Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

BACKGROUND: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients\u27 notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. OBJECTIVE: We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. METHODS: First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians\u27 agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. RESULTS: Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen\u27s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P \u3c .001). Rich learning features contributed to FOCUS\u27s performance substantially. CONCLUSIONS: FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care

Preoperative imatinib for patients with primary unresectable or metastatic/recurrent gastrointestinal stromal tumor

OBJECTIVES: Despite its rising popularity, reports on the use of preoperative imatinib mesylate (IM) in patients with advanced gastrointestinal stromal tumor (GIST) are limited. This study aims to explore the clinical efficacy of preoperative IM in patients with primarily unresectable or metastatic/recurrent GIST. METHODS: Between September 2009 and February 2014, patients with primarily unresectable or metastatic/recurrent GIST treated by a single medical team were recruited and considered for preoperative IM therapy. Re-examination was conducted regularly and abdominal enhanced CT data, blood biochemistry and responses to IM were recorded. RESULTS: A total of 18 patients were enrolled, including 13 with a primary tumor (7 stomach, 3 small bowel, 2 rectal and 1 pelvic tumor) and 5 with recurrent or metastatic GIST (2 with liver metastasis, 2 with anastomotic recurrence and 1 with pelvic GIST). The median follow-up time was 9.5 months (range of 3-63). The median tumor sizes before and after initiation of IM treatment were 9.1 cm and 6.0 cm (p = 0.003) based on the CT findings, respectively. All patients showed a decrease in tumor burden and the median tumor size reduction was 35%. Sixteen of the 18 patients showed a partial response to IM and two possessed stable disease. Nine of the 18 patients (50%) underwent surgical resection of primary or metastatic/recurrent tumors, with a median of 7 months of IM therapy. One case each of multivisceral resection and tumor recurrence were noted. CONCLUSIONS: IM as a preoperative therapy is feasible and safe for unresectable or metastatic/recurrent GIST that can effectively decrease tumor size, facilitating resection

Behaviour of a FRP anchor for seismic strengthening of clay brick masonry walls

Fibre reinforced polymer (FRP) anchors made from rolled or folded fibres have been shown to be an effective technology for delaying or even preventing premature debonding failure in concrete structures strengthened with externally bonded FRP. It would naturally be expected that the use of FRP anchors can improve the earthquakeresistance of FRP strengthened structures by increasing its loading capacity and ductility especially the latter. This study explores the application of FRP anchors in seismic strengthening of clay brick walls. One unique feature of such a system is that the brick unit has smaller dimensions compared to common concrete specimens. This paper reports an experimental pull out study of these FRP anchors. Test parameters included anchor construction, the diameter of the anchor, and the size of predrilled holes in clay brick. The experimental results indicate that FRP anchors can be designed to achieve high loading capacities and hence can be effectively used to prevent or delay FRP debonding failure. The results also indicate that the geometry of the anchor system has a significant effect on its loading capacity

Cyanoacrylate Injection Compared with Band Ligation for Acute Gastric Variceal Hemorrhage: A Meta-Analysis of Randomized Controlled Trials and Observational Studies

Background. Cyanoacrylate injection (GVO) and band ligation (GVL) are effective treatments for gastric variceal hemorrhage. However, data on the optimal treatment are still controversial. Methods. For our overall analysis, relevant studies were identified from several databases. For each outcome, data were pooled using a fixed-effect or random-effects model according to the result of a heterogeneity test. Results. Seven studies were included. Compared with GVL, GVO was associated with increased likelihood of hemostasis of active bleeding (odds ratio [OR] = 2.32; 95% confidence interval [CI] = 1.19–4.51) and a longer gastric variceal rebleeding-free period (hazard ratio = 0.37; 95% CI = 0.24–0.56). No significant differences were observed between GVL and GVO for mortality (hazard ratio = 0.66; 95% CI = 0.43–1.02), likelihood of variceal obliteration (OR = 0.89; 95% CI = 0.52–1.54), number of treatment sessions required for complete variceal eradication (weighted mean difference = −0.45; 95% CI = −1.14–0.23), or complications (OR = 1.02; 95% CI = 0.48–2.19). Conclusion. GVO may be superior to GVL for achieving hemostasis and preventing recurrence of gastric variceal rebleeding but has no advantage over GVL for mortality and complications. Additional studies are warranted to enable definitive conclusions

