scholarly journals Variation in the reporting of elective surgeries and its influence on patient safety indicators

2021 ◽  
Author(s):  
Kenneth John Locey ◽  
Thomas A Webb ◽  
Sana Farooqui ◽  
Bala Hota
Keyword(s):  
Machine Learning ◽  
Patient Safety ◽  
Quantile Regression ◽  
Claims Data ◽  
Machine Learning Algorithms ◽  
Patient Safety Indicators ◽  
Hospital Safety ◽  
Minimum Number ◽  
Related Group ◽  
Low Volume

Background: US hospital safety is routinely measured via patient safety indicators (PSIs). Receiving a score for most PSIs requires a minimum number of qualifying cases, which are partly determined by whether the associated diagnosis-related group (DRG) was surgical and whether the surgery was elective. While these criteria can exempt hospitals from PSIs, it remains to be seen whether exemption is driven by low volume, small numbers of DRGs, or perhaps, policies that determine how procedures are classified as elective. Methods: Using Medicare inpatient claims data from 4,069 hospitals between 2015 and 2017, we examined how percentages of elective procedures relate to numbers of surgical claims and surgical DRGs. We used a combination of quantile regression and machine learning based anomaly detection to characterize these relationships and identify outliers. We then used a set of machine learning algorithms to test whether outliers were explained by the DRGs they reported. Results: Average percentages of elective procedures generally decreased from 100% to 60% in relation to the number of surgical claims and the number of DRGs among them. Some providers with high volumes of claims had anomalously low percentages of elective procedures (5% to 40%). These low elective outliers were not explained by the particular surgical DRGs among their claims. However, among hospitals exempted from PSIs, those with the greatest volume of claims were always low elective outliers. Conclusion: Some hospitals with relatively high numbers of surgical claims may have classified procedures as non-elective in a way that ultimately exempted them from certain PSIs.

scholarly journals Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 241
Author(s):  
Dongwon Seo ◽  
Sunghyun Cho ◽  
Prabuddha Manjula ◽  
Nuri Choi ◽  
Young-Kuk Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fixation Index ◽  
Machine Learning Classification ◽  
Genetic Components ◽  
Marker Combination ◽  
A Genome ◽  
Minimum Number ◽  
Native Chickens

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

Identification of Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

2020 ◽  
Author(s):  
Dongwon Seo ◽  
Sunghyun Cho ◽  
Prabuddha Manjula ◽  
Nuri Choi ◽  
Young Kuk Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Snp Array ◽  
Machine Learning Algorithms ◽  
Case Group ◽  
Machine Learning Classification ◽  
Genetic Components ◽  
Native Chicken ◽  
Marker Combination ◽  
A Genome ◽  
Minimum Number

Abstract BackgroundA marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would also facilitate the protection of genetic resources, especially in developing countries. MethodsIn this study, a total of 20 lines 283 samples which were consist of Korean native chicken, commercial native chicken, and commercial broilers with layer population were used for finding the minimum number of marker combinations through the 600k high-density single nucleotide polymorphism (SNP) array. Application of the machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group from control chicken groups. In the verification of the selected markers, a total of 12 lines 182 samples were used to confirm the change in the accuracy of the target chicken breed identification.ResultsA total of 47,303 SNPs was used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by Adaboost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0% and 97.9%, respectively. The selected marker combinations increased the genetic distance between the case and control groups, and reduced the number of genetic components, confirming that an efficient classification of the groups was possible using small number of marker sets. In a verification study including additional chicken breeds and samples, the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations.ConclusionsThe GWAS and PCA analysis, machine learning algorithm used in this study is able to be applied efficiently to explore the minimum combination of markers that can distinguish varieties among a large number of SNP markers.

scholarly journals Factors Related with Nurse Compliance in The Implementation of Patient Safety Indicators at The X Hospital in Cilacap, Indonesia

2020 ◽  
Author(s):  
Taufik Alhidayah ◽  
Fransisca Sri Susilaningsih ◽  
Irman Somantri
Keyword(s):  
Patient Safety ◽  
Leadership Style ◽  
Sampling Technique ◽  
Head Nurse ◽  
Chi Square ◽  
Cross Sectional ◽  
Purposive Sampling ◽  
Safety Issues ◽  
Patient Safety Indicators ◽  
Hospital Safety

