To construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two ...radiomics models based on high-resolution computed tomography (HRCT) images.
We retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability.
Both models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability.
Nodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.
To examine the ability of computed tomography radiomic features in multivariate analysis and construct radiomic model for identification of the the WHO/ISUP pathological grade of clear cell renal ...cell carcinoma (ccRCC).
This was a retrospective study using data of four hospitals from January 2018 to August 2019. There were 197 patients with a definitive diagnosis of ccRCC by post-surgery pathology or biopsy. These subjects were divided into the training set (n = 122) and the independent external validation set (n = 75). Two phases of Enhanced CT images (corticomedullary phase, nephrographic phase) of ccRCC were used for whole tumor Volume of interest (VOI) plots. The IBEX radiomic software package in Matlab was used to extract the radiomic features of whole tumor VOI images. Next, the Mann-Whitney U test and minimum redundancy-maximum relevance algorithm(mRMR) was used for feature dimensionality reduction. Next, logistic regression combined with Akaike information criterion was used to select the best prediction model. The performance of the prediction model was assessed in the independent external validation cohorts. Receiver Operating Characteristic curve (ROC) was used to evaluate the discrimination of ccRCC in the training and independent external validation sets.
The logistic regression prediction model constructed with seven radiomic features showed the best performance in identification for WHO/ISUP pathological grades. The Area Under Curve (AUC) of the training set was 0.89, the sensitivity comes to 0.85 and specificity was 0.84. In the independent external validation set, the AUC of the prediction model was 0.81, the sensitivity comes to 0.58, and specificity was 0.95.
A radiological model constructed from CT radiomic features can effectively predict the WHO/ISUP pathological grade of CCRCC tumors and has a certain clinical generalization ability, which provides an effective value for patient prognosis and treatment.
A novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is ...presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the uncertain concepts. Interval set operations are discussed, and the connectives of concepts are represented by the operations on interval sets. Certain inclusion, possible inclusion, and weak inclusion relations between interval sets are presented, which are introduced to induce strong rules and weak rules from incomplete information table. The related properties of the inclusion relations are proved. It is concluded that the strong rules are always true whatever the missing values may be, while the weak rules may be true when missing values are replaced by some certain known values. Moreover, a confidence function is defined to evaluate the weak rule. The proposed approach presents a new view on rule induction from incomplete data based on interval set.
STEM education emphasizes improving student learning by linking abstract knowledge with real-world problems and engaging students in authentic projects to solve real-world problems. Accordingly, ...project-based learning has been widely promoted in STEM programs and has shown a promising impact on student learning. However, solving real-world problems in STEM projects involves complex processes. It remains unclear how students engage in complex problem-solving processes in STEM projects and how their processes may differ among students. This study was conducted with secondary school students who engaged in a design-based STEM project in small groups. The findings show that questioning and responding appeared most frequently and connected with other elements in group discourse, while argumentation and justification appeared least frequently. The findings reveal distinctive discourse patterns that differ among high-, medium- and low-performance groups, based on which the implications of the findings were discussed.
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available ...for machine learning and the need for data annotation from domain experts.
We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations.
The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes).
The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies.
Personalized learning has become an important and powerful paradigm catering for various needs, styles, preferences, and modes of learning. Several methods including task recommendations and path ...planning have recently emerged to effectively implement personalized learning using e-learning systems. The literature shows that the use of task recommendations in e-learning systems is a very effective way to facilitate personalized vocabulary learning. One of the key research issues regarding these personalized vocabulary learning systems is how to model the learning logs of each learner. Specifically, how to measure the learning effectiveness of each learned tasks has become a core issue for establishing personalized learning systems. Three theories, Spaced Learning (SL), Technique Feature Analysis (TFA), and Involvement Load Hypothesis (ILH), are commonly applied for achieving this purpose. In this study, we compared the effectiveness of these three linguistic theories for modeling EFL learners' personalized vocabulary learning via task recommendations. By conducting experimental studies among different groups of participants, our findings revealed that the ILH and TFA were more suitable than SL for facilitating personalized vocabulary learning. It is therefore suggested that future personalized vocabulary learning systems ought to be designed and developed based on comprehensive theoretical frameworks such as the ILH and TFA.
