Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision

The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts. We propose a method using ontologies and weak supervision. The approach includes two steps: (i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak supervision based on customised rules and Bidirectional Encoder Representations from Transformers (BERT) based contextual representations, and (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a case study, we show that the Text-to-UMLS process can be greatly improved with weak supervision, without any annotated data from domain experts. Our analysis shows that the overall pipeline processing discharge summaries can surface rare disease cases, which are mostly uncaptured in manual ICD codes of the hospital admissions.Comment: 5 pages, 3 figures, accepted for IEEE EMBC 202

Early malperfusion, ischemia reperfusion injury, and respiratory failure in acute complicated type B aortic dissection after thoracic endovascular repair

BACKGROUND: The aim of this study was to determine the early mortality and major complications of acute complicated type B aortic dissection (ACBD) after thoracic endovascular aortic repair (TEVAR). METHODS: Twenty-six consecutive patients with ACBD who underwent TEVAR were included. Clinical indications before TEVAR and in-hospital mortality and major complications after TEVAR were analyzed and compared with similar reports. RESULTS: TEVAR was technically successful in all cases. In-hospital mortality occurred in four patients (15%), and major complications occurred in an additional four patients (15%). Three of the four (75%) of the deaths were associated with malperfusion and ischemia reperfusion injury (IRI), and 3/4 (75%) of the major complications were caused by respiratory failure (RF). CONCLUSIONS: In-hospital mortality associated strongly with severe end-organ malperfusion and IRI, while major complications associated with RF, during TEVAR. Our results indicate that malperfusion, IRI and respiratory failure during TEVAR should be carefully monitored and aggressively treated

What Is Wrong With Chinese Soccer? Consumers’ Attention, Involvement, and Satisfaction

While Chinese supporters represent one of European soccer’s fastest-growing fan bases (Sporting Intelligence, 2011), the high-level of enthusiasm surrounding the European soccer leagues does not seem to be shared with China’s domestic league, the Chinese Super League (CSL). Soccer is in fact the most popular sport in China; however, locals have developed an attitude of resentment toward the product as they appear unhappy and unwilling to attend and watch CSL events, nor consume licensed CSL products. Based on these observations, the purpose of this study was to investigate Chinese soccer fans’ levels of satisfaction and dissatisfaction on the basis of their attention to and involvement with the CSL and its teams. According to Lavidge and Steiner’s (1961) hierarchy of effects model, there is an inherent sequence in cognition, affection, and conation, whereby awareness and attention can be linked to customer satisfaction. Seeing, experiencing, and evaluating form the essence of initial satisfaction (Oliver, 1997) and will lead to a consumer’s further attention, interest, and ultimately commitment to purchase a product or service (Anderson, Fornell, & Lehmann, 1994; Shank, 2009). Literature has shown that consumers with high levels of brand attention cope better with price changes that could potentially impact their behavioral intentions and satisfaction levels (Oh, 2000). Therefore, consumer attention to the CSL would experience higher levels of satisfaction with the league’s operations and marketing efforts (Hypothesis 1). Involvement is also known to play a critical role in determining consumer behavior because people with feelings of involvement tend to have expectations toward products or services that they consume or are planning to consume. Prior studies have identified the direct and indirect effects of involvement on levels of satisfaction (Mano & Oliver, 1993; Richins & Bloch, 1991; Swinyard, 1993). When an individual is continuously involved with a product or a service, he or she likely to be reasonably satisfied with the product or service and displays a high level of brand commitment. It was therefore hypothesized that consumers with a higher level of involvement would be more likely to search for information about a product or a service; when mediated by their satisfaction toward the product or service, they would be more likely to repurchase or reuse it (Hypothesis 2). Research participants (N = 926) were spectators at five CSL games, involving 10 of 16 CSL teams, held in five major Chinese cities. Based on the literature review and interviews of 26 CSL team administrators and coaches, a questionnaire was formulated that contained items geared toward measuring consumer attention, consumer involvement, and consumer satisfaction. The sample was randomly split into three sub-groups (n = 309, n = 309, and n = 308), which were respectively used to conduct an exploratory factor analysis (EFA) for the consumer satisfaction variables, confirmatory factor analysis (CFA) for the consumer satisfaction variables, and structural equation modeling (SEM) for examining the impact of consumer attention and involvement on consumer satisfaction. The EFA suggested three consumer satisfaction factors with 10 items retained. These factors were labeled as satisfaction with policy, satisfaction with operations, and satisfaction with marketing. The factor loadings ranged from .482 to .899. CFA confirmed the data fit of the model derived in EFA (x²/df = 2.154, RMSEA =.061, CFI = .963, SRMR = .042). Furthermore, the model displayed good reliability and validity evidence (AVE \u3e .5, CR \u3e .7, λ \u3e .6, rinter-factor \u3c .85). In SEM, the data fit the specified structural model reasonably well (x²/df = 2.463, RMSEA = .069, CFI = .945, and SRMR = .048). Among the proposed structural relationships, consumers’ attentions negatively influenced their satisfaction with marketing (β = -.169, p \u3c .05) and consumers’ involvements negatively influenced their satisfaction with operations (β = -.162, p \u3c .05). The relationships between attention and satisfaction with policy (β = -.025, p \u3e .05), between attention and satisfaction with operations (β = -.117, p \u3e .05), between involvement and satisfaction with policy (β = -.108, p \u3e .05), and between involvement and satisfaction with marketing (β = -.114, p \u3e .05) were not statistically significant. Contrary to the common beliefs that consumer’ attentions and involvements would positively affect one’s satisfaction, the findings of this study revealed opposite relationships between consumer cognition and affect. A reasonable explanation is that the CSL has serious issues in its operations and marketing, both of which are often resulted from the league’s administrative policies although consumers may not fully understand the presence, relevance, and impact of the policies on the other two observable aspects, namely operation and marketing. Many soccer fans in China very much care about the CSL and the general growth and development of soccer in China; however, they are very discouraged by the CSL’s ineffective administration and numerous corruptions, errors, and scandals in recent years. Constructive suggestions are offered at the end of project to improve the CSL’s operations and marketing efforts. Beyond China, it is hoped that the findings of this study can also aid league administrators in other countries where soccer fans give priority to foreign soccer games over their domestic products

Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review

BACKGROUND: People with type 2 diabetes have a higher risk of cardiovascular disease morbidity and mortality. We aim to distil the evidence, summarize the developments, and identify the gaps in relevant research on predicting cardiovascular disease in type 2 diabetes people using AI techniques in the last ten years. METHODS: A systematic search was carried out for literature published between 1st January 2010 and 30th May 2021 in five medical and scientific databases, including Medline, EMBASE, Global Health (CABI), IEEE Xplore and Web of Science Core Collection. All English language studies describing AI models for predicting cardiovascular diseases in adults with type 2 diabetes were included. The retrieved studies were screened and the data from included studies were extracted by two reviewers. The survey and synthesis of extracted data were conducted based on predefined research questions. IJMEDI checklist was used for quality assessment. RESULTS: From 176 articles identified by the search, 5 studies with sample sizes ranging from 560 to 203,517 met our inclusion criteria. The models predicted the risk of multiple cardiovascular diseases over 5 or 10 years. Ensemble learning, particularly random forest, is the most used algorithm in these models and consistently provided competitive performance. Commonly used features include age, body mass index, blood pressure measurements, and cholesterol measurements. Only one study carried out external validation. The area under the receiver operating characteristic curve for derivation cohorts varied from 0.69 to 0.77. AI models achieved better performance than conventional models in some specific scenarios. CONCLUSIONS: AI technologies seem to show promising performance (AUROC in external validation: 0.75 compared to 0.69 from conventional risk scores) for cardiovascular disease prediction in type 2 diabetes people. However, only one of the reviewed models conducted an external validation. Quality of reporting was low in general, and all models lack reproducibility and reusability

Ontology-driven and weakly supervised rare disease identification from clinical notes

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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

Efficacy and safety of stem cell therapy in cerebral palsy: A systematic review and meta-analysis

Aim: Although the efficacy and safety of stem cell therapy for cerebral palsy has been demonstrated in previous studies, the number of studies is limited and the treatment protocols of these studies lack consistency. Therefore, we included all relevant studies to date to explore factors that might influence the effectiveness of treatment based on the determination of safety and efficacy.Methods: The data source includes PubMed/Medline, Web of Science, EMBASE, Cochrane Library, from inception to 2 January 2022. Literature was screened according to the PICOS principle, followed by literature quality evaluation to assess the risk of bias. Finally, the outcome indicators of each study were extracted for combined analysis.Results: 9 studies were included in the current analysis. The results of the pooled analysis showed that the improvements in both primary and secondary indicators except for Bayley Scales of Infant and Toddler Development were more skewed towards stem cell therapy than the control group. In the subgroup analysis, the results showed that stem cell therapy significantly increased Gross Motor Function Measure (GMFM) scores of 3, 6, and 12 months. Besides, improvements in GMFM scores were more skewed toward umbilical cord mesenchymal stem cells, low dose, and intrathecal injection. Importantly, there was no significant difference in the adverse events (RR = 1.13; 95% CI = [0.90, 1.42]) between the stem cell group and the control group.Conclusion: The results suggested that stem cell therapy for cerebral palsy was safe and effective. Although the subgroup analysis results presented guiding significance in the selection of clinical protocols for stem cell therapy, high-quality RCTs validations are still needed

Quantitative learning strategies based on word networks

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network