Patient safety is one of the five crucial hospital safety issues. This study aimed to determine factors related with nurses’ compliance in the implementation of indicators of patient safety goals (IPSG 1, IPSG 2, IPSG 5, and IPSG 6). This study was a descriptive correlative with a cross-sectional approach. Samples were recruited using a purposive sampling technique (n = 102). Data were analyzed using chi-square and Mann–Whitney tests. The results of this study indicate that the leadership style of the head nurse, rewards, attitudes, and motivation had a significant relationship with the level of adherence in the implementation of IPSG 1 and IPSG 2. The level of nurses’ compliance in the implementation of IPSG 5 was only influenced by the leadership style of the room head and the nurses’ positive attitude. None of the factors had significant relationships with the level of nurses’ compliance in the implementation of IPSG 6. The consultative leadership style of the room head can change the level of nurses’ compliance in the implementation of IPSG 1 by 5.6 times, with 5.06 times toward IPSG 2 and 4.71 times toward IPSG 5. This research recommends the need for consultative leadership style from the room head to carry out the roles and functions as a supervisor to improve associate nurses’ compliance in the implementation of IPSG 1, IPSG 2, IPSG 5, and IPSG 6. Abstrak Faktor-Faktor yang Berhubungan dengan Tingkat Kepatuhan Perawat dalam Implementasi Indikator Sasaran Keselamatan Pasien di Rumah Sakit X Cilacap, Indonesia. Keselamatan pasien adalah salah satu dari lima isu penting keselamatan di rumah sakit. Penelitian ini bertujuan untuk menentukan faktor-faktor yang terkait dengan kepatuhan perawat dalam penerapan indikator sasaran keselamatan pasien (IPSG 1, IPSG 2, IPSG 5, dan IPSG 6). Desain penelitian menggunakan deskriptif korelatif dengan pendekatan cross-sectional. Sampel diambil dengan menggunakan teknik purposive sampling (n = 102). Data dianalisis dengan menggunakan uji Chi-square dan Mann-Whitney. Hasil penelitian menunjukkan bahwa gaya kepemimpinan kepala ruangan, penghargaan, sikap, dan motivasi memiliki hubungan yang signifikan dengan tingkat kepatuhan dalam penerapan IPSG 1 dan IPSG 2. Tingkat kepatuhan perawat dalam penerapan IPSG 5 hanya dipengaruhi oleh gaya kepemimpinan kepala ruangan dan sikap positif perawat. Tidak ada faktor yang memiliki hubungan signifikan dengan tingkat kepatuhan perawat dalam penerapan IPSG 6. Gaya kepemimpinan konsultatif kepala ruangan dapat mengubah tingkat kepatuhan perawat dalam penerapan IPSG 1 sebesar 5,6 kali, dengan 5,06 kali terhadap IPSG 2 dan 4,71 kali terhadap IPSG 5. Penelitian ini merekomendasikan perlunya gaya kepemimpinan konsultatif dari kepala ruangan untuk melaksanakan peran dan fungsi sebagai pengawas untuk meningkatkan kepatuhan perawat dalam penerapan IPSG 1, IPSG 2, IPSG 5, dan IPSG 6. Kata Kunci: indikator sasaran keselamatan pasien, kepatuhan, perawat

scholarly journals Factors Related with Nurse Compliance in the Implementation of Patient Safety Indicators at Hospital

2020 ◽  
Vol 23 (3) ◽  
pp. 170-183
Author(s):  
Taufik Alhidayah ◽  
Fransisca Sri Susilaningsih ◽  
Irman Somantri
Keyword(s):  
Patient Safety ◽  
Leadership Style ◽  
Sampling Technique ◽  
Head Nurse ◽  
Chi Square ◽  
Cross Sectional ◽  
Purposive Sampling ◽  
Safety Issues ◽  
Patient Safety Indicators ◽  
Hospital Safety

Patient safety is one of the five crucial hospital safety issues. This study aimed to determine factors related with nurses’ compliance in the implementation of indicators of patient safety goals (IPSG 1, IPSG 2, IPSG 5, and IPSG 6). This study was a descriptive correlative with a cross-sectional approach. Samples were recruited using a purposive sampling technique (n = 102). Data were analyzed using chi-square and Mann–Whitney tests. The results of this study indicate that the leadership style of the head nurse, rewards, attitudes, and motivation had a significant relationship with the level of adherence in the implementation of IPSG 1 and IPSG 2. The level of nurses’ compliance in the implementation of IPSG 5 was only influenced by the leadership style of the room head and the nurses’ positive attitude. None of the factors had significant relationships with the level of nurses’ compliance in the implementation of IPSG 6. The consultative leadership style of the room head can change the level of nurses’ compliance in the implementation of IPSG 1 by 5.6 times, with 5.06 times toward IPSG 2 and 4.71 times toward IPSG 5. This research recommends the need for consultative leadership style from the room head to carry out the roles and functions as a supervisor to improve associate nurses’ compliance in the implementation of IPSG 1, IPSG 2, IPSG 5, and IPSG 6. Abstrak Faktor-Faktor yang Berhubungan dengan Tingkat Kepatuhan Perawat dalam Implementasi Indikator Sasaran Keselamatan Pasien di Rumah Sakit. Keselamatan pasien adalah salah satu dari lima isu penting keselamatan di rumah sakit. Penelitian ini bertujuan untuk menentukan faktor-faktor yang terkait dengan kepatuhan perawat dalam penerapan indikator sasaran keselamatan pasien (IPSG 1, IPSG 2, IPSG 5, dan IPSG 6). Desain penelitian menggunakan deskriptif korelatif dengan pendekatan cross-sectional. Sampel diambil dengan menggunakan teknik purposive sampling (n= 102). Data dianalisis dengan menggunakan uji Chi-square dan Mann-Whitney. Hasil penelitian menunjukkan bahwa gaya kepemimpinan kepala ruangan, penghargaan, sikap, dan motivasi memiliki hubungan yang signifikan dengan tingkat kepatuhan dalam penerapan IPSG 1 dan IPSG 2. Tingkat kepatuhan perawat dalam penerapan IPSG 5 hanya dipengaruhi oleh gaya kepemimpinan kepala ruangan dan sikap positif perawat. Tidak ada faktor yang memiliki hubungan signifikan dengan tingkat kepatuhan perawat dalam penerapan IPSG 6. Gaya kepemimpinan konsultatif kepala ruangan dapat mengubah tingkat kepatuhan perawat dalam penerapan IPSG 1 sebesar 5,6 kali, dengan 5,06 kali terhadap IPSG 2 dan 4,71 kali terhadap IPSG 5. Penelitian ini merekomendasikan perlunya gaya kepemimpinan konsultatif dari kepala ruangan untuk melaksanakan peran dan fungsi sebagai pengawas untuk meningkatkan kepatuhan perawat dalam penerapan IPSG 1, IPSG 2, IPSG 5, dan IPSG 6. Kata Kunci: indikator sasaran keselamatan pasien, kepatuhan, perawat

