scholarly journals Assessing Instructional Modalities: Individualized Treatment Effects for Personalized Learning

2018 ◽  
Vol 26 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Joshua Beemer ◽  
Kelly Spoon ◽  
Juanjuan Fan ◽  
Jeanne Stronach ◽  
James P. Frazee ◽  
...  
Keyword(s):  
Treatment Effects ◽  
Personalized Learning ◽  
Individualized Treatment ◽  
Instructional Modalities

scholarly journals Random forests of interaction trees for estimating individualized treatment effects in randomized trials

2018 ◽  
Vol 37 (17) ◽  
pp. 2547-2560 ◽  
Author(s):  
Xiaogang Su ◽  
Annette T. Peña ◽  
Lei Liu ◽  
Richard A. Levine
Keyword(s):  
Random Forests ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individualized Treatment

Ensemble Learning for Estimating Individualized Treatment Effects in Student Success Studies

2017 ◽  
Vol 28 (3) ◽  
pp. 315-335 ◽  
Author(s):  
Joshua Beemer ◽  
Kelly Spoon ◽  
Lingjun He ◽  
Juanjuan Fan ◽  
Richard A. Levine
Keyword(s):  
Student Success ◽  
Ensemble Learning ◽  
Treatment Effects ◽  
Individualized Treatment

scholarly journals Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

2020 ◽  
Vol 11 ◽  
Author(s):  
Qiyang Ge ◽  
Xuelin Huang ◽  
Shenying Fang ◽  
Shicheng Guo ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Treatment Response ◽  
Treatment Effect ◽  
Treatment Effects ◽  
State Of The Art ◽  
Treatment Selection ◽  
Individualized Treatment ◽  
Generative Adversarial Networks ◽  
Precision Oncology ◽  
Adversarial Networks

Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. One AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) has been developed. However, GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that MCGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), k-Nearest Neighbor (KNN), random forest classification [RF (C)], random forest regression [RF (R)], logistic regression (LogR), and support vector machine (SVM). To illustrate their applications, the proposed MCGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.

scholarly journals Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

2020 ◽  
Author(s):  
Qiyang Ge ◽  
Xuelin Huang ◽  
Shenying Fang ◽  
Shihcheng Guo ◽  
yuanyuan Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Treatment Response ◽  
Treatment Effect ◽  
Treatment Effects ◽  
State Of The Art ◽  
Treatment Selection ◽  
Individualized Treatment ◽  
Generative Adversarial Networks ◽  
Precision Oncology ◽  
Adversarial Networks

Treatment response is heterogeneous. However the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. The artificial intelligence (AI) is a powerful tool for precision oncology. It can accurately estimate the individualized treatment effects and learn optimal treatment choices. Therefore, the AI approach can substantially improve progress and treatment outcomes of patients. As one of AI approach, conditional generative adversarial nets for inference of individualized treatment effects (GANITE) have been developed. However, the GANITE can only deal with binary treatment and does not provide a tool for optimal treatment selection. To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and continuous treatments. We propose to use sparse techniques for selection of biomarkers that predict the best treatment for each patient. Simulations show that the CGANs outperform seven other state-of-the-art methods: linear regression (LR), Bayesian linear ridge regression (BLR), KNN, random forest classification (RF (C)), random forest regression (RF (R)), logistic regression (LogR) and support vector machine (SVM). To illustrate their applications, the proposed CGANs were applied to 256 patients with newly diagnosed acute myeloid leukemia (AML) who were treated with high dose ara-C (HDAC), Idarubicin (IDA) and both of these two treatments (HDAC+IDA) at M. D. Anderson Cancer Center. Our results showed that the MCGAN can more accurately and robustly estimate the individualized treatment effects than other state-of-the art methods. Several biomarkers such as GSK3, BILIRUBIN, SMAC are identified and a total of 30 biomarkers can explain 36.8% of treatment effect variation.

A Phonomotor Approach to Apraxia of Speech Treatment

2020 ◽  
Vol 29 (4) ◽  
pp. 2109-2130
Author(s):  
Lauren Bislick
Keyword(s):  
Phase I ◽  
Treatment Effects ◽  
Single Case ◽  
Motor Planning ◽  
The Other ◽  
Multimodal Approach ◽  
Apraxia Of Speech ◽  
Duration Of Treatment ◽  
Treatment Gains ◽  
Future Work

Purpose This study continued Phase I investigation of a modified Phonomotor Treatment (PMT) Program on motor planning in two individuals with apraxia of speech (AOS) and aphasia and, with support from prior work, refined Phase I methodology for treatment intensity and duration, a measure of communicative participation, and the use of effect size benchmarks specific to AOS. Method A single-case experimental design with multiple baselines across behaviors and participants was used to examine acquisition, generalization, and maintenance of treatment effects 8–10 weeks posttreatment. Treatment was distributed 3 days a week, and duration of treatment was specific to each participant (criterion based). Experimental stimuli consisted of target sounds or clusters embedded nonwords and real words, specific to each participants' deficit. Results Findings show improved repetition accuracy for targets in trained nonwords, generalization to targets in untrained nonwords and real words, and maintenance of treatment effects at 10 weeks posttreatment for one participant and more variable outcomes for the other participant. Conclusions Results indicate that a modified version of PMT can promote generalization and maintenance of treatment gains for trained speech targets via a multimodal approach emphasizing repeated exposure and practice. While these results are promising, the frequent co-occurrence of AOS and aphasia warrants a treatment that addresses both motor planning and linguistic deficits. Thus, the application of traditional PMT with participant-specific modifications for AOS embedded into the treatment program may be a more effective approach. Future work will continue to examine and maximize improvements in motor planning, while also treating anomia in aphasia.

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