Engineering Gelation Kinetics in Living Silk Hydrogels by Differential Dynamic Microscopy Microrheology and Machine Learning

Microbes embedded in hydrogels comprise one form of living material. Discovering formulations that balance potentially competing mechanical and biological properties in living hydrogels, for example gel time of the hydrogel formulation and viability of the embedded organisms, can be challenging. In this work, a pipeline is developed to automate characterization of the gel time of hydrogel formulations. Using this pipeline, living materials comprised of enzymatically crosslinked silk and embedded E. coli, formulated from within a 4D parameter space, are engineered to gel within a pre-selected timeframe. Gelation time is estimated using a novel adaptation of microrheology analysis using differential dynamic microscopy (DDM). In order to expedite the discovery of gelation regime boundaries, Bayesian machine learning models are deployed with optimal decision-making under uncertainty. The rate of learning is observed to vary between AI-assisted planning and human planning, with the fastest rate occurring during AI-assisted planning following a round of human planning. For a subset of formulations gelling within a targeted timeframe of 5-15 minutes, fluorophore production within the embedded cells is substantially similar across treatments, evidencing that gel time can be tuned independent of other material properties, at least over a finite range, while maintaining biological activity.

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Online machine learning algorithms to optimize performances of complex wireless communication systems

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022097 ◽

2021 ◽

Vol 19 (2) ◽

pp. 2056-2094

Author(s):

Koji Oshima ◽

◽

Daisuke Yamamoto ◽

Atsuhiro Yumoto ◽

Song-Ju Kim ◽

...

Keyword(s):

Machine Learning ◽

Wireless Communication ◽

Communication Systems ◽

Learning Algorithm ◽

Learning Technologies ◽

Machine Learning Algorithms ◽

Optimal Decision ◽

Wireless Communication Systems ◽

Optimal Decision Making ◽

Comprehensive Framework

<abstract><p>Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.</p></abstract>

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Reliable Machine Learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care

2019 International Conference on Communication and Electronics Systems (ICCES) ◽

10.1109/icces45898.2019.9002593 ◽

2019 ◽

Author(s):

G. Sabarmathi ◽

R. Chinnaiyan

Keyword(s):

Machine Learning ◽

Health Care ◽

Decision Making ◽

Patient Satisfaction ◽

Optimal Decision ◽

Learning Approach ◽

Quality Health Care ◽

Machine Learning Approach ◽

Quality Health ◽

Optimal Decision Making

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From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis

JMIR Medical Informatics ◽

10.2196/medinform.3445 ◽

2015 ◽

Vol 3 (1) ◽

pp. e11 ◽

Cited By ~ 31

Author(s):

Athanasios Tsoukalas ◽

Timothy Albertson ◽

Ilias Tagkopoulos

Keyword(s):

Machine Learning ◽

Decision Making ◽

Decision Support ◽

Data Driven ◽

Optimal Decision ◽

Learning Approach ◽

Machine Learning Approach ◽

Optimal Decision Making ◽

Probabilistic Machine Learning

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Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients

Methods of Information in Medicine ◽

10.1055/s-0039-1693731 ◽

2019 ◽

Vol 58 (02/03) ◽

pp. 079-085

Author(s):

Akira A. Nair ◽

Mihir Velagapudi ◽

Lakshmana Behara ◽

Ravitheja Venigandla ◽

Christine T. Fong ◽

...