scholarly journals ANALYSIS OF THE INFLUENCE OF MACHINE LEARNING ALGORITHM PARAMETERS ON THE RESULTS OF TRAFFIC CLASSIFICATION IN REAL TIME

T-Comm ◽  
2021 ◽  
Vol 15 (9) ◽  
pp. 24-35
Author(s):  
Irina A. Krasnova ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Traffic Classification ◽  
Data Set ◽  
Minimum Number ◽  
The Impact

The paper analyzes the impact of setting the parameters of Machine Learning algorithms on the results of traffic classification in real-time. The Random Forest and XGBoost algorithms are considered. A brief description of the work of both methods and methods for evaluating the results of classification is given. Experimental studies are conducted on a database obtained on a real network, separately for TCP and UDP flows. In order for the results of the study to be used in real time, a special feature matrix is created based on the first 15 packets of the flow. The main parameters of the Random Forest (RF) algorithm for configuration are the number of trees, the partition criterion used, the maximum number of features for constructing the partition function, the depth of the tree, and the minimum number of samples in the node and in the leaf. For XGBoost, the number of trees, the depth of the tree, the minimum number of samples in the leaf, for features, and the percentage of samples needed to build the tree are taken. Increasing the number of trees leads to an increase in accuracy to a certain value, but as shown in the article, it is important to make sure that the model is not overfitted. To combat overfitting, the remaining parameters of the trees are used. In the data set under study, by eliminating overfitting, it was possible to achieve an increase in classification accuracy for individual applications by 11-12% for Random Forest and by 12-19% for XGBoost. The results show that setting the parameters is a very important step in building a traffic classification model, because it helps to combat overfitting and significantly increases the accuracy of the algorithm’s predictions. In addition, it was shown that if the parameters are properly configured, XGBoost, which is not very popular in traffic classification works, becomes a competitive algorithm and shows better results compared to the widespread Random Forest.

scholarly journals An integrated machine learning model for indoor network optimization to maximize coverage

2021 ◽  
Vol 24 (1) ◽  
pp. 394
Author(s):  
Ahmed Wasif Reza ◽  
Abdullah Al Rifat ◽  
Tanvir Ahmed
Keyword(s):  
Machine Learning ◽  
Network Optimization ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Simple Task ◽  
Machine Learning Model ◽  
Minimum Number

Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.

scholarly journals Improving measurements of similarity judgments with machine-learning algorithms

2020 ◽  
Author(s):  
Jeffrey R Stevens ◽  
Alexis Saltzman ◽  
Tanner Rasumussen ◽  
Leen-Kiat Soh
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intertemporal Choice ◽  
Learning Algorithms ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Minimum Number ◽  
Similarity Judgments ◽  
Intertemporal Choices

Intertemporal choices involve assessing options with different reward amounts available at different time delays. The similarity approach to intertemporal choice focuses on judging how similar amounts and delays are. Yet we do not fully understand the cognitive process of how these judgments are made. Here, we use machine-learning algorithms to predict similarity judgments to (1) investigate which algorithms best predict these judgments, (2) assess which predictors are most useful in predicting participants' judgments, and (3) determine the minimum number of judgments required to accurately predict future judgments. We applied eight algorithms to similarity judgments for reward amount and time delay made by participants in two data sets. We found that neural network, random forest, and support vector machine algorithms generated the highest out-of-sample accuracy. Though neural networks and support vector machines offer little clarity in terms of a possible process for making similarity judgments, random forest algorithms generate decision trees that can mimic the cognitive computations of human judgment-making. We also found that the numerical difference between amount values or delay values was the most important predictor of these judgments, replicating previous work. Finally, the best performing algorithms such as random forest can make highly accurate predictions of judgments with relatively small sample sizes (~15), which will help minimize the numbers of judgments required to extrapolate to new value pairs. In summary, machine-learning algorithms provide both theoretical improvements to our understanding of the cognitive computations involved in similarity judgments and intertemporal choices as well as practical improvements in designing better ways of collecting data.

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