Development of an Abbreviated Adult Reading History Questionnaire (ARHQ-Brief) Using a Machine Learning Approach

2021 ◽  
pp. 002221942110476
Author(s):  
Luxi Feng ◽  
Roeland Hancock ◽  
Christa Watson ◽  
Rian Bogley ◽  
Zachary A. Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reading Ability ◽  
Reading Disorder ◽  
Learning Approach ◽  
Future Directions ◽  
L1 Regularization ◽  
Machine Learning Approach ◽  
Adult Reading ◽  
Reading History

Several crucial reasons exist to identify whether an adult has had reading disorder (RD) and to predict a child’s likelihood of developing RD. The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires. High ARHQ scores indicate an increased likelihood that an adult had RD as a child, and that their children may develop RD. This study focused on whether a subset of ARHQ items (ARHQ-brief) could be equally effective in assessing adults’ reading history as the full ARHQ. We used a machine learning approach, lasso (known as L1 regularization), and identified 6 of 23 items that resulted in the ARHQ-brief. Data from 97 adults and 47 children were included. With the ARHQ-brief, we report a threshold of 0.323 as suitable to identify past likelihood of RD in adults with a sensitivity of 72.4% and a specificity of 81.5%. Comparison of predictive performances between ARHQ-brief and the full ARHQ showed that ARHQ-brief explained an additional 10%–35.2% of the variance in adult and child reading. Furthermore, we validated ARHQ-brief’s superior ability to predict reading ability using an independent sample of 28 children. We close by discussing limitations and future directions.

Development of an Abbreviated Adult Reading History Questionnaire (ARHQ-Brief) Using a Machine Learning Approach

2020 ◽  
Author(s):  
Luxi Feng ◽  
Roeland Hancock ◽  
Christa Watson ◽  
Rian Bogley ◽  
Zachary Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reading Ability ◽  
Reading Skills ◽  
Reading Difficulties ◽  
Reading Disorder ◽  
Learning Approach ◽  
Future Directions ◽  
Machine Learning Approach ◽  
Adult Reading ◽  
Reading History

The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires to screen adults to assess their reading history. High ARHQ scores indicate an increased likelihood that an adult had reading difficulties as a child, and that their children may develop reading disorder (RD). Although a variety of ARHQ-revised exist, whether using a subset of ARHQ items could be equally effective and hence more efficient has yet to be determined. We created an abbreviated version of the ARHQ, tilted the ARHQ-brief, that reduced the number of items down from 23 to 6, and compared its performance with that of the full ARHQ on reading skills in adults and their children. Data from 97 adults and 51 children were included. With the ARHQ-brief, we report a threshold of 0.323 as suitable to identify past RD in adults with a sensitivity of 72.4% and a specificity of 81.5%. Comparison of predictive performances between ARHQ-brief and ARHQ showed that ARHQ-brief explained an additional 10-35.2% of the variance in adult and child reading. Further, we validated ARHQ-brief’s outperformance to predict reading ability using an independent sample of 32 children. We close by discussing limitations and future directions.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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