• Cirrhosis
• Cox model
• Deep neural network
• Factor enhanced DeepSurv
• Random survival forest

• Humans
• Deep Learning
• Liver Cirrhosis/mortality
• Liver Cirrhosis, Biliary/mortality
• Proportional Hazards Models
• Survival Analysis
• Male
• Algorithms
• Female

• AI technology
• Cancer diagnosis
• Cancer prevention
• Early detection

• Acute lymphoblastic leukemia
• Classification
• Data mining
• Machine learning
• Mortality
• Relapse

• Humans
• Precursor Cell Lymphoblastic Leukemia-Lymphoma/mortality
• Machine Learning
• Child
• Prognosis
• Child, Preschool
• Male
• Female
• Adolescent
• Retrospective Studies
• Infant
• Recurrence

• Artificial intelligence
• Behavioral health
• Data science
• Quality of life

• Artificial intelligence
• Clinical decision systems
• Clinical rules
• Hybrid architectures
• Machine learning
• Rule-based systems

• Humans
• Artificial Intelligence
• Machine Learning
• Algorithms
• Surveys and Questionnaires

• Breast cancer
• HER2
• Hybrid bayesian network
• Prognostic model

• Humans
• Female
• Prognosis
• Breast Neoplasms/drug therapy
• Bayes Theorem

• 2022SJZD141 The Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province
• 81872711 National Natural Science Foundation of China

• Breast cancer
• Deep learning
• Image segmentation
• Tumor segmentation

• Bibliometrix analysis
• artificial intelligence
• breast cancer
• diagnosis and prognosis
• knowledge structures

We propose a framework for jointly modelling tumour size at diagnosis and time to distant metastatic spread, from diagnosis, based on latent dynamic sub-models of growth of the primary tumour and of distant metastatic detection. The framework also includes a sub-model for screening sensitivity as a function of latent tumour size. Our approach connects post-diagnosis events to the natural history of cancer and, once refined, may prove useful for evaluating new interventions, such as personalised screening regimes. We evaluate our model-fitting procedure using Monte Carlo simulation, showing that the estimation algorithm can retrieve the correct model parameters, that key patterns in the data can be captured by the model even with misspecification of some structural assumptions, and that, still, with enough data it should be possible to detect strong misspecifications. Furthermore, we fit our model to observational data from an extension of a case-control study of post-menopausal breast cancer in Sweden, providing model-based estimates of the probability of being free from detected distant metastasis as a function of tumour size, mode of detection (of the primary tumour), and screening history. For women with screen-detected cancer and two previous negative screens, the probabilities of being free from detected distant metastases 5 years after detection and removal of the primary tumour are 0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. We also study the probability of having latent/dormant metastases at detection of the primary tumour, estimating that 33% of patients in our study had such metastases.

• Breast cancer
• continuous growth model
• distant metastatic spread
• natural history model
• survival analysis

• Breast Neoplasms/diagnosis
• Case-Control Studies
• Early Detection of Cancer
• Female
• Humans
• Mammography
• Mass Screening

• Swedish Research Council
• Cancerfonden
• Swedish e-Science Research Centre

• Scopus database
• artificial intelligence
• cancer
• control
• diagnosis
• machine learning
• prevention

• Artificial Intelligence
• Bibliometrics
• Delivery of Health Care
• Humans
• Machine Learning
• Neoplasms/diagnosis
• Publications

• Jiangsu Provincial Health Development Center Open Project

• outcomes
• predictive modeling
• radiogenomics
• radiomics
• radiotherapy

• R01 CA233487 NCI NIH HHS
• R41 CA243722 NCI NIH HHS
• 75N92022D00018 NHLBI NIH HHS
• P30 CA008748 NCI NIH HHS
• 75N99020D00018 ORFDO NIH HHS
• R37 CA222215 NCI NIH HHS
• 75N94020D00018 NICHD NIH HHS
• 75N92023D00018 NHLBI NIH HHS

• TCGA
• attention mechanism
• cancer prediction
• gating mechanism
• graph convolutional network

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

• artificial intelligence
• diabetes
• digital health
• ethnicity
• ethnoracial equity
• race

• psychiatric disorders
• biomarkers

• Algorithms
• Artificial Intelligence
• Deep Learning
• Humans
• Mental Health
• Outcome Assessment, Health Care

• R01 MH112148 NIMH NIH HHS
• P50 MH113838 NIMH NIH HHS
• R01 MH105384 NIMH NIH HHS
• R01 MH121922 NIMH NIH HHS
• R01 MH119177 NIMH NIH HHS
• National Science Foundation
• U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)

• acoustic neuroma
• artificial intelligence
• artificial neural network
• logistic regression
• recurrence
• vestibular schwannoma

• T32 GM008620 NIGMS NIH HHS
• TL1 TR001415 NCATS NIH HHS
• UL1 TR001414 NCATS NIH HHS
• National Center for Advancing Translational Sciences
• National Center for Research Resources

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

• associative memory (AM)
• associative processing (AP)
• breast cancer detection (BCD)
• neural network (NN)
• pattern recognition (PR)

• Adaptive neuro fuzzy inference network
• Deep stacked sparse auto-encoders
• Fetal state
• Neural network

