Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question-answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

Multidimensional analysis of job advertisements for medical record information managers.

Objective: The rapid growth of the medical industry has resulted in a tremendous increase in medical record data, which can be utilized for hospital management, aiding in diagnosis and treatment, medical research, and other purposes. For data management and analysis, medical institutions require more qualified medical record information managers. In light of this, we conducted an analysis of the qualifications, abilities, and job emphasis of medical record information managers in order to propose training recommendations. Materials and methods: From online job posting sites, a sample of 241 job advertisements for medical record information management positions posted by Chinese healthcare institutions were collected. We conducted word frequency and keyword co-occurrence analysis to uncover overall demands at the macro level, and job analysis to investigate job-specific disparities at the micro level. Based on content analysis and job analysis, a competency framework was designed for medical record information managers. Results: The most frequent keywords were "code," "job experience," and "coding certification," according to the word frequency analysis. The competency framework for managers of medical record information is comprised of seven domains: essential knowledge, medical knowledge, computer expertise, problem-solving skills, leadership, innovation, and attitude and literacy. One of the fundamental skills required of medical record information managers is coordination and communication. Similarly, knowledge and skill requirements emphasize theoretical knowledge, managerial techniques, performance enhancement, and innovation development. Conclusion: According to organization type and job differences, the most crucial feature of the job duties of medical record information managers is cross-fertilization. The findings can be utilized by various healthcare organizations for strategic talent planning, by the field of education for medical record information managers for qualification and education emphasis adjustment, and by job seekers to enhance their grasp of the profession and self-evaluation.

Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019.

BACKGROUND: Consumer health informatics (CHI) originated in the 1990s. With the rapid development of computer and information technology for health decision making, an increasing number of consumers have obtained health-related information through the internet, and CHI has also attracted the attention of an increasing number of scholars. OBJECTIVE: The aim of this study was to analyze the research themes and evolution characteristics of different study periods and to discuss the dynamic evolution path and research theme rules in a time-series framework from the perspective of a strategy map and a data flow in CHI. METHODS: The Web of Science core collection database of the Institute for Scientific Information was used as the data source to retrieve relevant articles in the field of CHI. SciMAT was used to preprocess the literature data and construct the overlapping map, evolution map, strategic diagram, and cluster network characterized by keywords. Besides, a bibliometric analysis of the general characteristics, the evolutionary characteristics of the theme, and the evolutionary path of the theme was conducted. RESULTS: A total of 986 articles were obtained after the retrieval, and 931 articles met the document-type requirement. In the past 21 years, the number of articles increased every year, with a remarkable growth after 2015. The research content in 4 different study periods formed the following 38 themes: patient education, medicine, needs, and bibliographic database in the 1999-2003 study period; world wide web, patient education, eHealth, patients, medication, terminology, behavior, technology, and disease in the 2004-2008 study period; websites, information seeking, physicians, attitudes, technology, risk, food labeling, patient, strategies, patient education, and eHealth in the 2009-2014 study period; and electronic medical records, health information seeking, attitudes, health communication, breast cancer, health literacy, technology, natural language processing, user-centered design, pharmacy, academic libraries, costs, internet utilization, and online health information in the 2015-2019 study period. Besides, these themes formed 10 evolution paths in 3 research directions: patient education and intervention, consumer demand attitude and behavior, and internet information technology application. CONCLUSIONS: Averaging 93 publications every year since 2015, CHI research is in a rapid growth period. The research themes mainly focus on patient education, health information needs, health information search behavior, health behavior intervention, health literacy, health information technology, eHealth, and other aspects. Patient education and intervention research, consumer demand, attitude, and behavior research comprise the main theme evolution path, whose evolution process has been relatively stable. This evolution path will continue to become the research hotspot in this field. Research on the internet and information technology application is a secondary theme evolution path with development potential.

Retrospective Study on the Influencing Factors and Prediction of Hospitalization Expenses for Chronic Renal Failure in China Based on Random Forest and LASSO Regression.

Aim: With the improvement in people's living standards, the incidence of chronic renal failure (CRF) is increasing annually. The increase in the number of patients with CRF has significantly increased pressure on China's medical budget. Predicting hospitalization expenses for CRF can provide guidance for effective allocation and control of medical costs. The purpose of this study was to use the random forest (RF) method and least absolute shrinkage and selection operator (LASSO) regression to predict personal hospitalization expenses of hospitalized patients with CRF and to evaluate related influencing factors. Methods: The data set was collected from the first page of data of the medical records of three tertiary first-class hospitals for the whole year of 2016. Factors influencing hospitalization expenses for CRF were analyzed. Random forest and least absolute shrinkage and selection operator regression models were used to establish a prediction model for the hospitalization expenses of patients with CRF, and comparisons and evaluations were carried out. Results: For CRF inpatients, statistically significant differences in hospitalization expenses were found for major procedures, medical payment method, hospitalization frequency, length of stay, number of other diagnoses, and number of procedures. The R2 of LASSO regression model and RF regression model are 0.6992 and 0.7946, respectively. The mean absolute error (MAE) and root mean square error (RMSE) of the LASSO regression model were 0.0268 and 0.043, respectively, and the MAE and RMSE of the RF prediction model were 0.0171 and 0.0355, respectively. In the RF model, and the weight of length of stay was the highest (0.730). Conclusions: The hospitalization expenses of patients with CRF are most affected by length of stay. The RF prediction model is superior to the LASSO regression model and can be used to predict the hospitalization expenses of patients with CRF. Health administration departments may consider formulating accurate individualized hospitalization expense reimbursement mechanisms accordingly.

