What does the mind learn? A comparison of human and machine learning representations

Spicer, Jake; Sanborn, Adam N.

doi:10.1016/j.conb.2019.02.004

Cited by 22 publications

(9 citation statements)

References 63 publications

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“…The advantage of AI is that it can deal with complex and data-rich problems stably and flexibly, which makes its application in medical bioinformatics take a qualitative leap [20,21]. In recent years, AI has shown higher accuracy in clinical prediction modeling of tumor genomics and is expected to become a promising tool for tumor diagnosis and prognosis evaluation [22,23]. Numerous research results have suggested that using artificial intelligence algorithm to construct multigene models based on RNA or protein levels has the indispensable clinical value of earlier diagnosis and prognosis estimation in cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

Zhu

Yang

et al. 2022

Wireless Communications and Mobile Computing

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With the accumulation and development of medical multimodal data as well as the breakthrough in the theory and practice of artificial neural network and deep learning algorithm, the deep integration of multimodal data and artificial intelligence based on the Internet has become an important goal of the Internet of Medical Things. The deep application of the latest technologies in the medical field, such as artificial intelligence, machine learning, multimodal data, and advanced sensors, has a profound impact on the development of medical research. Artificial intelligence can achieve low-consumption and high-efficiency screening of specific markers due to its powerful data integration and processing capabilities, and its advantages are fully demonstrated in the construction of disease-related risk prediction models. In this study, multi-type cloud data were used as research objects to explore the potential of alternative CpG sites and establish a high-quality prognosis model of cervical cancer DNA methylation big data. 14,419 strict differentially methylated CpG sites (DMCs) were identified by ChAMP methylation analysis and presented these distributions based on different genomic regions and relation to island. Further, rbsurv and Cox regression analyses were performed to construct a prognostic model integrating these four methylated CpG sites that could adequately predict the survival of patients ( AUC = 0.833 , P < 0.001 ). The low- and high-risk patient groups, divided by risk score, showed significantly different overall survival (OS) in both the training ( P < 0.001 ) and validation datasets ( P < 0.005 ). Moreover, the model has an independent predictive value for FIGO stage and age and is more suitable for predicting survival time in patients with histological type (SCC) and histologic grade (G2/G3). Finally, the model exhibited much higher predictive accuracy compared to other known models and the corresponding expression of genes. The proposed model provides a novel signature to predict the prognosis, which can serve as a useful guide for increasing the accuracy of predicting overall survival of cervical cancer patients.

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Section: Discussionmentioning

confidence: 99%

Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

Zhu

Yang

et al. 2022

Wireless Communications and Mobile Computing

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“…Other constraints, such as representational or process noise constraints, are less well-attested and their consequences less clear cut. For example, applying representational constraints require first establishing what the representations are, and the nature of cognitive representations is often controversial (Spicer & Sanborn 2019).…”

Section: Sampling As a Resource-rational Constraintmentioning

confidence: 99%

What are the appropriate axioms of rationality for reasoning under uncertainty with resource-constrained systems?

Atmanspacher¹,

Basieva²,

Busemeyer³

et al. 2020

Behav Brain Sci

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When constrained by limited resources, how do we choose axioms of rationality? The target article relies on Bayesian reasoning that encounter serious tractability problems. We propose another axiomatic foundation: quantum probability theory, which provides for less complex and more comprehensive descriptions. More generally, defining rationality in terms of axiomatic systems misses a key issue: rationality must be defined by humans facing vague information.

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“…In many cases, there is a lack of objective and effective evaluation indicators. Recent advances in computing power and big data handling has enabled some artificial intelligence applications began to outperform human intelligence in solving tasks that require complex decision-making ( 10 ). Common machine-learning algorithms include the support vector machine (SVM), random forest (RF), and decision tree (DT) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Developing and validating a multivariable machine learning model for the preoperative prediction of lateral lymph node metastasis of papillary thyroid cancer

Huang¹,

Li²,

Zhong³

et al. 2023

Gland Surg

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Background At present, preoperative diagnosis of lateral cervical lymph node metastasis (LLNM) in patients with papillary thyroid carcinoma (PTC) mostly depends on the training and expertise of ultrasound doctors. A machine-learning model for predicting LLNM accurately before PTC surgery may help to determine the scope of surgery and reduce unnecessary surgical trauma. Methods The data of patients with primary PTC who underwent thyroidectomy with lateral cervical lymph node surgery at Beijing Tongren Hospital between July 2009 and June 2021 were retrospectively analyzed. All patients had complete ultrasonic examination, clinical data, and definite pathology diagnosis of lymph nodes. LLNM was confirmed by postoperative pathology. The patients were randomly divided into a training set (155 cases) and a test set (98 cases) at a ratio of 6:4. Eleven parameters, including patient demographics, ultrasound results, and tumor-related conditions, were collected, and a prediction model was established using the support vector machine (SVM) algorithm. Several other machine-learning algorithms were also used to establish models for comparison. The accuracy, precision, recall, F1-score, sensitivity, specificity, Cohen’s kappa value, and area under the receiver operating characteristic curve (AUC) were used to evaluate model performance. Results A total of 87 males and 156 females were included in the study, aged 14–80 years. One hundred and four patients of them had LLNM and 139 did not have LLNM. The pandas Python library was used for the statistical analysis, and the Spearman coefficient was used to analyze the correlation between each parameter and the prediction index. The SVM model performed the best among all the models. Its accuracy, precision, recall, F1-score, sensitivity, specificity, Cohen’s kappa value, and AUC were 90.8%, 91.0%, 90.8%, 90.8%, 87.5%, 94.0%, 81.6%, and 91.0%, respectively. Conclusions This model can enable surgeons to improve the accuracy of ultrasonography in predicting LLNM without additional examination, thus avoiding missing positive lateral cervical lymph nodes and reducing the sequelae caused by unnecessary lateral neck dissection.

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What does the mind learn? A comparison of human and machine learning representations

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 22 publications

References 63 publications

Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

Optimizing the Prognostic Model of Cervical Cancer Based on Artificial Intelligence Algorithm and Data Mining Technology

What are the appropriate axioms of rationality for reasoning under uncertainty with resource-constrained systems?

Developing and validating a multivariable machine learning model for the preoperative prediction of lateral lymph node metastasis of papillary thyroid cancer

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