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Also theoretical training such workplace health promotion as to maintain the working as body motoric, motion patterns or the use of ergonomic capacity of health care personnel? In one study, the consumption of analgesics de- HTA. Despite frequent communication with Study personnel actively taking part in psychological the authors of the studies not all questions with regard health interventions benefited from a significantly de- to the study populations and methods could be answered creased intake of analgesics, better stress management to entire satisfaction.
After three months of intervention the study population of an oncology ward displayed more This HTA can only be considered as an overview of verified positive attitudes to cancer illness, patients, colleagues interventions for the maintenance and improvement of and themselves. Also psycho-social intervention training the employability of health care personnel. This overview of forensic health care personnel led to better attitudes therefore rather needs to be understood as a synopsis to patients, deepened their knowledge about severe than an evaluation of benefits.
Further research is neces- mental illness and showed a significant reduction of sary including larger sample sizes, sufficient study dura- burnout in the study population. After one year of inter- tion and follow-ups, a lower risk of bias while taking into vention in clinical supervision health care personnel for consideration relevant quality criteria and thus guaran- patients with dementia illness showed decreased burnout teeing a better documentation of the results in the pub- rates as well.
After a nine month study examining interventions for im- proving physical and psychological health found a sig- nificant increase in subjective health perception, physical Corresponding author: Barbara Buchberger, MPH contrast to the control group.
Six out of chemotherapy trials for breast cancer define PRO as the primary endpoint, while 98 trials report clinical endpoints survival, tumour response, progression in their primary analyses. The authors thank the department of anesthesiology at Changhua Christian Hospital for providing the IV-PCA patient data, and participating in this study. There is little research into applications of machine learning to postoperative nausea and vomiting prediction. For the methodological part information extraction from the literature is structured by the report's chapters, for the empirical part data extraction sheets were constructed. We tested the classifiers listed in Table 2 , using different groups of patient features. Anreizeffekte und Projektion bis
At the same time, the inter- University of Duisburg-Essen, Institute for Health Care vention group mentioned in comparison to the control Management and Research, Schuetzenbahn 70, group a decrease of muscle pain and highlighted a sub- Essen, Germany, Phone: The effectiveness of interventions in workplace health cluded that interventions mainly focusing on technical promotion as to maintain the working capacity of health care training are improper for the reduction of musculoskeletal personal.
Instead alternative strategies would need to be considered. The authors of the second systematic review This article is freely available from also reported that physical training and advanced training http: Preferably, multifactorial interventions Published: This is an Open Access article distributed under the terms of the Creative Commons Attribution License sizes as well as the high classified risk of bias in particular http: You with regard to missing data for concealment and with no are free: The orginal English version starts at p.
With the advance of medical science, people have gradually become aware of the importance of pain management because pain can negatively affect quality of health care and even do more harm than an illness itself when it becomes intolerable.
According to the studies, PCA patient-controlled analgesia is one of the most effective techniques for postoperative analgesia [ 1 , 2 ]. Despite the fact that IV-PCA Intravenous PCA has been widely used in hospitals for its effectiveness and safety as acute postoperative pain management, PCA also usually entails PONV post-operative nausea and vomiting that complicates recovery from surgery and decreases patient satisfaction [ 3 , 4 ].
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Most previous studies of PONV were focused on identifying the risk factors, using regression techniques or proposing probabilistic models [ 7 - 10 ]. A recent work that applied an artificial neural network to predict postoperative vomiting has been proposed [ 11 ].
In this study, we investigated patient PCA demand behaviors, and derived demand pattern attributes by clustering demand profiles for PONV prediction. In addition, we proposed to use a neighborhood-based data cleaning technique to clarify class boundary.
Lastly, we conducted a comparison of various machine learning classifiers to identify the best feature set and classifiers for PONV prediction. After excluding incomplete IV-PCA log files and patient records with missing values, we obtained patient records.
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Each subject is represented by totally 28 basic attributes divided into 5 categories: In addition to commonly studied demographic and physiological factors relevant to analgesic consumption, IV-PCA related attributes, such as the number of demands per hour, have been shown to correlate significantly with analgesic consumption prediction [ 11 , 12 ].
These findings suggest that these demand behavior-related attributes are likely to correlate with incidences of postoperative nausea and vomiting. To generate behavior pattern attributes for PONV prediction, we considered two types of pattern attributes based on time domain and frequency domain, respectively.
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For time-based behavior pattern attributes, we first characterize different IV-PCA demand behaviors in the course of time. Four different time units were used in this study: From a time-based behavior pattern we can observe the change in the number of PCA demands and the amount of analgesic consumption; however, we cannot distinguish the distributions of PCA demands in different frequencies. Therefore, we also applied Fourier transform to time-based profiles to obtain a frequency-based profile.
After the process of various IV-PCA profiles, we applied k-medoid clustering to these profiles to identify significant demand patterns among the study patients. Figure 3 shows the four patterns identified in the timebased IV-PCA dose profiles of the patients in a 12 h time period [ 13 ].
The X-axis indicates the 12 h time line.
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The Y-axis represents the PCA dose within a particular 20 min time unit. The demand profiles grouped into a cluster demonstrated similar demand behaviors, and the medoid of a cluster represented the behavior pattern for that cluster over time. By applying k-medoids to different IV-PCA demand profiles, we generated different demandpattern attributes.
We used 28 basic patient attributes, classified in 5 categories, to describe each study subject.
In addition, we derived a number of different PCA demand pattern attributes from various PCA demand profiles, based on different time units, different demand reference e. Though these attributes can characterize patient behaviors, they may also negatively interact with those 28 basic attributes.
To avoid negative interaction among the features, we selected important features according to their information gain and used only these selected features to represent each patient.
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We show the feature selection process in Figure 4. From the point of machine learning, prediction of nausea and vomiting is a classification problem in an imbalanced class domain. Conventional machinelearning algorithms are typically biased toward the majority class, and produce poor predictive accuracy for the minority class.
In addition to unequal class distribution, instances sparsely scattered in the data space make the prediction of a minority class even more difficult. We applied a neighborhood-based data cleaning approach to remove spurious data points of the majority class. The rationale behind this process is that the nearest majority class neighbors of a minority class member are likely to mislead learning algorithms.
Without them, learning algorithms can more easily recognize the minority class boundary. An expertly written resource at the units, platforms, and applied sciences utilized in the dissolution checking out of oral pharmaceutical dosage kinds, this reference presents reader-friendly chapters on at the moment applied gear, apparatus qualification, attention of the gastrointestinal body structure in attempt layout, the research and interpretation of knowledge and strategy automation -laying the basis for the construction of applicable and worthy dissolution exams in keeping with the predicted position and length of drug unencumber from the dosage shape in the gastrointestinal tract.
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