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Background: In recent years, the incidence of gastrointestinal neuroendocrine tumors (GI-NETs) has remarkably increased due to the widespread use of screening gastrointestinal endoscopy. Currently, the most common treatments are surgery and endoscopic resection. Compared to surgery, endoscopic resection possesses a higher risk of resection margin residues for the treatment of GI-NETs. Methods: A total of 315 patients who underwent surgery or endoscopic resection for GI-NETs were included. We analyzed their resection modality (surgery, ESD, EMR), margin status, Preoperative marking and Prognosis. Results: Among 315 patients included, 175 cases underwent endoscopic resection and 140 cases underwent surgical treatment. A total of 43 (43/175, 24.57%) and 10 (10/140, 7.14%) patients exhibited positive resection margins after endoscopic resection and surgery, respectively. Multivariate regression analysis suggested that no preoperative marking and endoscopic treatment methods were risk factors for resection margin residues. Among the patients with positive margin residues after endoscopic resection, 5 patients underwent the radical surgical resection and 1 patient underwent additional ESD resection. The remaining 37 patients had no recurrence during a median follow-up of 36 months. Conclusions: Compared with surgery, endoscopic therapy has a higher margin residual rate. During endoscopic resection, preoperative marking may reduce the rate of lateral margin residues, and endoscopic submucosal dissection may be preferred than endoscopic mucosal resection. Periodical follow-up may be an alternative method for patients with positive margin residues after endoscopic resection. [ABSTRACT FROM AUTHOR]

BACKGROUND: To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgement model by designing a virtuous cycle of inpatient cost supervision information system and exploring a complete set of inpatient cost supervision methods. OBJECTIVE: To lay the foundation for applying artificial intelligence (AI) technology in medical insurance cost control supervision and provide feasible paths and available tools for medical insurance cost control managers. METHODS: By way of collecting and cleaning electronic medical record (EMR) data from 2016 to 2018 of a city in East China, focusing on basic patient information and cost information, and using a combination of machine learning modeling and information system construction, the study tries to form a feasible inpatient cost supervision method and operation path. RESULTS: The set of the regulatory method, applied in nursing homes of a city in East China, is compelling. The accuracy rates of rationality judgement in different main diseases are stable up to 80%, the false positive rate is steady within 10%, and rehabilitation fee days of hospitalization, and the number of complications are important factors affecting the rationality of the inpatient cost. CONCLUSION: The model construction and optimization method combining machine learning and information system can make practical cost rationality judgement on medical institution's inpatient cost data, which can directly reflect the key influencing factors of relevant inpatient costs, and achieve the effect of guiding medical behavior and improving the efficiency of medical insurance fund use. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMR)-trained machine learning models have the potential in CVD risk prediction by integrating a range of medical data from patients, facilitate timely diagnosis and classification of CVDs. We tested the hypothesis that unsupervised ML approach utilizing EMR could be used to develop a new model for detecting prevalent CVD in clinical settings., Methods: We included 155,894 patients (aged ≥ 18 years) discharged between January 2014 and July 2022, from Xuhui Hospital, Shanghai, China, including 64,916 CVD cases and 90,979 non-CVD cases. K-means clustering was used to generate the clustering models with k = 2, 4, and 8 as predetermined number of clusters k = 2, 4, and 8. Bayesian theorem was used to estimate the models' predictive accuracy., Results: The overall predictive accuracy of the 2-, 4-, and 8-classification clustering models in the training set was 0.856, 0.8634, and 0.8506, respectively. Similarly, the predictive accuracy of the 2-, 4-, and 8-classification clustering models in the testing set was 0.8598, 0.8659, and 0.8525, respectively. After reducing from 19 dimensions to 2 dimensions by principal component analysis, significant separation was observed for CVD cases and non-CVD cases in both training and testing sets., Conclusion: Our findings indicate that the utilization of EMR data can support the development of a robust model for CVD detection through an unsupervised ML approach. Further investigation using longitudinal design is needed to refine the model for its applications in clinical settings., Competing Interests: Declarations. Ethics approval and consent to participate: This study was performed in accordance with the guidelines of the Declaration of Helsinki. The study design was approved by the Ethics Committee of Shanghai Xuhui Central Hospital (approval no.: 2023033), and the institutional review board waived the requirement to obtain the informed consent. Consent for publication: All authors have approved for its publication. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: Clinical named entity recognition (CNER) is a fundamental task in natural language processing used to extract named entities from electronic medical record texts. In recent years, with the continuous development of machine learning, deep learning models have replaced traditional machine learning and template-based methods, becoming widely applied in the CNER field. However, due to the complexity of clinical texts, the diversity and large quantity of named entity types, and the unclear boundaries between different entities, existing advanced methods rely to some extent on annotated databases and the scale of embedded dictionaries., Objective: This study aims to address the issues of data scarcity and labeling difficulties in CNER tasks by proposing a dataset augmentation algorithm based on proximity word calculation., Methods: We propose a Segmentation Synonym Sentence Synthesis (SSSS) algorithm based on neighboring vocabulary, which leverages existing public knowledge without the need for manual expansion of specialized domain dictionaries. Through lexical segmentation, the algorithm replaces new synonymous vocabulary by recombining from vast natural language data, achieving nearby expansion expressions of the dataset. We applied the SSSS algorithm to the Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) + conditional random field (CRF) and RoBERTa + Bidirectional Long Short-Term Memory (BiLSTM) + CRF models and evaluated our models (SSSS + RoBERTa + CRF; SSSS + RoBERTa + BiLSTM + CRF) on the China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2019 datasets., Results: Our experiments demonstrated that the models SSSS + RoBERTa + CRF and SSSS + RoBERTa + BiLSTM + CRF achieved F1-scores of 91.30% and 91.35% on the CCKS-2017 dataset, respectively. They also achieved F1-scores of 83.21% and 83.01% on the CCKS-2019 dataset, respectively., Conclusions: The experimental results indicated that our proposed method successfully expanded the dataset and remarkably improved the performance of the model, effectively addressing the challenges of data acquisition, annotation difficulties, and insufficient model generalization performance., (© Jian Tang, Zikun Huang, Hongzhen Xu, Hao Zhang, Hailing Huang, Minqiong Tang, Pengsheng Luo, Dong Qin. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