Cumulative estrogen concentration is an important determinant of the risk of developing breast cancer. Estrogen carcinogenesis is attributed to the combination of receptor-driven mitogenesis and DNA damage induced by quinonoid metabolites of estrogen. The present study was focused on developing an improved breast cancer prediction model using estrogen quinone-protein adduct concentrations. Blood samples from 152 breast cancer patients and 71 healthy women were collected, and albumin (Alb) and hemoglobin (Hb) adducts of estrogen-3,4-quinone and estrogen-2,3-quinone were extracted and evaluated as potential biomarkers of breast cancer. A multilayer perceptron (MLP) was used as the predictor model and the resultant prediction of breast cancer was more accurate than other existing detection methods. A MLP using the logarithm of the concentrations of the estrogen quinone-derived adducts (four input nodes, 10 hidden nodes, and one output node) was used to predict breast cancer risk with accuracy close to 100% and area under curve (AUC) close to one. The AUC value of one showed that both data sets were separable. We conclude that Alb and Hb adducts of estrogen quinones are promising biomarkers for the early detection of breast cancer.

• Adolescent
• Adult
• Aged
• Biomarkers, Tumor/blood
• Breast Neoplasms/blood
• Estrogens/blood
• Female
• Hemoglobins/metabolism
• Humans
• Middle Aged
• Models, Biological
• Predictive Value of Tests
• Quinones/blood
• Serum Albumin, Human/metabolism

• National Science Council
• National Science Council (TW)

The prediction of relapse in childhood acute lymphoblastic leukemia (ALL) is a critical factor for successful treatment and follow-up planning. Our goal was to construct an ALL relapse prediction model based on machine learning algorithms. Monte Carlo cross-validation nested by 10-fold cross-validation was used to rank clinical variables on the randomly split training sets of 336 newly diagnosed ALL children, and a forward feature selection algorithm was employed to find the shortest list of most discriminatory variables. To enable an unbiased estimation of the prediction model to new patients, besides the split test sets of 150 patients, we introduced another independent data set of 84 patients to evaluate the model. The Random Forest model with 14 features achieved a cross-validation accuracy of 0.827 ± 0.031 on one set and an accuracy of 0.798 on the other, with the area under the curve of 0.902 ± 0.027 and 0.904, respectively. The model performed well across different risk-level groups, with the best accuracy of 0.829 in the standard-risk group. To our knowledge, this is the first study to use machine learning models to predict childhood ALL relapse based on medical data from Electronic Medical Record, which will further facilitate stratification treatments.

• Bayesian network
• Heuristic
• Systematic

• Breast cancer
• CAD, computer-aided diagnosis
• Cancer recurrence
• Computer-assisted diagnosis
• DT, decision tree
• FH, family history of cancer
• HPBCR, the proposed hybrid predictor of breast cancer recurrence
• HRT, hormone therapy
• I. Node, number of involved axillary lymph nodes
• Machine learning
• NR, lymph node involvement ratio
• Prognosis
• T. Node, number of dissected axillary lymph nodes
• TS, tumor size
• XRT, radiotherapy

• big data
• data mining
• machine learning
• radiotherapy
• systems radiobiology

• Breast Neoplasms
• Data Mining
• Decision Support Techniques
• Neural Networks
• Support Vector Machine
• Survival Analysis

• Hierarchical forward feature selection
• Positron emission tomography
• Prediction
• Prior knowledge
• Small sample
• Support vector machine

• Algorithms
• Breast Neoplasms/classification
• Breast Neoplasms/genetics
• Data Mining/methods
• Decision Trees
• Logistic Models
• Mutation
• Neural Networks, Computer

• Algorithms
• Atypical Squamous Cells of the Cervix
• Databases as Topic
• Decision Support Systems, Clinical
• Female
• Humans
• Neural Networks, Computer
• Precision Medicine
• Probability
• Reproducibility of Results
• Uterine Cervical Dysplasia/diagnosis
• Uterine Cervical Dysplasia/pathology

• DCE-MRI
• breast cancer
• diffusion MRI
• machine learning
• neoadjuvant chemotherapy
• predictive modeling

In this work, we propose a multilevel and multiparametric approach in order to model the growth and progression of oral squamous cell carcinoma (OSCC) after remission. OSCC constitutes the major neoplasm of the head and neck region, exhibiting a quite aggressive nature, often leading to unfavorable prognosis.

We formulate a Decision Support System assembling a multitude of heterogeneous data sources (clinical, imaging tissue and blood genomic), aiming to capture all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse. Moreover, we collect and analyze gene expression data from circulating blood cells throughout the follow-up period in consecutive time-slices, in order to model the temporal dimension of the disease. For this purpose a Dynamic Bayesian Network (DBN) is employed which is able to capture in a transparent manner the underlying mechanism dictating the disease evolvement, and employ it for monitoring the status and prognosis of the patients after remission.

By feeding as input to the DBN data from the baseline visit we achieve accuracy of 86%, which is further improved to complete discrimination when data from the first follow-up visit are also employed.

Knowing in advance the progression of the disease, i.e. identifying groups of patients with higher/lower risk of reoccurrence, we are able to determine the subsequent treatment protocol in a more personalized manner.