How to develop machine learning models for healthcare

Chen, Po-Hsuan Cameron; Liu, Yun; Peng, Lily

doi:10.1038/s41563-019-0345-0

Cited by 214 publications

(167 citation statements)

References 22 publications

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“…First, the validation should be appropriate for the anticipated use cases [ 5 , 67 ]. For example, in the pre-diagnosis and post-diagnosis use cases, ML-based tools need to be adequately validated using representative multi-institutional data to ensure generalization of the approaches and interoperability.…”

Section: Machine Learning Basicsmentioning

confidence: 99%

Artificial intelligence in digital breast pathology: Techniques and applications

et al. 2020

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Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

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Section: Machine Learning Basicsmentioning

confidence: 99%

Artificial intelligence in digital breast pathology: Techniques and applications

et al. 2020

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“…This is an algorithm used to measure similarity between two sequences which may vary in time or speed. It works as follows: ≥ , the time complexity can be said to be O L ( ) 1 2 . Softwares designed to evaluate this distance often implement some optimizations in the algorithms in order to contain the computation time (see e.g.…”

Section: Correlation Analysis and Dimensionality Reduction The Simplmentioning

confidence: 99%

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers

Agliari

Barra

et al. 2020

Sci Rep

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in this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sample of 2829 labelled patients; labels highlight whether a patient is suffering from cardiac pathologies. In the first part of the work we analyze statistically the heartbeat series associated to each patient and we work them out to get a coarse-grained description of heart variability in terms of 49 markers well established in the reference community. These markers are then used as inputs for a multi-layer feed-forward neural network that we train in order to make it able to classify patients. However, before training the network, preliminary operations are in order to check the effective number of markers (via principal component analysis) and to achieve data augmentation (because of the broadness of the input data). With such groundwork, we finally train the network and show that it can classify with high accuracy (at most ~85% successful identifications) patients that are healthy from those displaying atrial fibrillation or congestive heart failure. In the second part of the work, we still start from raw data and we get a classification of pathologies in terms of their related networks: patients are associated to nodes and links are drawn according to a similarity measure between the related heartbeat series. We study the emergent properties of these networks looking for features (e.g., degree, clustering, clique proliferation) able to robustly discriminate between networks built over healthy patients or over patients suffering from cardiac pathologies. We find overall very good agreement among the two paved routes. Artificial intelligence (AI) is gaining a growing role in healthcare: in the last years, several devices and advanced algorithms have been successfully employed to assist medical workers (see e.g. 1-5). Among the most important goals of this partnership between humans and machines is the wide accessibility (even in low-income and remote areas) to medical assistance and the reduction of the time needed to reach a diagnosis. Of course, in order for AI-based devices to analyze large amounts of information and make (fast and correct) decisions, they first need to undergo a suitable training 6-9. Basically, during training, a machine-learning model is exposed to examples and its internal parameters are tuned accordingly; once training is over, new data are presented to the model which then uses what it has learned to explain that data. For instance, a model meant to classify images of skin lesions as benign lesions or malignant skin cancer will be trained on a dataset of skin pictures from different patients, previously labeled as benign or malignant, through which the model learns to detect in the input image specific patterns that are hallmarks of malignancies. Clearly, the more accurate the training and the better the performance. Nowadays, an accurate training is in principle possible given that each patient generates large volumes of health data such as...

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“…Accurate disease forecasts can help medical organizations in taking countermeasures and advance preparedness of hospitals and the general population. Recently, machine learning (ML) techniques are being increasingly implemented in the analysis of healthcare data [ 1 ]. ML analysis can help combat diseases and improve medical systems by increasing their efficiency.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

Olsavszky

Dosius

Vlădescu

et al. 2020

IJERPH

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The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

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How to develop machine learning models for healthcare

Cited by 214 publications

References 22 publications

Artificial intelligence in digital breast pathology: Techniques and applications

Artificial intelligence in digital breast pathology: Techniques and applications

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

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