The construction of a harmonious society requires college students to coordinate their ideological, political, and moral qualities with social development and the needs of the times. Through the ...investigation and analysis of the ideological, political, and moral qualities of college students, on the one hand, we can see that the ideological, political, and moral qualities of college students are generally positive and healthy. On the other hand, it also exposes the outstanding problems in the ideological and political aspects of college students and the shortcomings of the ideological and political work in colleges and universities. This paper analyzes the dynamic changes of college students’ ideological and political changes and further studies the relationship between various indicators and students’ ideological and moral qualities through multiple linear regression analysis.
Inappropriate prescribing of medications and polypharmacy among older adults are associated with a wide range of adverse outcomes. It is critical to understand the attitudes towards ...deprescribing-reducing the use of potentially inappropriate medications (PIMs)-among this vulnerable group. Such information is particularly lacking in low - and middle-income countries.
In this study, we examined Chinese community-dwelling older adults' attitudes to deprescribing as well as individual-level correlates. Through the community-based health examination platform, we performed a cross-sectional study by personally interviews using the revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire (version for older adults) in two communities located in Suzhou, China. We recruited participants who were at least 65 years and had at least one chronic condition and one prescribed medication.
We included 1,897 participants in the present study; the mean age was 73.8 years (SD = 6.2 years) and 1,023 (53.9%) were women. Most of older adults had one chronic disease (n = 1,364 71.9%) and took 1-2 regular drugs (n = 1,483 78.2%). Half of the participants (n = 947, 50%) indicated that they would be willing to stop taking one or more of their medicines if their doctor said it was possible, and 924 (48.7%) older adults wanted to cut down on the number of medications they were taking. We did not find individual level characteristics to be correlated to attitudes to deprescribing.
The proportions of participants' willingness to deprescribing were much lower than what prior investigations among western populations reported. It is important to identify the factors that influence deprescribing and develop a patient-centered and practical deprescribing guideline that is suitable for Chinese older adults.
The unpredictability of business activities means that business process management should provide a way to adapt to change. The traditional workflow approach, based on predefined process logic, ...offers little support for today's complex and dynamic business environment. Therefore, a cognitive approach is proposed to help manage complex business activities, based on continuous awareness of situations and real-time decisions on activities. In this approach, the business environment is seen as capturing events that occurred and the state of tasks and resources; business logic involving process routing, operational constraints, exception handling and business strategy is used to determine which actions are appropriate for the current situation. By extending process management from process logic to business logic, the methodology offers flexibility, agility and adaptability in complex business process management.
E-learning is emerging as a popular approach of education in the workplace by virtue of its flexibility to access, just-in-time delivery, and cost-effectiveness. To improve social interaction and ...knowledge sharing in e-learning, Web 2.0 is increasingly utilized and integrated with e-learning applications. However, existing social learning systems fail to align learning with organizational goals and individual needs in a systemic way. The dominance of technology-oriented approaches makes e-learning applications less goal-effective and poor in quality and design. To solve the problem, we address the requirement of integrating organizational, social, and individual perspectives in the development of Web 2.0 e-learning systems. To fulfill the requirement, a key performance indicator (KPI)-oriented approach is presented in this study. By integrating a KPI model with Web 2.0 technologies, our approach is able to: 1) set up organizational goals and link the goals with expertise required for individuals; 2) build a knowledge network by linking learning resources to a set of competences to be developed and a group of people who learn and contribute to the knowledge network through knowledge creation, sharing, and peer evaluation; and 3) improve social networking and knowledge sharing by identifying each individual’s work context, expertise, learning need, performance, and contribution. The mechanism of the approach is explored and elaborated with conceptual frameworks and implementation technologies. A prototype system for Web 2.0 e-learning has been developed to demonstrate the effectiveness of the approach.
PDF
