measure hope in children based on the premise that children are goal directed and that their goal-related thoughts can be understood, yet their purposefulness, meaning of life and spirit in pediatric nephrology remains mostly unexamined. Although pediatric nephrology has made significant advances in molecular techniques, artificial intelligence, machine learning, and started to address more broad social issues such as racism, health equity, diversity of our work force, etc, it lacks both systematic ways of studying and philosophical approach to fostering human spirit. This mini review examines the place and knowledge gaps in human spirit and spirituality in pediatric nephrology. We review the concept of the human spirit and medical literature pertaining to its role in pediatric nephrology.

Woroniecki Robert, Moritz Michael L

2023

hope, quality of life, religious beliefs, resilience, social determinants of health, spirit, spirituality

General General

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials.

In ACS omega

Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algorithms provide a new avenue to solve the synthesizability challenge. In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in Fourier-transformed crystal properties (FTCP) representation and use a deep learning model to predict synthesizability in the form of a synthesizability score (SC). Such an SC model, as a synthesizability filter for new materials, enables an efficient and accurate classification to identify promising material candidates. The SC prediction model achieved 82.6/80.6% (precision/recall) overall accuracy in predicting ternary crystal materials. We also trained the SC model by only considering compounds uploaded on the MP before 2015 as the training set and testing on multiple sets of materials uploaded after 2015. In the post-2019 test set, we obtain a high 88.60% true positive rate accuracy, coupled with 9.81% precision, indicating that newly added materials remain unexplored and have high synthesis potential. Further, we provide a list of 100 materials predicted to be synthesizable from this post-2019 dataset (highest SC) for future studies, and our SC model, as a validation filter, is beneficial for future material screening and discovery.

Zhu Ruiming, Tian Siyu Isaac Parker, Ren Zekun, Li Jiali, Buonassisi Tonio, Hippalgaonkar Kedar

2023-Mar-07

General General

Interpretable Skin Cancer Classification based on Incremental Domain Knowledge Learning.

In Journal of healthcare informatics research

The recent advances in artificial intelligence have led to the rapid development of computer-aided skin cancer diagnosis applications that perform on par with dermatologists. However, the black-box nature of such applications makes it difficult for physicians to trust the predicted decisions, subsequently preventing the proliferation of such applications in the clinical workflow. In this work, we aim to address this challenge by developing an interpretable skin cancer diagnosis approach using clinical images. Accordingly, a skin cancer diagnosis model consolidated with two interpretability methods is developed. The first interpretability method integrates skin cancer diagnosis domain knowledge, characterized by a skin lesion taxonomy, into model development, whereas the other method focuses on visualizing the decision-making process by highlighting the dominant of interest regions of skin lesion images. The proposed model is trained and validated on clinical images since the latter are easily obtainable by non-specialist healthcare providers. The results demonstrate the effectiveness of incorporating lesion taxonomy in improving model classification accuracy, where our model can predict the skin lesion origin as melanocytic or non-melanocytic with an accuracy of 87%, predict lesion malignancy with 77% accuracy, and provide disease diagnosis with an accuracy of 71%. In addition, the implemented interpretability methods assist understand the model's decision-making process and detecting misdiagnoses. This work is a step toward achieving interpretability in skin cancer diagnosis using clinical images. The developed approach can assist general practitioners to make an early diagnosis, thus reducing the redundant referrals that expert dermatologists receive for further investigations.

Rezk Eman, Eltorki Mohamed, El-Dakhakhni Wael

2023-Mar

Artificial intelligence, Clinical images, Domain knowledge, Interpretability, Skin cancer, Skin lesion taxonomy

General General

Prediction of Prednisolone Dose Correction Using Machine Learning.

In Journal of healthcare informatics research

UNLABELLED : Wrong dose, a common prescription error, can cause serious patient harm, especially in the case of high-risk drugs like oral corticosteroids. This study aims to build a machine learning model to predict dose-related prescription modifications for oral prednisolone tablets (i.e., highly imbalanced data with very few positive cases). Prescription data were obtained from the electronic medical records at a single institute. Cluster analysis classified the clinical departments into six clusters with similar patterns of prednisolone prescription. Two patterns of training datasets were created with/without preprocessing by the SMOTE method. Five ML models (SVM, KNN, GB, RF, and BRF) and logistic regression (LR) models were constructed by Python. The model was internally validated by five-fold stratified cross-validation and was validated with a 30% holdout test dataset. Eighty-two thousand five hundred fifty-three prescribing data for prednisolone tablets containing 135 dose-corrected positive cases were obtained. In the original dataset (without SMOTE), only the BRF model showed a good performance (in test dataset, ROC-AUC:0.917, recall: 0.951). In the training dataset preprocessed by SMOTE, performance was improved on all models. The highest performance models with SMOTE were SVM (in test dataset, ROC-AUC: 0.820, recall: 0.659) and BRF (ROC-AUC: 0.814, recall: 0.634). Although the prescribing data for dose-related collection are highly imbalanced, various techniques such as the following have allowed us to build high-performance prediction models: data preprocessing by SMOTE, stratified cross-validation, and BRF classifier corresponding to imbalanced data. ML is useful in complicated dose audits such as oral prednisolone.

SUPPLEMENTARY INFORMATION : The online version contains supplementary material available at 10.1007/s41666-023-00128-3.

Sato Hiroyasu, Kimura Yoshinobu, Ohba Masahiro, Ara Yoshiaki, Wakabayashi Susumu, Watanabe Hiroaki

2023-Mar

Drug safety, Imbalanced data, Machine learning, Prednisolone, Prescription error

Public Health Public Health

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning.

In Frontiers in public health

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.

Taheri-Shirazi Maryam, Namdar Khashayar, Ling Kelvin, Karmali Karima, McCradden Melissa D, Lee Wayne, Khalvati Farzad

2023

appointment scheduling, logistic regression, no-show, random forest, waiting room time