Predicting and mapping neighborhood-scale health outcomes: A machine learning approach

2021 ◽  
Vol 85 ◽  
pp. 101562
Author(s):  
Chen Feng ◽  
Junfeng Jiao
Keyword(s):  
Machine Learning ◽  
Health Outcomes ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Neighborhood Scale

scholarly journals Machine-learning analysis identifies digital behavioral phenotypes for engagement and health outcome efficacy of mHealth interventions for obesity: post-hoc analyses of a randomized trial (Preprint)

2021 ◽  
Author(s):  
Meelim Kim ◽  
Jaeyeong Yang ◽  
Woo-Young Ahn ◽  
Hyung Jin Choi
Keyword(s):  
Machine Learning ◽  
Cognitive Behavioral Therapy ◽  
Health Outcomes ◽  
Clinical Outcomes ◽  
Weight Change ◽  
Behavioral Therapy ◽  
Cognitive Behavioral ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Learning Analysis

BACKGROUND The digital healthcare community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE This study aimed to investigate the performance of multivariate phenotypes predicting the engagement rate and health outcomes of digital cognitive behavioral therapy (dCBT) using a machine learning approach. METHODS We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy (dCBT) for eight weeks. To discriminate the important characteristics, we conducted a machine-learning analysis. RESULTS A higher engagement rate was associated with higher weight loss at 8 weeks (r = -0.59, p < 0001) and 24 weeks (r = -0.52, p = 0001). The machine learning approach revealed distinct multivariate profiles associated with varying impacts on the outcomes. Lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, eight types of digital phenotypes predicted engagement rates (mean R2 = 0416, SD = 0006). The prediction of short-term weight change (mean R2 = 0382, SD = 0015) was associated with six different digital phenotypes. Lastly, two behavioral measures of digital phenotypes were associated with a long-term weight change (mean R2 = 0590, SD = 0011). CONCLUSIONS Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of digital intervention with the machine learning method. Our results also highlight the importance of assessing multiple aspects of motivation before and during the intervention to improve both engagement rate and clinical outcomes. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. CLINICALTRIAL ClinicalTrials.gov NCT03465306 (Retrieved September 18, 2017, https://register.clinicaltrials.gov/NCT03465306)

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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