Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction

Ljubić, Branimir; Roychoudhury, Shoumik; Cao, Xi; Pavlovski, Martin; Obradovic, Stefan; Nair, Richard; Glass, Lucas M.; Obradović, Zoran

doi:10.1016/j.cmpb.2020.105765

Cited by 43 publications

(24 citation statements)

References 25 publications

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“…Neurological conditions are among the most common medical system subject to ML model applications. [29,31,32,38,46,53,[61][62][63]66,69,70] The most frequent type of data used in these applications were imaging data. Images consist of spatially coherent pixels in a local region, meaning that pixels close to each other share similar information.…”

Section: Discussionmentioning

confidence: 99%

“…Insurance claims data was frequently used but often lacks important clinical information such as laboratory results and medications. [67,70] Many traditional and deep ML models were utilized with the goal of helping to detect COVID-19 infections, complications, or outcomes as one of the most frequent research topic in the last two years. [30,34,[43][44][45][47][48][49]51,52,56,65] The performance of predictive ML models in medicine depends on multiple factors.…”

Section: Discussionmentioning

confidence: 99%

“…RNN models capture the temporal nature of EHR, imaging and other medical data to predict diseases, complications, and outcomes. [66][67][68][69][70][71][72][73] Deep learning models produce higher accuracy but suffer from issues of interpretability and instability. [15,75] Combinations of traditional and deep learning models could address challenges of interpretability and accuracy.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

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Machine learning (ML) models for analyzing medical data are critical for both accelerating development of novel diagnostic and treatment strategies and improving the accuracy of medical care delivery. Our objective was to comprehensively review supervised ML models for diagnosis or treatment prediction. Publications indexed in PubMed were reviewed to identify articles utilizing supervised predictive ML models in medicine. Articles published between 01/01/2020–01/01/2022 were included in this review. Initially, PubMed was searched using MeSH major terms, and if more extensive search results were needed, a broader search was applied (titles/abstracts).PubMed indexed 21,268 published articles (MeSH Major topic) describing ML methods implemented in medicine. Of those, 11,726 articles were published within the last 2 years. Most of the published ML models in medicine in the last two years were different types of deep learning models (about 75%). Fifty articles were included in this review.Almost all categories of disease were subjects of ML predictions. Positive and negative factors in each of the scenarios need to be evaluated before the most optimal ML model is selected. Domain knowledge and collaborations between physicians and ML experts can improve the selection and prediction performance of ML models in medicine and facilitate implementation in clinical practice. Predictive ML models could provide recommendations to recruit suitable patients for clinical trials. Prediction ML models may contribute to development of more effective diagnostic and therapeutic choices, founded on evidence-based medicine. A broad range of methodological approaches have been taken toward this goal, and those approaches are presented here with their various advantages and disadvantages.AUTHOR SUMMARYOver the last decade, there has been rapid development of Machine learning (ML) methods to analyze Big Data in medicine. ML is aimed to make the computer learn from past experiences and make predictions by recognizing patterns in medical data. We performed a comprehensive systematic literature review of recent publications (last two years), indexed in PubMed/MEDLINE that have described either traditional or deep supervised prediction ML models in medicine. We identified 21,268 articles describing ML implementation in medicine. 11,726 articles were published in the last 2 years. We presented the number of publications describing each of the most often ML methods to show current trends in development of these models. Most of the recently published ML models in medicine were deep learning models. We found that the understanding of disease is likely to lead to more accurate prediction. An important dilemma is the selection of optimal ML models for a specific task, considering amount and type of available data. Domain knowledge and collaborations between physicians and ML experts can improve the prediction performance of ML models, which could help clinicians to select the most effective diagnostic and therapeutic choices available and decrease medical errors.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Ljubić

Pavlovski

Gillespie

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The prescient shows the precision estimated the characterization models, affectability, and explicitness esteem. Ljubic et al ( 2020 ) presented the method to diagnose Alzheimer’s disease from electronic medical record (EMR) data. The results acquired showed the accuracy by 90% on using the SCRL dataset.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

490

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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“…The amount, distribution, and reasons for inconsistencies may all introduce bias. Machine learning techniques can only be applied after careful hypothesis generation, selection of relevant input cohorts, data preprocessing, feature selection and other fine tuning [3]. To apply these techniques in clinical research, statistics, computer science, and domain knowledge are indispensable.…”

Section: Introductionmentioning

confidence: 99%

ClinFlow – An Interactive Application for Clinical Data Mining

Stoicescu

Ferreira

Tamminen

et al. 2021

Studies in Health Technology and Informatics

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Analyzing clinical data comes with many challenges. Medical expertise combined with statistical and programming knowledge must go hand-in-hand when applying data mining methods on clinical datasets. This work aims at bridging the gap between clinical expertise and computer science knowledge by providing an application for clinical data analysis with no requirement for statistical programming knowledge. Our tool allows clinical researchers to conduct data processing and visualization in an interactive environment, thus providing an assisting tool for clinical studies. The application was experimentally evaluated with an analysis of Type 1 Diabetes clinical data. The results obtained with the tool are in line with the domain literature, demonstrating the value of our application in data exploration and hypothesis testing.

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Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction

Cited by 43 publications

References 25 publications

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Systematic Review of Supervised Machine Learning Models in Prediction of Medical Conditions

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

ClinFlow – An Interactive Application for Clinical Data Mining

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