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models

We focus on the challenge of out-of-distribution (OOD) detection in deep learning models, a crucial aspect in ensuring reliability. Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs. We propose a novel one-class open-set OOD detector that leverages text-image pre-trained models in a zero-shot fashion and incorporates various descriptions of in-domain and OOD. Our approach is designed to detect anything not in-domain and offers the flexibility to detect a wide variety of OOD, defined via fine- or coarse-grained labels, or even in natural language. We evaluate our approach on challenging benchmarks including large-scale datasets containing fine-grained, semantically similar classes, distributionally shifted images, and multi-object images containing a mixture of in-domain and OOD objects. Our method shows superior performance over previous methods on all benchmarks. Code is available at https://github.com/gyhandy/One-Class-AnythingComment: 16 pages (including appendix and references), 3 figure

Exploring causal associations of alcohol with cardiovascular and metabolic risk factors in a Chinese population using Mendelian randomization analysis

Observational studies suggest that moderate alcohol consumption may be protective for cardiovascular disease, but results may be biased by confounding and reverse causality. Mendelian randomization, which uses genetic variants as proxies for exposures, can minimise these biases and therefore strengthen causal inference. Using a genetic variant in the ALDH2 gene associated with alcohol consumption, rs671, we performed a Mendelian randomization analysis in 1,712 diabetes cases and 2,076 controls from Nantong, China. Analyses were performed using linear and logistic regression, stratified by sex and diabetes status. The A allele of rs671 was strongly associated with reduced odds of being an alcohol drinker in all groups, but prevalence of alcohol consumption amongst females was very low. The A allele was associated with reduced systolic and diastolic blood pressure and decreased total and HDL cholesterol in males. The A allele was also associated with decreased triglyceride levels, but only robustly in diabetic males. There was no strong evidence for associations between rs671 and any outcomes in females. Our results suggest that associations of alcohol consumption with blood pressure and HDL-cholesterol are causal. Alcohol also appeared to have adverse effects on triglyceride levels, although this may be restricted to diabetics

Development and Validation of a Prognostic Nomogram for Extremity Soft Tissue Leiomyosarcoma

Background: Extremity soft tissue leiomyosarcoma (LMS) is a rare disease with a poor prognosis. The aim of this study is to develop nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with extremity soft tissue LMS.Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, 1,528 cases of extremity soft tissue LMS diagnosed between 1983 and 2015 were included. Cox proportional hazards regression modeling was used to analyze prognosis and obtain independent predictors. The independent predictors were integrated to develop nomograms predicting 5- and 10-year OS and CSS. Nomogram performance was evaluated by a concordance index (C-index) and calibration plots using R software version 3.5.0.Results: Multivariate analysis revealed that age ≥60 years, high tumor grade, distant metastasis, tumor size ≥5 cm, and lack of surgery were significantly associated with decreased OS and CSS. These five predictors were used to construct nomograms for predicting 5- and 10-year OS and CSS. Internal and external calibration plots for the probability of 5- and 10-year OS and CSS showed excellent agreement between nomogram prediction and observed outcomes. The C-index values for internal validation of OS and CSS prediction were 0.776 (95% CI 0.752–0.801) and 0.835 (95% CI 0.810–0.860), respectively, whereas those for external validation were 0.748 (95% CI 0.721–0.775) and 0.814 (95% CI 0.785–0.843), respectively.Conclusions: The proposed nomogram is a reliable and robust tool for accurate prognostic prediction in patients with extremity soft tissue LMS