scholarly journals Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

2021 ◽  
Author(s):  
Laura-Jayne Gardiner ◽  
Anna Paola Carrieri ◽  
Karen Bingham ◽  
Graeme Macluskie ◽  
David Bunton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Human Tissue ◽  
Drug Response ◽  
Drug Efficacy ◽  
Mitogen Activated Protein Kinase ◽  
Patient Specific ◽  
Genomic Features ◽  
Inflammatory Bowel ◽  
Omic Data

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn's disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from a complex interaction between the host and environmental factors. Investigating drug efficacy in cultured human fresh IBD tissues can improve our understanding of the reasons why certain medications are more or less effective for different patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to informatively integrate multi-modal data to predict inter-patient specific variation in drug response. Using explanation of our models, we interpret the models' predictions inferring unique combinations of important features associated with human tissue pharmacological responses. The inferred multi-modal features originate from multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data and all are associated with drug efficacy generated by a preclinical human fresh IBD tissue assay. To pharmacologically assess patient variation in response to IBD treatment, we used the reduction in the release of the inflammatory cytokine TNFa; from the fresh IBD tissues in the presence or absence of test drugs, as a measure of drug efficacy. The TNF pathway is a common target in current therapies for IBD; we initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor on the production of TNFa; however, we later show the approach can be applied to other targets, test drugs or mechanisms of interest. Our best model was able to predict TNFa; levels from a combination of integrated demographic, medicinal and genomic features with an error as low as 4.98% on unseen patients. We incorporated transcriptomic data to validate and expand insights from genomic features. Our results showed variations in drug effectiveness between patients that differed in gender, age or condition and linked new genetic polymorphisms in our cohort of IBD patients to variation in response to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models drug response in a relevant human tissue model of IBD while also identifying its most predictive features as part of a transparent ML-based precision medicine strategy.

scholarly journals Results of the Seventh Scientific Workshop of ECCO: Precision medicine in IBD - disease outcome and response to therapy

2021 ◽  
Author(s):  
Bram Verstockt ◽  
Nurulamin M Noor ◽  
Urko M Marigorta ◽  
Polychronis Pavlidis ◽  
Parakkal Deepak ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Response To Therapy ◽  
Treatment Algorithms ◽  
New Era ◽  
Extreme Phenotypes ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Omic Data Integration ◽  
Omic Data ◽  
Prognostic And Predictive Markers

Abstract Inflammatory bowel diseases [IBD] are a heterogeneous spectrum with two extreme phenotypes, Crohn’s disease [CD] and ulcerative colitis [UC], which both represent numerous phenotypical variations. Hence, we should no longer approach all IBD patients similarly, but rather aim to rethink clinical classifications and modify treatment algorithms to usher in a new era of precision medicine in IBD. This scientific ECCO workshop aims to provide a state-of-the-art overview on prognostic and predictive markers, shed light on key questions in biomarker development, propose best practices in IBD biomarker development [including trial design], and discuss the potential for multi-omic data integration to help drive further advances to make precision medicine a reality in IBD.

scholarly journals Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sepehr Golriz Khatami ◽  
Sarah Mubeen ◽  
Vinay Srinivas Bharadhwaj ◽  
Alpha Tom Kodamullil ◽  
Martin Hofmann-Apitius ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Clinical Decision Making ◽  
Biological Process ◽  
Drug Repurposing ◽  
Clinical Decision ◽  
Patient Specific ◽  
Drug Candidates ◽  
Scoring Algorithm ◽  
Specific Pathway

AbstractThe utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

scholarly journals Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Zeeshan Ahmed ◽  
Khalid Mohamed ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Precision Medicine ◽  
Real Time ◽  
Clinical Decision Making ◽  
Clinical Laboratory ◽  
Social Issues ◽  
Patient Specific ◽  
Learning Platform

Abstract Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

scholarly journals Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

2020 ◽  
Author(s):  
Sepehr Golriz Khatami ◽  
Sarah Mubeen ◽  
Vinay Srinivas Bharadhwaj ◽  
Alpha Tom Kodamullil ◽  
Martin Hofmann-Apitius ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Clinical Decision Making ◽  
Biological Process ◽  
Drug Repurposing ◽  
Clinical Decision ◽  
Patient Specific ◽  
Drug Candidates ◽  
Scoring Algorithm ◽  
Specific Pathway

AbstractThe utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and a novel scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for three different cancers. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

Towards Realizing the Vision of Precision Medicine: AI Based Prediction of Clinical Drug Response

2020 ◽  
Author(s):  
Johann de Jong ◽  
Ioana Cutcutache ◽  
Matthew Page ◽  
Sami Elmoufti ◽  
Cynthia Dilley ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Drug Response ◽  
Clinical Drug

Patient-specific IMRT QA verification using machine learning and gamma radiomics

2021 ◽  
Vol 82 ◽  
pp. 100-108
Author(s):  
Jéssica Caroline Lizar ◽  
Carolina Cariolatto Yaly ◽  
Alexandre Colello Bruno ◽  
Gustavo Arruda Viani ◽  
Juliana Fernandes Pavoni
Keyword(s):  
Machine Learning ◽  
Patient Specific ◽  
Imrt Qa

Precision medicine-based machine learning analyses to explore optimal exercise therapies for individuals with knee osteoarthritis

2021 ◽  
Vol 29 ◽  
pp. S397-S398
Author(s):  
S. Kim ◽  
M.R. Kosorok ◽  
L. Arbeeva ◽  
T. Schwartz ◽  
Y.M. Golightly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knee Osteoarthritis ◽  
Precision Medicine

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

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