Keyword(s):

Machine Learning ◽

Decision Making ◽

Point Of Care ◽

Optimal Decision ◽

Learning Models ◽

Glucose Levels ◽

Optimal Decision Making ◽

Glucose Management ◽

Log Ratio ◽

Machine Learning Models

Abstract Background Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose management plan. Objective To develop predictive models to estimate peak glucose levels in surgical patients and to implement the best performing model as a point-of-care clinical tool to assist the surgical team to optimally manage glucose levels. Methods Using a large perioperative dataset (6,579 patients) of patient- and surgery-specific parameters, we developed and validated linear regression and machine learning models (random forest, extreme gradient boosting [Xg Boost], classification and regression trees [CART], and neural network) to predict the peak glucose levels during surgery. The model performances were compared in terms of mean absolute percentage error (MAPE), logarithm of the ratio of the predicted to actual value (log ratio), median prediction error, and interquartile error range. The best performing model was implemented as part of a web-based application for optimal decision-making toward glucose management during surgery. Results Accuracy of the machine learning models were higher (MAPE = 17%, log ratio = 0.029 for Xg Boost) when compared with that of the linear regression model (MAPE = 22%, log ratio = 0.041). The Xg Boost model had the smallest median prediction error (5.4 mg/dL) and the narrowest interquartile error range (−17 to 24 mg/dL) as compared with the other models. The best performing model, Xg Boost, was implemented as a web application, Hyper-G, which the perioperative providers can use at the point of care to estimate peak glucose levels during surgery. Conclusions Machine learning models are able to accurately predict peak glucose levels during surgery. Implementation of such a model as a web-based application can facilitate optimal decision-making and advance planning of glucose management strategies.

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Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap

Symmetry ◽

10.3390/sym13030389 ◽

2021 ◽

Vol 13 (3) ◽

pp. 389

Author(s):

Sunghae Jun

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Decision Theory ◽

Bayesian Learning ◽

Bayesian Decision Theory ◽

Optimal Decision ◽

Bayesian Decision ◽

Specific Domain ◽

Human Thinking ◽

Optimal Decision Making

In the field of cognitive science, much research has been conducted on the diverse applications of artificial intelligence (AI). One important area of study is machines imitating human thinking. Although there are various approaches to development of thinking machines, we assume that human thinking is not always optimal in this paper. Sometimes, humans are driven by emotions to make decisions that are not optimal. Recently, deep learning has been dominating most machine learning tasks in AI. In the area of optimal decisions involving AI, many traditional machine learning methods are rapidly being replaced by deep learning. Therefore, because of deep learning, we can expect the faster growth of AI technology such as AlphaGo in optimal decision-making. However, humans sometimes think and act not optimally but emotionally. In this paper, we propose a method for building thinking machines imitating humans using Bayesian decision theory and learning. Bayesian statistics involves a learning process based on prior and posterior aspects. The prior represents an initial belief in a specific domain. This is updated to posterior through the likelihood of observed data. The posterior refers to the updated belief based on observations. When the observed data are newly added, the current posterior is used as a new prior for the updated posterior. Bayesian learning such as this also provides an optimal decision; thus, this is not well-suited to the modeling of thinking machines. Therefore, we study a new Bayesian approach to developing thinking machines using Bayesian decision theory. In our research, we do not use a single optimal value expected by the posterior; instead, we generate random values from the last updated posterior to be used for thinking machines that imitate human thinking.

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OPTIMAL DECISION MAKING WHEN PLANNING MOBILE COMMUNICATION NETWORKS TAKING INTO ACCOUNT A SET OF QUALITY FACTORS

Telecommunications and Radio Engineering ◽

10.1615/telecomradeng.v74.i18.40 ◽

2015 ◽

Vol 74 (18) ◽

pp. 1635-1649

Author(s):

V. M. Bezruk ◽

D. V. Chebotaryova

Keyword(s):

Decision Making ◽

Communication Networks ◽

Mobile Communication ◽

Optimal Decision ◽

Quality Factors ◽

Mobile Communication Networks ◽

Optimal Decision Making

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Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Cancer Cell International ◽

10.1186/s12935-021-01981-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Muhammad Javed Iqbal ◽

Zeeshan Javed ◽

Haleema Sadia ◽

Ijaz A. Qureshi ◽

Asma Irshad ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cancer Diagnosis ◽

Disease Risk ◽

Clinical Applications ◽

Optimal Decision ◽

Great Promise ◽

Time Range ◽

Base System ◽

The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

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