ELIZA cgi-bash version rev. 1.91
- Medical English LInking keywords finder for the PubMed Zipped Archive (ELIZA) -
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kwic search for risk out of >500 occurrences
421954 occurrences (No.40 in the rank) during 5 years in the PubMed. [cache]
336) Model calibration examining the reliability in risk prediction was performed using either the Pearson r or the Hosmer-Lemeshow test in four studies.
* Machine learning for predicting long-term kidney allograft survival: a scoping review.
- Supervised machine learning (ML) is a class of algorithms that "learn" from existing input-output pairs, which is gaining popularity in pattern recognition for classification and prediction problems. In this scoping review, we examined the use of supervised ML algorithms for the prediction of long-term allograft survival in kidney transplant recipients. Data sources included PubMed, the Cumulative Index to Nursing and Allied Health Literature, and the Institute for Electrical and Electronics Engineers (IEEE) Xplore libraries from inception to November 2019. We screened titles and abstracts and potentially eligible full-text reports to select studies and subsequently abstracted the data. Eleven studies were identified. Decision trees were the most commonly used method (n = 8), followed by artificial neural networks (ANN) (n = 4) and Bayesian belief networks (n = 2). The area under receiver operating curve (AUC) was the most common measure of discrimination (n = 7), followed by sensitivity (n = 5) and specificity (n = 4). Model calibration examining the reliability in risk prediction was performed using either the Pearson r or the Hosmer-Lemeshow test in four studies. One study showed that logistic regression had comparable performance to ANN, while another study demonstrated that ANN performed better in terms of sensitivity, specificity, and accuracy, as compared with a Cox proportional hazards model. We synthesized the evidence related to the comparison of ML techniques with traditional statistical approaches for prediction of long-term allograft survival in patients with a kidney transplant. The methodological and reporting quality of included studies was poor. Our study also demonstrated mixed results in terms of the predictive potential of the models.
=>1.損害の恐れ, 危険, 冒険, リスク, 2.危険にさらす, 3.敢えてする, 被保険者, 被保険物, 危険にさらす,
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Overview of noun risk
The noun risk has 4 senses (first 2 from tagged texts)
1. (4) hazard, jeopardy, peril, risk, endangerment -- (a source of danger; a possibility of
incurring loss or misfortune; "drinking alcohol is a health hazard")
2. (2) risk, peril, danger -- (a venture undertaken without regard to possible loss or injury; "he
saw the rewards but not the risks of crime"; "there was a danger he would do the wrong thing")
3. risk, risk of infection -- (the probability of becoming infected given that exposure to an
infectious agent has occurred)
4. risk, risk of exposure -- (the probability of being exposed to an infectious agent)
Overview of verb risk
The verb risk has 2 senses (first 2 from tagged texts)
1. (8) risk, put on the line, lay on the line -- (expose to a chance of loss or damage; "We risked
losing a lot of money in this venture"; "Why risk your life?"; "She laid her job on the line when
she told the boss that he was wrong")
2. (2) gamble, chance, risk, hazard, take chances, adventure, run a risk, take a chance -- (take a
risk in the hope of a favorable outcome; "When you buy these stocks you are gambling")
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