Research on Evaluation Model of Hospital Informatization Level Based on Decision Tree Algorithm

In order to improve the weight calculation accuracy of hospital informatization level evaluation and shorten the evaluation time, a research method of hospital informatization level evaluation model based on the decision tree algorithm is proposed. Using the decision tree algorithm combining fuzzy theory and ID3, the decision tree is constructed to analyze the hospital information data. By means of questionnaire survey, expert experience, mathematical statistics, and in-depth interview, information facilities construction, information resources construction, information scientific research application, management information, and information guarantee are selected as the nodes of the decision tree to evaluate the hospital information level. Construct the structural equation model, standardize the data, extract the weight of each evaluation index, and complete the evaluation of hospital informatization level. The experimental results show that the weight calculation results of this method are basically consistent with the actual results, and the evaluation efficiency is improved.

Data Preparation of the nuMoM2b Dataset

In 2010, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) started the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b), a prospective cohort study of a racially/ethnically/geographically diverse population of nulliparous women with singleton gestation. The nuMoM2b is a very large dataset, consisting of data for 10,038 patients with over 4,600 features per patient, spread out over 80 files. In this report, we share our experience preparing and working with this dataset. We present our data preprocessing of the nuMoM2b dataset to get a deeper understanding of the data, extract the most relevant features, make the fewest assumptions when filling in unknown values, and reducing the dimensionality of the data. We hope this report is useful to researchers interested in building machine learning and statistical models from the nuMoM2b dataset.

Gender differences in organ involvement and survival in systemic sclerosis – experience of a EUSTAR center

Introduction. The low overall prevalence of systemic sclerosis (SSc) and the low proportion of male patients have resulted in a scarcity of studies assessing sex differences in SSc patients, and contradictory results have often been observed. Material and method. We performed a retrospective observational study using data extract from the EULAR scleroderma trials and research (EUSTAR) cohort 096 . We looked at sex influence on disease characteristics at baseline and then focused on patients with at least 2 years of follow-up to estimate the effects of sex on disease progression and survival. Results. 173 patients with SSc were available for the baseline analyses. In the longitudinal analysis after a mean follow-up of 3.5(±0.65) years, male sex was associated with a higher risk of scleroderma renal crisis (OR:9.45 (1.49 to 59.69); p = 0.004), digital contractures (OR:8.2 (3.1 to 21.9); p < 0.001), arrhythmias (OR: 3.37 (1.36 to 8.34); p = 0.006), pulmonary fibrosis (OR: 3.56, (1.51 to 8.41); p = 0.003), pulmonary hypertension (OR: 3.01 (1.19 to 7.59); p = 0.016), severe vascular involvement (OR:2.86, (1.22 to 6.73); p = 0.013) and low ventricular ejection fraction (OR: 2.84, (1.2 to 6.73); p = 0.014). Males had significantly reduced survival time after diagnosis (p = 0.004). The most frequent causes of death were scleroderma renal crisis in males and pulmonary hypertension in females. Conclusions. Although more common in women, SSc appears as strikingly more severe in men. Our results demonstrate a higher risk of severe organ involvement and poor prognosis in men. These results raise the point of including sex in the management and the decision-making process.

Generating Enriched Synthetic German Hospital Claims Data – A Use Case Driven Approach

Clinical data and above all individual patient data are highly sensitive. All the more it is important to protect these critical information while analyzing and exploring their specifics for further research. However, in order to enable students and other researchers to develop decision support systems and to use modern data analysis methods such as intelligent pattern recognition, the provision of clinical data is essential. In order to allow this while completely protecting the privacy of a patient, we present a mixed approach to generate semantically and clinically realistic data: (1) We use available synthetic data, extract information on patient visits and diagnoses and adapt them to the encoding systems of German claims data; (2) based on a statistical analysis of real German hospital data, we identify distributions of procedures, laboratory data and other measurements and transfer them to the synthetic patient’s visits and diagnoses in a semi-automated way. This enabled us to provide students a data set that is as semantically and clinically realistic as possible to apply patient-level prediction algorithms within the development of clinical decision support systems without putting patient data at any risk.

