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Basic Approaches of Artificial Intelligence and Machine Learning in Thermal Image Processing
Published in U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer, Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications, 2023
U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer
This algorithm is based on the Bayes theorem which is based on the concept of conditional probability. Conditional probability is the estimation of the probability of one event, given that another one has already occurred. The Naïve Bayes is the fastest algorithm when compared to other algorithm techniques and is also relatively simple and easy to implement. This algorithm needs a small amount of training data to evaluate the necessary parameters. The Bayes algorithm is a supervised learning algorithm. It is possible to apply it in both binary and multiclass classifications. The Bayes algorithm can be mathematically represented as follows:where p(y|a) = Likelihood, p(a) = Class prior probability, P(a|y) = Posterior probability, and P(y) = Predictor probability.
Screening and Diagnostic Tests
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
This conditional probability is called the posterior probability of disease. Although it seems low – for every 1,000,000 positive Pap smears, only 1,383 represent true positive results – we have still obtained useful information. Once we are told that an individual has a positive Pap smear, the probability that she has cervical cancer increases more than 17-fold – . We are in the realm of rare events; having cervical cancer. The positive test makes that event 17.3 times more likely.
The health economics of osteoporosis and estrogen replacement therapy
Published in Barry G. Wren, Progress in the Management of the Menopause, 2020
Having decided the outcome measure to be used, it is then necessary to decide the magnitude of the treatment effect. The reliability with which this can be determined depends on the time frame over which the analysis is to occur. In analysis of effects lasting 3–5 years it is likely that data will be available from randomized trials. If analyses are directed to longer time frames, treatment effects have to be derived from epidemiological studies which have a lower validity. The data for these analyses are usually derived from meta-analyses of large numbers of studies. Furthermore, over long time frames and with agents having effects on various diseases, it is necessary to use conditional probability models to allow for interactions between disease processes.
Dynamic evaluation of postoperative survival in intrahepatic cholangiocarcinoma patients who did not undergo lymphadenectomy: a multicenter study
Published in Scandinavian Journal of Gastroenterology, 2023
Tingfeng Huang, Jie Kong, Hongzhi Liu, Zhipeng Lin, Qizhu Lin, Jianying Lou, Shuguo Zheng, Xinyu Bi, Jianming Wang, Wei Guo, Fuyu Li, Jian Wang, Yamin Zheng, Jingdong Li, Shi Cheng, Weiping Zhou, Yongyi Zeng
In recent years, conditional analysis has emerged as an effective method to assess the conditions of ICC patients. Relevant studies [9,26,27] in the field have confirmed that the risk of death in these patients will gradually decrease with survival time. In CS analyses, patients are grouped according to survival time, and different groups present different expected survival rates. Thus, a more accurate prognosis evaluation may be provided [23]. It is important to acknowledge that obtaining survival estimates for ICC patients with undissected lymph nodes is more challenging than for those with dissected lymph nodes. Therefore, in this study, we calculated patients’ conditional probability of survival using multicenter data. The conditional survival analysis showed that a longer survival time within a certain period was often associated with a higher probability of subsequent survival. This outcome is consistent with the results of previous related studies [28,29].
The risk of recurrence in surgically treated head and neck squamous cell carcinomas: a conditional probability approach
Published in Acta Oncologica, 2021
Daniele Borsetto, Mantegh Sethi, Jerry Polesel, Michele Tomasoni, Alberto Deganello, Piero Nicolai, Paolo Bossi, Cristoforo Fabbris, Gabriele Molteni, Daniele Marchioni, Margherita Tofanelli, Fiordaliso Cragnolini, Giancarlo Tirelli, Andrea Ciorba, Stefano Pelucchi, Virginia Corazzi, Pietro Canzi, Marco Benazzo, Valentina Lupato, Vittorio Giacomarra, Diego Cazzador, Luigia Bandolin, Anna Menegaldo, Giacomo Spinato, Rupert Obholzer, Jonathan Fussey, Paolo Boscolo-Rizzo
In the context of cancer treatment, conditional probability provides different information than the standard cumulative recurrence rates; in fact, the former are static estimates that rely upon patient and tumor characteristics at the time of diagnosis, and they do not fully consider that the risk of recurrence changes as the time from diagnosis elapses. Thus, the cumulative 5-year recurrence rates are useful at the time of diagnosis, rather than during follow-up. Indeed, patients frequently enquire about their chances of cancer recurrence at post-treatment surveillance appointments. For this purpose, a conditional probability approach to cancer recurrence has the advantage of allowing temporal localization of the outcome of interest using baseline characteristics together with the information that the patient has not recurred up to the point of estimation. The ideal model would include all tumor, patient and treatment characteristics known to influence prognosis; however, this approach would require a very large sample size, so for the purpose of this study, we focused on the two most important prognostic discriminators, i.e. tumor site and stage [15].
Descriptive analysis and comparison of strategic incremental rehearsal to “Business as Usual” sight-word instruction for an adult nonreader with intellectual disability
Published in Developmental Neurorehabilitation, 2018
David M. Richman, Laura Grubb, Samuel Thompson
Contingent delivery of praise and corrective feedback was programmed to follow correct and incorrect responses, respectively, in the SIR protocol. Because there was no protocol for CRI, contingency strength of consequent procedures (praise and corrective feedback) was assessed using conditional and background probability. Borrero et al.17 described how conditional and background probabilities could be compared to yield an index of contingency strength. Conditional probability is the likelihood that a consequence will follow a response (e.g., anchor), expressed as a probability. Background probability is the overall likelihood of the consequence, regardless of an anchor event. When conditional probability is high and background probability is low, the relation is considered a positive contingency (the consequence is more likely to be observed following the response than under any other condition). When conditional probability and background probability are similar, the relation is considered neutral (the consequence is no more likely to be observed following the response than under any other condition) and when conditional probability is lower than background probability, the relation is considered negative (the response lowers the likelihood of observing the consequence, relative to under other conditions).