scholarly journals Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning

2022 ◽  
Vol 12 (1) ◽  
pp. 87
Author(s):  
Sang Ho Oh ◽  
Su Jin Lee ◽  
Jongyoul Park
Keyword(s):  
Reinforcement Learning ◽  
Precision Medicine ◽  
Computational Models ◽  
Recommendation System ◽  
Machine Learning Techniques ◽  
Treatment Recommendation ◽  
Patient Specific ◽  
Antihypertensive Medications ◽  
Medical Diagnoses ◽  
Health And Disease

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications.

scholarly journals A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

2022 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Folasade Isinkaye
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Plant Disease ◽  
Recommendation System ◽  
Research Effort ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Treatment Recommendation ◽  
Disease Detection ◽  
Recommended Treatment

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

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.

scholarly journals Immunotherapy as a Precision Medicine Tool for the Treatment of Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 173
Author(s):  
Maria Adamaki ◽  
Vassilios Zoumpourlis
Keyword(s):  
Prostate Cancer ◽  
Precision Medicine ◽  
Checkpoint Inhibitors ◽  
Invasive Disease ◽  
Patient Specific ◽  
Treatment Alternative ◽  
Substantial Evidence ◽  
Significant Survival ◽  
Localized Disease ◽  
Specific Antigens

Prostate cancer (PCa) is the most frequently diagnosed type of cancer among Caucasian males over the age of 60 and is characterized by remarkable heterogeneity and clinical behavior, ranging from decades of indolence to highly lethal disease. Despite the significant progress in PCa systemic therapy, therapeutic response is usually transient, and invasive disease is associated with high mortality rates. Immunotherapy has emerged as an efficacious and non-toxic treatment alternative that perfectly fits the rationale of precision medicine, as it aims to treat patients on the basis of patient-specific, immune-targeted molecular traits, so as to achieve the maximum clinical benefit. Antibodies acting as immune checkpoint inhibitors and vaccines entailing tumor-specific antigens seem to be the most promising immunotherapeutic strategies in offering a significant survival advantage. Even though patients with localized disease and favorable prognostic characteristics seem to be the ones that markedly benefit from such interventions, there is substantial evidence to suggest that the survival benefit may also be extended to patients with more advanced disease. The identification of biomarkers that can be immunologically targeted in patients with disease progression is potentially amenable in this process and in achieving significant advances in the decision for precision treatment of PCa.

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 898
Author(s):  
Marta Saiz-Vivó ◽  
Adrián Colomer ◽  
Carles Fonfría ◽  
Luis Martí-Bonmatí ◽  
Valery Naranjo
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Patient Specific ◽  
Mr Images ◽  
Training Samples ◽  
Volumetric Images ◽  
Acquisition Costs ◽  
Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

Use of Circulating Cell-Free DNA to Guide Precision Medicine in Patients with Colorectal Cancer

2021 ◽  
Vol 72 (1) ◽  
pp. 399-413
Author(s):  
Van K. Morris ◽  
John H. Strickler
Keyword(s):  
Colorectal Cancer ◽  
Precision Medicine ◽  
Residual Disease ◽  
Cost Effective ◽  
Patient Specific ◽  
Blood Draw ◽  
Cell Free Dna ◽  
Free Dna ◽  
Metastatic Setting ◽  
Diagnostic Technology

Patient-specific biomarkers form the foundation of precision medicine strategies. To realize the promise of precision medicine in patients with colorectal cancer (CRC), access to cost-effective, convenient, and safe assays is critical. Improvements in diagnostic technology have enabled ultrasensitive and specific assays to identify cell-free DNA (cfDNA) from a routine blood draw. Clinicians are already employing these minimally invasive assays to identify drivers of therapeutic resistance and measure genomic heterogeneity, particularly when tumor tissue is difficult to access or serial sampling is necessary. As cfDNA diagnostic technology continues to improve, more innovative applications are anticipated. In this review, we focus on four clinical applications for cfDNA analysis in the management of CRC: detecting minimal residual disease, monitoring treatment response in the metastatic setting, identifying drivers of treatment sensitivity and resistance, and guiding therapeutic strategies to overcome resistance.