POS0854 SEX DIFFERENCES IN SYSTEMIC SCLEROSIS PATIENTS IN A SINGLE CENTER IN EASTERN EUROPE

Background:The low overall prevalence of systemic sclerosis (SSc) and the low proportion of male patients have resulted in a scarcity of studies assessing sex differences in SSc patients, and contradictory results have often been observed.Objectives:The aim of the study was to assess differences in disease manifestations in a cohort of SSc patients according to gender.Methods:We performed a retrospective observational study using data extract from the EULAR scleroderma trials and research (EUSTAR) cohort 096.We looked at sex influence on disease characteristics at baseline and then focused on patients with at least 2 years of follow-up to estimate the effects of sex on disease progression and survival.Results:173 patients with SSc were available for the baseline analyses. Males were older (52,96 vs 45,88, p=0.009), were more likely to smoke (73% vs 7%, p<0,001), had more frequent diffuse skin involvement (73,1% vs 56,5%,p<0.01), higher modified Rodnan skin score (34,61% vs 17%, p=0.01) and activity score(84,62% vs 46,26%,p<0.001) and were more often associed with positive acute phase reactants (65,38% vs 38,77%, p=0.01). Severe interstitial lung disease was more common in males (59,09% vs 27,53%, p=0.003), also the presence of tendon friction rubs was more frequent in this sex group (23,07% vs 8,84%, p=0.032).In the longitudinal analysis after a mean follow-up of 3,5(±0,65) years, male sex was associated with a higher risk of scleroderma renal crisis (OR:9.45 (1.49 to 59.69); p=0.004), digital contractures (OR:8,2 (3,1 to 21,9); p<0,001), arrhythmias (OR:3,37 (1.36 to 8,34); p=0.006), pulmonary fibrosis (OR: 3.56, (1.51 to 8.41); p=0.003), pulmonary hypertension (OR: 3.01 (1.19 to 7,59); p=0.016), severe vascular involvement (OR:2,86, (1,22 to 6,73); p=0.013) and low ventricular ejection fraction (OR: 2,84, (1.2 to 6,73); p=0.014). Males had significantly reduced survival time after diagnosis (p=0,004). The most frequent causes of death were scleroderma renal crisis in males and pulmonary hypertension in females.Conclusion:Although more common in women, SSc appears as strikingly more severe in men. Our results demonstrate a higher risk of severe organ involvement and poor prognosis in men. These results raise the point of including sex in the management and the decision-making process.Disclosure of Interests:None declared

Understanding human resources management strategy in implementing good government practice: what research evidence say

Understanding human resource development strategies in good governance service practices are essential. For this reason, we have reviewed several study pieces of evidence to add to the enrichment of our study. The reading sources we are looking for are data-based sources from Google scholars, ERIC publications, and other literature sources. Furthermore, we take the analysis stage by involving the coding system, evaluation, data extract, in-depth interpretation, and drawing conclusions that are valid findings and high reliability because these findings have answered the questions and danced the hypothesis of this study. We depend on secondary data and descriptive qualitative study designs. Based on the discussion of the findings data, we can conclude that the HR governance strategy working in public service organizations is not only required for superior competence, technical skills, and mastery of the underlying laws and regulations, but they are state apparatuses that are even more crucial in shaping mental and behavioral professional, friendly in nurturing, honest, sympathetic, wise, intelligent, and highly responsible.

P469 The PROPER study: interim analysis of a pan-European real-world study of SB5 adalimumab biosimilar after transition from reference adalimumab in patients with Crohn’s disease

