Special Issue "Machine Learning in Healthcare"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Machine Learning".

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Dr. Renato Umeton
E-Mail Website
Guest Editor
Dana-Farber Cancer Institute, Boston, MA, USA
Dr. Gregory Antell
E-Mail Website
Guest Editor
Dana-Farber Cancer Institute, Boston, MA, USA

Special Issue Information

Dear Colleagues,

Research and development in machine learning (ML), and particular sub-fields such as deep learning (DL) and natural language processing (NLP), have enjoyed tremendous advances in performance over the past decade, thanks in part to hardware improvements such as GPUs and troves of labeled data available for benchmark. This general trend applies to healthcare and clinical care as well, where ML efforts in the field have been catalyzed by the volume and variety of data generated by maturing technologies such as electronic health record systems; high-throughput “-omics” methods; and the expanded usage of medical, wearable, and mobile devices. The application of these methods can range from hypothesis generation to decision support to fully automated AI systems, each associated with a unique set of requirements and challenges that must be met in order to meaningfully operationalize ML in healthcare.

This Special Issue addresses two major and interrelated themes. The first is focused upon machine learning research in healthcare, with special emphasis on advances in computer vision, sequence models and transformers, and generalized ML and DL methodologies that apply to non-image and non-temporal data. The second is focused upon challenges of ML operationalization from both clinical and engineering perspectives. In the latter, we welcome submissions related to ML in production, explainable AI, ML monitoring, and integration with clinical workflows.

Our main goal is to stimulate discussion between ML researchers, technologists, and healthcare domain experts, across academia and industry, to meet the common goal of delivering reliable, fair, and high-performance ML applications in healthcare.

Dr. Renato Umeton
Dr. Gregory Antell
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • computer vision
  • natural language processing
  • clinical NLP
  • clinical decision support
  • machine learning operations
  • explainable AI
  • machine learning monitoring

Published Papers (3 papers)

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Research

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Article
Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN
Informatics 2021, 8(2), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8020040 - 09 Jun 2021
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Abstract
The accurate segmentation and identification of vertebrae presents the foundations for spine analysis including fractures, malfunctions and other visual insights. The large-scale vertebrae segmentation challenge (VerSe), organized as a competition at the Medical Image Computing and Computer Assisted Intervention (MICCAI), is aimed at [...] Read more.
The accurate segmentation and identification of vertebrae presents the foundations for spine analysis including fractures, malfunctions and other visual insights. The large-scale vertebrae segmentation challenge (VerSe), organized as a competition at the Medical Image Computing and Computer Assisted Intervention (MICCAI), is aimed at vertebrae segmentation and labeling. In this paper, we propose a framework that addresses the tasks of vertebrae segmentation and identification by exploiting both deep learning and classical machine learning methodologies. The proposed solution comprises two phases: a binary fully automated segmentation of the whole spine, which exploits a 3D convolutional neural network, and a semi-automated procedure that allows locating vertebrae centroids using traditional machine learning algorithms. Unlike other approaches, the proposed method comes with the added advantage of no requirement for single vertebrae-level annotations to be trained. A dataset of 214 CT scans has been extracted from VerSe’20 challenge data, for training, validating and testing the proposed approach. In addition, to evaluate the robustness of the segmentation and labeling algorithms, 12 CT scans from subjects affected by severe, moderate and mild scoliosis have been collected from a local medical clinic. On the designated test set from Verse’20 data, the binary spine segmentation stage allowed to obtain a binary Dice coefficient of 89.17%, whilst the vertebrae identification one reached an average multi-class Dice coefficient of 90.09%. In order to ensure the reproducibility of the algorithms hereby developed, the code has been made publicly available. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare)
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Article
Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis
Informatics 2021, 8(2), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8020027 - 15 Apr 2021
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Abstract
Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that [...] Read more.
Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare)
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Review

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Review
Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
Informatics 2021, 8(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8010016 - 03 Mar 2021
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Abstract
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making [...] Read more.
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare)
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