Abstract 289: Transcriptomic Analysis of Inter- and Intra-patient Variation in Human iPSCs: Platform for Precision Medicine to Predict Drug Toxicity

2016 ◽  
Vol 119 (suppl_1) ◽  
Author(s):  
Elena Matsa ◽  
Paul W Burridge ◽  
Kun-Hsing Yu ◽  
Haodi Wu ◽  
Vittavat Termglinchan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Precision Medicine ◽  
Disease Modeling ◽  
Sendai Virus ◽  
Expression Profiles ◽  
Cardiac Differentiation ◽  
Patient Specific ◽  
Specific Gene ◽  
Drug Induced ◽  
Human Ipscs

Rapid improvements in human induced pluripotent stem cell (hiPSC) differentiation methodologies have allowed previously unattainable access to high-purity, patient-specific cardiomyocytes (CMs) for use in disease modeling, cardiac regeneration, and drug testing. In the present study, we investigate the ability of hiPSC-derived cardiomyocytes (hiPSC-CMs) to reflect the donor’s genetic identity and serve as preclinical functional readout platforms for precision medicine. We used footprint-free Sendai virus to create two separate hiPSC clones from the fibroblasts of five different individuals lacking known mutations associated with cardiovascular disease. Whole genome expression profiling of hiPSC-CMs showed that inter-patient variation was greater than intra-patient variation, thereby verifying that reprogramming and cardiac differentiation technologies can preserve patient-specific gene expression signatures. Gene ontologies (GOs) accounting for inter-patient variation were mostly metabolic or epigenetic. Toxicology analysis based on gene expression profiles predicted patient-specific susceptibility of hiPSC-CMs to cardiotoxicity, and functional assays using drugs targeting key regulators in pathways predicted to produce cardiotoxicity showed inter-patient differential responses in hiPSC-CMs. Our data suggest that hiPSC-CMs can be used in vitro to predict and help prevent patient-specific drug-induced cardiotoxicity, potentially enabling personalized patient consultation in the future.

scholarly journals Treatment adherence and blood pressure outcome among hypertensive out-patients in two tertiary hospitals in Sokoto, Northwestern Nigeria

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Rasaq Adisa ◽  
Olumide Ayodeji Ilesanmi ◽  
Titilayo Oyelola Fakeye
Keyword(s):  
Blood Pressure ◽  
Cigarette Smoking ◽  
Treatment Adherence ◽  
Pressure Control ◽  
Patient Specific ◽  
Lifestyle Modifications ◽  
Antihypertensive Medications ◽  
Chi Square ◽  
Cross Sectional ◽  
Adherence To Medication

Abstract Background Treatment adherence play important roles in blood pressure control leading to reduction in morbidity and mortality. This study therefore assessed adherence to pharmacological and non-pharmacological therapies among ambulatory hypertensive patients. Reasons for treatment non-adherence, and association between adherence and blood pressure were also investigated. Methods Cross-sectional questionnaire-guided interview and retrospective review of medical records of 605-patients from two-tertiary healthcare institutions in Sokoto, Northwestern Nigeria. Nine-item modified Morisky adherence scale was used to assess medication adherence. Overall adherence score to lifestyle modifications was obtained from the total scores from 4-domains of non-pharmacological measures including cigarette smoking and alcohol cessation, salt-restriction and exercise. Patient-specific adherence education was provided at contact to resolve the knowledge gap(s). Clinical-parameters were retrieved at contact and subsequent 2-months appointment. Descriptive statistics, Chi-square and Student’s t-test were used for analysis at p < 0.05. Results Fifty-four (8.9%) patients were adherent to medications. Forgetfulness (404; 35.2%) was the most common reason for medication non-adherence. Use of buddy/companion reminder (605, 30.2%) top the list of adherence education. Overall adherence to lifestyle modifications was 36(6.0%). Mean systolic blood pressure (SBP) at contact was 149.6 ± 22.5 versus 134.2 ± 15.8 mmHg at 2-months with a 10% reduction. There were significant associations in baseline SBP for patients with or without adherence to medication, cigarette smoking cessation, and exercise (p < 0.05). Conclusions Overall adherence to antihypertensive medications and lifestyle modifications is suboptimal, underscoring the need for continuous patient-specific adherence education to ensure better therapeutic outcomes.

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