Machine learning systems and precision medicine: A conceptual and experimental approach to single individual statistics

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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

Osteoarthritis and Cartilage ◽

10.1016/j.joca.2021.02.516 ◽

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

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Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽

10.1038/s41586-021-03583-3 ◽

2021 ◽

Author(s):

Stefanie Warnat-Herresthal ◽

◽

Hartmut Schultze ◽

Krishnaprasad Lingadahalli Shastry ◽

Sathyanarayanan Manamohan ◽

...

Keyword(s):

Machine Learning ◽

Precision Medicine ◽

Distributed Data ◽

X Ray ◽

Uniform Distributions ◽

Privacy Laws ◽

Data Owner ◽

Machine Learning Approach ◽

Chest X Ray ◽

Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽

10.3390/s21072514 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2514

Author(s):

Tharindu Kaluarachchi ◽

Andrew Reis ◽

Suranga Nanayakkara

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Review Paper ◽

Learning Systems ◽

Learning Approaches ◽

Application Development ◽

Research Gaps ◽

Domain Experts ◽

Working Definition ◽

Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

Journal of Materials Chemistry A ◽

10.1039/d1ta04441a ◽

2021 ◽

Author(s):

Siwei Song ◽

Fang Chen ◽

Yi Wang ◽

Kangcai Wang ◽

Mi Yan ◽

...

Keyword(s):

Machine Learning ◽

Virtual Screening ◽

High Throughput ◽

Experimental Approach ◽

Chemical Data ◽

Research Paradigm ◽

New Materials ◽

High Throughput Virtual Screening

With the growth of chemical data, computation power and algorithms, machine learning-assisted high-throughput virtual screening (ML-assisted HTVS) is revolutionizing the research paradigm of new materials. Herein, a combined ML-assisted HTVS...

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Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽

10.3390/e23010018 ◽

2020 ◽

Vol 23 (1) ◽

pp. 18

Author(s):

Pantelis Linardatos ◽

Vasilis Papastefanopoulos ◽

Sotiris Kotsiantis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Black Box ◽

Learning Systems ◽

Model Complexity ◽

Learning Models ◽

New Methods ◽

Industrial Adoption ◽

Machine Learning Models ◽

The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

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