Perioperative Complications and Safety Evaluation of Robot-Assisted Radical Hysterectomy of Cervical Cancer After Neoadjuvant Chemotherapy.

PURPOSE: To evaluate the perioperative complications of patients with cervical cancer who are treated with robot-assisted radical hysterectomy (RRH) and to further evaluate the safety of patients undergoing NACT. METHODS: A total of 805 consecutive cervical cancer patients undergoing RRH were involved in this report. Their clinical characteristics were retrieved from hospital medical records. Perioperative complications were subdivided into intraoperative and postoperative complications, which were graded according to the Clavien-Dindo classification (CDC), and the complications of grade III and above were defined as severe complications. Furthermore, the two-level logistic regression model was used to estimate the risk factors of perioperative and severe complications and to further confirm the relationship between NACT and perioperative and severe complications. RESULTS: The perioperative complication rate and severe complications were 45.09% and 7.83%, respectively. Poorly differentiated tumor and NACT were identified as independent risk factors for perioperative complications by multifactor analysis. Furthermore, we concentrated on the relations between NACT and complications. The risk of perioperative complications of the group with NACT (OR = 11.08, 95% CI: 5.70-21.54) was significantly higher than the group without NACT, especially in postoperative complications (OR=17.65, 95% CI: 8.63-36.08), even after adjusting confounding factors. However, there was no statistically significant difference in terms of severe complications (OR=1.68, 95% CI: 0.64-4.41) and intraoperative complications (OR=0.51, 95% CI: 0.18-1.41). Moreover, as the times of NACT increase, the impact on perioperative complications is more pronounced. A similar trend was observed in postoperative complications, while this statistical difference was still not observed in intraoperative and severe complications. CONCLUSION: This result demonstrates the feasibility and safety of RRH of cervical carcinoma after NACT in generally, since it only causes mild complications, not severe complications.

Multidimensional Feature Classification of the Health Information Needs of Patients With Hypertension in an Online Health Community Through Analysis of 1000 Patient Question Records: Observational Study.

BACKGROUND: With the rapid development of online health communities, increasing numbers of patients and families are seeking health information on the internet. OBJECTIVE: This study aimed to discuss how to fully reveal the health information needs expressed by patients with hypertension in their questions in a web-based environment and how to use the internet to help patients with hypertension receive personalized health education. METHODS: This study randomly selected 1000 text records from the question data of patients with hypertension from 2008 to 2018 collected from Good Doctor Online and constructed a classification system through literature research and content analysis. This paper identified the background characteristics and questioning intention of each patient with hypertension based on the patient's question and used co-occurrence network analysis and the k-means clustering method to explore the features of the health information needs of patients with hypertension. RESULTS: The classification system for the health information needs of patients with hypertension included the following nine dimensions: drugs (355 names), symptoms and signs (395 names), tests and examinations (545 names), demographic data (526 kinds), diseases (80 names), risk factors (37 names), emotions (43 kinds), lifestyles (6 kinds), and questions (49 kinds). There were several characteristics of the explored web-based health information needs of patients with hypertension. First, more than 49% of patients described features, such as drugs, symptoms and signs, tests and examinations, demographic data, and diseases. Second, patients with hypertension were most concerned about treatment (778/1000, 77.80%), followed by diagnosis (323/1000, 32.30%). Third, 65.80% (658/1000) of patients asked physicians several questions at the same time. Moreover, 28.30% (283/1000) of patients were very concerned about how to adjust the medication, and they asked other treatment-related questions at the same time, including drug side effects, whether to take the drugs, how to treat the disease, etc. Furthermore, 17.60% (176/1000) of patients consulted physicians about the causes of clinical findings, including the relationship between the clinical findings and a disease, the treatment of a disease, and medications and examinations. Fourth, by k-means clustering, the questioning intentions of patients with hypertension were classified into the following seven categories: "how to adjust medication," "what to do," "how to treat," "phenomenon explanation," "test and examination," "disease diagnosis," and "disease prognosis." CONCLUSIONS: In a web-based environment, the health information needs expressed by Chinese patients with hypertension to physicians are common and distinct, that is, patients with different background features ask relatively common questions to physicians. The classification system constructed in this study can provide guidance to health information service providers for the construction of web-based health resources, as well as guidance for patient education, which could help solve the problem of information asymmetry in communication between physicians and patients.

Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm.

AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP) and build up an area-specified senile cataract diagnosis related group (DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund. METHODS: The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc. RESULTS: The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases. CONCLUSION: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.