• Harmless treatment of the EMR. • Modified EMR was utilized as a mineral admixture. • Environmental performance analysis of Modified EMR. Electrolytic manganese residue (EMR) is a general industrial solid waste which also contains some pollutants. So the accumulation of EMR causes severe land resource occupy and potential environmental risks. This study focuses on the new eco-friendly cement manufactured with EMR using the harmless treatment technology of calcination as a mineral admixture. Specifically, the particular pollutants, physical performance and environmental characterization were mainly investigated of cement mineral admixture made of recycled EMR. At the same time, the hydration process and mechanism of all products including EMR were studied to using XRD, FT-IR and SEM. The result showed that the ammonia nitrogen and manganese in the leaching solution EMR were 1134 mg/L and 8356 mg/L, respectively, both far exceed the Chinese standard requirements. The high-temperature (650℃) harmless technology can minimize the pollutants in EMR, such as the leaching content of COD, ammonia nitrogen and manganese sulfate were all decreases up to 80%. As well as, the flue gas particular pollutants concentration of Mn, SO 2 and NO x of EMR were all meet the China standard. And the heavy metals' results of single factor index method and potential ecological environment risk were all meet the relevant standard, and the contents of heavy metals in EMR are all under the level of national standard. In addition, the strength of cement mortar can be promoted when the content of modified EMR calcined at 650 °C is less than 3%, and its physical properties were all meet the standard requirement, and more ettringite AFt and C-S-H gel were formed in the hydration product system and filled in the pores, and the hydration products were more evenly distributed and the structure was denser, as well as, the environmental indicators of cement mixed with 3% calcined EMR met the requirements of the standard. This study can provide an effective way to safety and environmental protection dispose the electrolytic manganese residue. [ABSTRACT FROM AUTHOR]

Objectives: To describe direct medical costs associated with each line of treatment among metastatic colorectal cancer (mCRC) patients in China. Methods: Electronic medical records between 2011 and 2016 were extracted from 12 tertiary hospitals in China for adult patients who initiated third-line treatment at least nine months before the end of data collection. Direct medical costs included costs of wards, diagnostic tests, surgical procedures, special materials, drugs and others. Costs were assessed by line of treatment, and drug costs were further breakdown for patients receiving chemotherapy alone and those receiving chemo- and biologics-combined therapy. Results: Of the 404 mCRC patients, the mean age was 55 years old and 62% were male. Oxaliplatin- and irinotecan-based regimens dominated first- and second-line treatment, respectively (44 and 37%). From first- to second- to third-line, the proportion of patients receiving targeted biologics increased from 18% at first-line and 12% at second-line to 34% at third-line; median number of treatment cycles reduced from 6 to 4 and to 2. The corresponding mean direct medical costs per person per cycle increased from $2,514 to $2,678 to $5,121. Mean drug costs per cycle increased from $2,314 to $2,673 to $4,316 among patients receiving chemotherapy alone and from $3,245 to $2,717 to $6,533 among patients receiving chemo- and biologics-combined therapy. Conclusions: Before 2017, mCRC patients in China did not receive the maximum benefits of precision medicine breakthroughs. Reduced treatment cycles and increased costs per cycle from first- to third-line suggested poor healthcare resource utilization. With earlier initiation and more treatment cycles, targeted biologics may better demonstrate their effectiveness among Chinese patients. Our findings reflected the urgent need to increase drug accessibility in China before 2017 and underscore that including innovative biologics into Chinese health insurance plans can reduce patients' economic burden and improve the management of mCRC.