Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop?

Research output: Contribution to journalArticle

Abstract

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.
LanguageEnglish
Pages119-131
Number of pages12
JournalBrain informatics
Volume3
Issue number2
Early online date31 Mar 2016
DOIs
StatusPublished - 17 May 2016

Fingerprint

Learning systems
Health
Protein folding
Recommender systems
Reinforcement learning
Speech recognition
Computer science
Computational complexity

Keywords

  • Machine Learning
  • Health Informatics

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

Cite this

Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? / Holzinger, Andreas.

In: Brain informatics, Vol. 3, No. 2, 17.05.2016, p. 119-131.

Research output: Contribution to journalArticle

@article{79aa1ea8d80f4521a5af39b21e2ac0e8,
title = "Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop?",
abstract = "Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.",
keywords = "Machine Learning, Health Informatics, Maschinelles Lernen, Medizinische Informatik",
author = "Andreas Holzinger",
note = "http://www.springer.com/computer/ai/journal/40708",
year = "2016",
month = "5",
day = "17",
doi = "10.1007/s40708-016-0042-6",
language = "English",
volume = "3",
pages = "119--131",
journal = "Brain informatics",
issn = "2198-4018",
number = "2",

}

TY - JOUR

T1 - Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop?

AU - Holzinger,Andreas

N1 - http://www.springer.com/computer/ai/journal/40708

PY - 2016/5/17

Y1 - 2016/5/17

N2 - Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

AB - Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

KW - Machine Learning

KW - Health Informatics

KW - Maschinelles Lernen

KW - Medizinische Informatik

UR - http://rd.springer.com/journal/40708

U2 - 10.1007/s40708-016-0042-6

DO - 10.1007/s40708-016-0042-6

M3 - Article

VL - 3

SP - 119

EP - 131

JO - Brain informatics

T2 - Brain informatics

JF - Brain informatics

SN - 2198-4018

IS - 2

ER -