scholarly journals An interpretable machine learning model for diagnosis of Alzheimer's disease

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6543 ◽  
Author(s):  
Diptesh Das ◽  
Junichi Ito ◽  
Tadashi Kadowaki ◽  
Koji Tsuda
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medical Diagnosis ◽  
Cost Effective ◽  
Learning Model ◽  
Patient Specific ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Effective Diagnosis

We present an interpretable machine learning model for medical diagnosis called sparse high-order interaction model with rejection option (SHIMR). A decision tree explains to a patient the diagnosis with a long rule (i.e., conjunction of many intervals), while SHIMR employs a weighted sum of short rules. Using proteomics data of 151 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, SHIMR is shown to be as accurate as other non-interpretable methods (Sensitivity, SN = 0.84 ± 0.1, Specificity, SP = 0.69 ± 0.15 and Area Under the Curve, AUC = 0.86 ± 0.09). For clinical usage, SHIMR has a function to abstain from making any diagnosis when it is not confident enough, so that a medical doctor can choose more accurate but invasive and/or more costly pathologies. The incorporation of a rejection option complements SHIMR in designing a multistage cost-effective diagnosis framework. Using a baseline concentration of cerebrospinal fluid (CSF) and plasma proteins from a common cohort of 141 subjects, SHIMR is shown to be effective in designing a patient-specific cost-effective Alzheimer’s disease (AD) pathology. Thus, interpretability, reliability and having the potential to design a patient-specific multistage cost-effective diagnosis framework can make SHIMR serve as an indispensable tool in the era of precision medicine that can cater to the demand of both doctors and patients, and reduce the overwhelming financial burden of medical diagnosis.

Random Forest Machine Learning Model for Predicting Combustion Feedback Information of a Natural Gas Spark Ignition Engine

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Jinlong Liu ◽  
Christopher Ulishney ◽  
Cosmin Emil Dumitrescu
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Gas ◽  
Engine Performance ◽  
Cost Effective ◽  
Learning Model ◽  
Operating Conditions ◽  
Control Variables ◽  
Feedback Information ◽  
Machine Learning Model

Abstract Engine calibration requires detailed feedback information that can reflect the combustion process as the optimized objective. Indicated mean effective pressure (IMEP) is such an indicator describing an engine’s capacity to do work under different combinations of control variables. In this context, it is of interest to find cost-effective solutions that will reduce the number of experimental tests. This paper proposes a random forest machine learning model as a cost-effective tool for optimizing engine performance. Specifically, the model estimated IMEP for a natural gas spark ignited engine obtained from a converted diesel engine. The goal was to develop an economical and robust tool that can help reduce the large number of experiments usually required throughout the design and development of internal combustion engines. The data used for building such correlative model came from engine experiments that varied the spark advance, fuel-air ratio, and engine speed. The inlet conditions and the coolant/oil temperature were maintained constant. As a result, the model inputs were the key engine operation variables that affect engine performance. The trained model was shown to be able to predict the combustion-related feedback information with good accuracy (R2 ≈ 0.9 and MSE ≈ 0). In addition, the model accurately reproduced the effect of control variables on IMEP, which would help narrow the choice of operating conditions for future designs of experiment. Overall, the machine learning approach presented here can provide new chances for cost-efficient engine analysis and diagnostics work.

scholarly journals AN INTERPRETABLE MACHINE LEARNING MODEL FOR INDIVIDUALIZED PROTOCOL SELECTION AND GONADOTROPIN DOSE SELECTION DURING OVARIAN STIMULATION

2021 ◽  
Vol 116 (3) ◽  
pp. e174
Author(s):  
Kevin E. Loewke ◽  
Veronica I. Nutting ◽  
Justina Hyunjii Cho ◽  
David I. Hoffman ◽  
Louis N. Weckstein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Stimulation ◽  
Learning Model ◽  
Dose Selection ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Protocol Selection

scholarly journals A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Natasha L. Patel-Murray ◽  
Miriam Adam ◽  
Nhan Huynh ◽  
Brook T. Wassie ◽  
Pamela Milani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Learning Model ◽  
Modes Of Action ◽  
Interpretable Machine Learning ◽  
Machine Learning Model

scholarly journals Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Charles K. Fisher ◽  
◽  
Aaron M. Smith ◽  
Jonathan R. Walsh ◽  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Synthetic Data ◽  
Patient Characteristics ◽  
Patient Data ◽  
Word Recall ◽  
Boltzmann Machine ◽  
Machine Learning Model

Abstract Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.

Accelerated Reactivity Mechanism and Interpretable Machine Learning Model of N-Sulfonylimines toward Fast Multicomponent Reactions

2020 ◽  
Vol 22 (21) ◽  
pp. 8480-8486
Author(s):  
Krupal P. Jethava ◽  
Jonathan Fine ◽  
Yingqi Chen ◽  
Ahad Hossain ◽  
Gaurav Chopra
Keyword(s):  
Machine Learning ◽  
Multicomponent Reactions ◽  
Learning Model ◽  
Interpretable Machine Learning ◽  
Machine Learning Model

scholarly journals Interpretable Machine Learning Model for Mortality Prediction in ICU: A Multicenter Study

2020 ◽  
Author(s):  
Ka Man Fong ◽  
Shek Yin Au ◽  
George Wing Yiu Ng ◽  
Anne Kit Hung Leung
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Severity Score ◽  
Learning Model ◽  
Mortality Prediction ◽  
Large Dataset ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Precision Recall Curve ◽  
Apache Iv

Abstract Background: Researchers have long been struggling to improve the disease severity score in mortality prediction in ICU. The digitalization of medical health records and advancement of computation power have promoted the use of machine learning in critical care. This study aimed to develop an interpretable machine learning model using datasets from multicenters, and to compare with the APACHE IV, in predicting hospital mortality of patients admitted to ICU.Method: The datasets were assembled from the eICU database including 136145 patients across 208 hospitals throughout the U.S. and 5 ICUs in Hong Kong, including 10909 patients. The two datasets were first combined into one large dataset before 80:20 stratified split into the training set and the test set. The XGBoost machine algorithm was chosen to predict the hospital mortality. The variables in the model were the same as those included in the APACHE IV score. The discrimination and calibration of the model were assessed. The model would be interpreted using the Shapley Additive explanations values.Results: Of the 147054 patients in the whole cohort, the hospital mortality was 9.3%. The area under the precision-recall curve for the XGBoost algorithm was 0.57, and 0.49 for APACHE IV. Similarly, the XGBoost reached an area under the receiving operating curve (AUROC) of 0.90, while APACHE IV had an AUROC of 0.87. Additionally, the XGBoost algorithm showed better calibration than the APACHE IV. The three most important variables were age, heart rate, and whether the patient was on ventilator.Conclusions: The severity score developed by machine learning model using mutlicenter datasets outperformed the APACHE IV in predicting hospital mortality for patients admitted to ICU.

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