Background SB5, a biosimilar to reference adalimumab, received EU marketing authorisation in August 2017, based on pre-clinical and clinical Phase I and III studies that demonstrated bioequivalence, similar efficacy, and comparable safety and immunogenicity to the reference. The ongoing real-world study ‘PROPER’ is designed to provide insights into outcomes of the transition from reference to SB5 outside the controlled, randomised, clinical trial setting, and under an umbrella design has enrolled 1,000 patients with immune-mediated inflammatory disease, treated at centres in Belgium, Germany, Ireland, Italy, Spain and the UK. The objective of this interim analysis is to describe clinical characteristics and outcomes in patients with Crohn’s disease transitioned from reference to biosimilar adalimumab SB5. Methods Eligible patients had been transitioned to SB5 as part of routine treatment following a minimum of 16 weeks’ treatment with reference adalimumab. Data are captured from patient charts retrospectively for 24 weeks prior to and prospectively and/or retrospectively for 48 weeks after SB5 initiation. This first interim analysis of the Crohn’s disease cohort reports outcome measures including baseline clinical characteristics, disease activity, persistence on SB5, clinical management and safety for patients enrolled at 32 specialist sites and followed up to the data extract date of February 5th, 2021. Results Of the 459 patients included in this interim analysis, at time of data extract 108 had completed 48 weeks on SB5, 45 patients had discontinued SB5, and 10 had withdrawn from study. A disease flare was reported for 29 (6.3%) of patients, of whom 22 had no subsequent change in biologic treatment; 7 had a change, of whom 5 switched to a different biologic; 2 had secondary loss of response (physician reported). Twelve patients reported 13 serious adverse events, of which 4 (anal fistula, 2 perianal abscesses and subileus) were considered by study physician to be related to SB5 administration. In Table 1 below, baseline refers to the time of SB5 initiation. Conclusion This interim analysis provides a first insight into clinical management of patients in a contemporary study cohort of European patients with long-standing Crohn’s disease, transitioned from reference to biosimilar adalimumab SB5 and followed in clinical practice. The majority of patients showed no meaningful difference in disease activity or SB5 dosing regimen by Week 48 post-transition, and the Covid-19 pandemic seems to have had no impact on SB5 use in this cohort. No new safety concerns were detected.

Research on the Service Mode of the University Library Based on Data Mining

In the digital information age, data mining technology is becoming more widely used in libraries for its useful impact. In the context of big data, how to efficiently mine big data, extract features, and provide users with high-quality personalized service is one of the important issues that needs to be solved in the current university library big data application. Brain computing is a kind of comprehensive processing behavior of the human brain simulated by the computer, which can comprehensively analyze a variety of information and play a very good guiding role in processing library service behavior. This paper briefly introduces the related concepts and algorithms of data mining technology and deeply studies the classical algorithm of association rules, namely, Apriori algorithm, which analyzes the necessity and feasibility of applying data mining technology to university library management. The design idea and functional goal of the college book intelligent recommendation system are based on the decision tree method and association rule analysis method. Through the application research of data mining technology in the personalized service of the university library, combined with the actual work, this paper proposes data mining of association rules in the university library system. The research further elaborates on the system architecture, data processing, mining implementation algorithms, and application of mining results. The experimental results of the research have certain significance for the university library to explore personalized services, provide book recommendation services, and make corresponding decisions to optimize the library’s collection layout.

An In-Depth Analysis of The Data Mining Algorithms and Their Effective Applicability in Health and Biomedical Informatics

Nowadays, the Health care industry is one of the fastest-growing industries. As we already know, health care has researched very widely, introducing many medical data that is not easy to mine. Data mining is an approach that helps to discover essential data from massive data or collection of data. So, in medical Science, there is a need for tools that help analyses the data, extract the significant result from massive data, and discover efficient use of information. Generally, three things are mandatory in medical for every patient. First is patient details, diagnosis and medications. Converting these data into a basic pattern for predicting the patient disease helps in early diagnosis. This research mainly focuses on the data mining approach, which is widely considered in the medical field.

Teaching and Assessing Data Literacy for Adolescent Learners

Today, adolescent learners are exposed to deepfakes from online news to social media. They need data literacy—being able to pose questions about data, extract relevant information, and evaluate claims about data—to retrieve factual information and take informed actions. However, not many students in the U.S. are equipped with data literacy to detect deepfakes. This chapter examines existing practices for teaching and assessing data literacy and suggests best practices for supporting adolescent learners in attaining data literacy. This chapter also discusses the future steps needed to implement these best practices in the classroom so that young learners can mitigate the impacts of deepfakes in their lives.