Diseases of the Peripheral Nerve and Mononeuropathies
Philip B. Gorelick, Fernando D. Testai, Graeme J. Hankey, Joanna M. Wardlaw in Hankey's Clinical Neurology, 2020
Anomalies: Fibers from the median nerve in the forearm may cross to the ulnar nerve (the Martin–Gruber anastomosis) in 15–30% of the general population and a high frequency in patients with trisomy 21 (Figure 25.32).The most common variation is that fibers of the anterior interosseous nerve anastomose with the ulnar nerve to innervate muscles normally innervated by the ulnar nerve (usually first dorsal interosseous, adductor pollicis, and abductor digiti minimi).The number of axons taking the anomalous course varies.
Applying Data Mining in Smart Home
Bruno Bouchard in Smart Technologies in Healthcare, 2017
From the formula, anomalies can be detected and predictions can be made. If an event X is a probability approaching 1, then it is considered as most likely to occur. On the other hand, if its probability is close to 0, it will be considered as an unusual event and will be ignored from further predictions. The fi step is to use an enhanced version of Active LeZi (ALZ) (Gopalratnam and Cook 2003) algorithm for the prediction by adding these discovered temporal rules as input data. This predictor is sequential and employs incremental parsing and uses Markov models. It should be noted that improved ALZ could be used for anomaly detection. This could be done by using the prediction as input in an anomaly’s detection algorithm and by comparing prediction sequence with observations. Thus, if the new observation does not correspond to the expected event, an assisting sequence could be triggered. The add-on to the Active LeZi is shown below (Algorithm 4):
Learning Engineering Uses Data (Part 2): Analytics
Jim Goodell, Janet Kolodner in Learning Engineering Toolkit, 2023
Ideally, learning curves should generally look like the dotted line shown in the functions module graph on the right in Figure 6.8. It starts high and goes down in a smooth line. For the logic module learning curve (on the left of Figure 6.8), learning doesn’t seem to be occurring—students aren’t improving with this skill over time. And although the learning curve for the functions module looks better, it has some strange anomalies where the error rate goes back up, such as right before the fifth and fifteenth attempts. These anomalies show where learning isn’t taking place as expected, but they don’t tell us the causes of the anomalies. Finding the cause of the anomalies requires more investigation. It could be that learning can be improved by providing more practice opportunities for a given skill, more scaffolding in the course in the form of more or better instruction, or by breaking down the skills further to provide more practice opportunities in finer-grained skills. These are just a few of the next steps that were taken on this Discrete Math Primer.
Primary temporal bone chondrosarcoma: experience with 10 cases
Published in Acta Oto-Laryngologica, 2019
Kun Zhang, Peng Qu, Endong Zhang, Chunfu Dai, Yilai Shu, Bing Chen
This study was conducted at the ENT Department at Shanghai Eye, Ear, Nose and Throat Hospital in China. All patients who confirmed the diagnosis of TBC by pathology results from June 2009 to June 2018 were included in this study. Chondrosarcoma originating at other subsites of the cranial base such as the paranasal sinuses, temporomandibular joint, or jugular foramen were excluded, as were radiologically presumed cases without definitive histological confirmation. The patient's medical records were reviewed for demographic data, clinical presentations, diagnostic findings, histopathology, tumor stages, treatment strategies, length of clinical follow-up, disease recurrence and survival. In addition, the involvement of individual anomalies was also analyzed. The characteristics of these 10 patients are presented in Table 1.
Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing
Published in Journal of Applied Statistics, 2023
Guannan Wang, Zhiling Gu, Xinyi Li, Shan Yu, Myungjin Kim, Yueying Wang, Lei Gao, Li Wang
The entire detection and repairing procedure is illustrated in Figure 9. First of all, we obtained the data from all of the four data sources, and used the dissimilarity measure proposed in the above to compare them. We visualize and check the difference at the state level among different data sources based on the comparison results. For the county-level data, we calculate the measure and report the top 10 counties, which are the most different pairwisely. Then, all the data are processed with all types of anomaly detection discussed in Section 4.2. Once an anomaly has been detected, a warning will be given automatically by R package cdcar. We handle different types of anomalies depending on the circumstances. For example, if an order dependency violation is detected, we will repair that point using our data repairing algorithms proposed in Section 5.1. If a point anomaly is detected, we first manually check possible legitimate reasons based on news and social media. If correction is necessary, we will repair the point anomalies using the proposed algorithm, see Algorithm 1.
Causal Considerations Can Inform the Interpretation of Surprising Associations in Medical Registries
Published in Cancer Investigation, 2022
Alberto Carmona-Bayonas, Paula Jiménez-Fonseca, Javier Gallego, Pavlos Msaouel
The Bayesian approach shares the same need for appropriate specification of analyses models (24,38). Bayesian analyses can compute the probability of hypotheses as a function of the data and allow the incorporation of prior knowledge through plausible prior probability distributions (25,26,28). However, the use of strongly skeptical priors, such as spiked priors focusing on the null effect, presupposes that we have very strong prior evidence that seasonality in no way affects OS in patients with advanced gastric cancer. Taken to the extreme, such “nullism” hinders the acquisition of evidence from the data (38). Some of the most successful scientific strategies have been inspired by investigating anomalies (e.g., outliers or unexpected observations for theory), as proposed by philosophers of science, such as Popper, Kuhn, and Lakatos (66–69). Therefore, strongly skeptical priors, while they may be useful in certain contexts (3), do not substitute for the need to properly parametrize our statistical models based on plausible causal frameworks.
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