AimTo perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.Study eligibility criteriaCohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.Data sourcesArticles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.Data extractionWe extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.Bias assessmentA bias assessment of AI models was done using PROBAST.ParticipantsPatients tested positive for COVID-19.ResultsWe included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.ConclusionsA broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.
Background
Antitachycardia pacing (ATP) is an effective treatment for ventricular tachycardia (VT). We evaluated the efficacy of different ATP programs based on a large remote monitoring data set from patients with implantable cardioverter-defibrillators (ICDs).
Methods
A dataset from 18,679 ICD patients was used to evaluate the first delivered ATP treatment. We considered all device programs that were used for at least 50 patients, leaving us with 7 different programs and a total of 32,045 episodes. We used the two-proportions z-test (α = 0.01) to compare the probability of success and the probability for acceleration in each group with the corresponding values of the default setting.
Results
Overall, the first ATP treatment terminated in 78.4%–97.5% of episodes with slow VT and 81.5%–91.1% of episodes with fast VT. The default setting of the ATP programs with the number of sequences S = 3 was applied to treat 30.1% of the slow and 36.6% of the fast episodes. Reducing the maximum number of sequences to S = 2 decreased the success rate for slow VT (
P
< 0.0001,
h
= 0.38), while the setting S = 4 resulted in the highest success rate of 97.5% (
P < 0
.
0001
,
h
= 0.27).
Conclusion
While the default programs performed well, we found that increasing the number of sequences from 3 to 4 was a promising option to improve the overall ATP performance.
Strategic alliances are increasingly gaining popular for Textile companies to achieve fast and economical growth in today's globalization. Strategic alliances are an important source of resources, learning, and thereby core competencies improvement. So managers have to make conscious decisions to develop certain competencies so as to have all competencies that are required to be successful, firms look for strategic alliances and to leverage their partner firms' competencies. However coordination with alliance partners is not easy; each part has its own reporting process and measures, and each brings its own perspective of what it wants to contribute to the alliance and what it intents to obtain from the alliance. Transcending these informational and motivational asymmetries, as economists would phrase it, requires an open, transparent process in which both sides clearly articulate their expected contributions and their desired outcomes resulting in a document that summarizes the theory of the strategic for the alliance. Developing an Alliance Balanced Scorecard (ABS) can mitigate the natural conflict between alliance partners. It includes four-perspective framework: financial, strategic, operational, and relationship.This study has been made on how to apply the Balanced Scorecard on an alliance-making of 8 Textile companies as a result of a demand. We aim to provide a picture of what a prospective Textile Industries could look like, for the alliance-making in Iran, by developing an Alliance Strategy Map focused on the core competencies improvement. The work has been conducted as a qualitative case study at the Textile firms in Isfahan. The scorecard was developed by using analytic hierarchy process (AHP). We argue that results based on AHP analysis would help a company to make more informed strategic management decision concerning further investment for competences and key assets development, and outsourcing non-core assets and competences. This paper reports on the results of that empirical survey, and the results show that: Maintain market position; Expanding their competencies; Gain access to complementary resources; Compete against common competitor; Reducing risk and uncertainty are important influences on alliance making for textile companies. Careful strategic planning and good partnership preparation are essential for alliance success.
Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users.Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm.Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%).Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.
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