Biomedical Informatics for Computer‐Aided Decision Support Systems: A Survey

Belle, Ashwin; Kon, Mark A.; Najarian, Kayvan

doi:10.1155/2013/769639

Cited by 55 publications

(32 citation statements)

References 46 publications

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“…Success in this area could help to enable personalized healthcare or precision medicine [350,351]. Earlier, we reviewed approaches for patient categorization.…”

Section: The Impact Of Deep Learning In Treating Disease and Developimentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2017

Preprint

236

248

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Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes, and treatment of patients—and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

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“…Success in this area could help to enable personalized healthcare or precision medicine [350,351]. Earlier, we reviewed approaches for patient categorization.…”

Section: The Impact Of Deep Learning In Treating Disease and Developimentioning

confidence: 99%

Opportunities and obstacles for deep learning in biology and medicine

Ching

Himmelstein

Beaulieu‐Jones

et al. 2017

Preprint

236

248

View full text Add to dashboard Cite

show abstract

“…Indeed, numerous entropy-based metrics have recently provided a significant ability to reveal useful information from diseases that still represent a clinical challenge, such as Alzheimer's [1], schizophrenia [2], myocardial infarction [3] or atrial fibrillation (AF) [4], among others. The information provided by these metrics is mainly related to underlying mechanisms that cannot be quantified directly by clinicians in an exploratory examination, thus providing a significant knowledge increase of those diseases, as well as improving their diagnosis and treatment [5,6]. Within this context, wavelet entropy (WE) has demonstrated very interesting results because it combines entropy and wavelet decomposition to increase its robustness to non-stationarities, noise and artifacts [7].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Rodenas

García

Alcaraz

et al. 2015

Entropy

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This work introduces for the first time the application of wavelet entropy (WE) to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF), automatically from the electrocardiogram (ECG). Given that AF is often asymptomatic and usually presents very brief initial episodes, its early automatic detection is clinically relevant to improve AF treatment and prevent risks for the patients. After discarding noisy TQ intervals from the ECG, the WE has been computed over the median TQ segment obtained from the 10 previous noise-free beats under study. In this way, the P-waves or the fibrillatory waves present in the recording were highlighted or attenuated, respectively, thus enabling the patient's rhythm identification (sinus rhythm or AF). Results provided a discriminant ability of about 95%, which is comparable to previous works. However, in contrast to most of them, which are mainly based on quantifying RR series variability, the proposed algorithm is able to deal with patients under rate-control therapy or with a reduced heart rate variability during AF. Additionally, it also presents interesting properties, such as the lowest delay in detecting AF or sinus rhythm, the ability to detect episodes as brief as five beats in length or its integration facilities under real-time beat-by-beat ECG monitoring systems. Consequently, this tool may help clinicians in the automatic detection of a wide variety of AF episodes, thus gaining further knowledge about the mechanisms initiating this arrhythmia.

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“…These amounts of data make it difficult for health care professionals or patients to provide a timely treatment decision [84]. CDSS support the medical decision making process in diagnostics, therapeutics and prognostics in main medical disciplines [74].…”

Section: A Glimpse Into Machine Learning Methods For Health Carementioning

confidence: 99%

Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

Donsa

Spat

Beck

et al. 2015

Lecture Notes in Computer Science

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Abstract. Diabetes mellitus (DM) is a growing global disease which highly affects the individual patient and represents a global health burden with financial impact on national health care systems. Type 1 DM can only be treated with insulin, whereas for patients with type 2 DM a wide range of therapeutic options are available. These options include lifestyle changes such as change of diet and an increase of physical activity, but also administration of oral or injectable antidiabetic drugs. The diabetes therapy, especially with insulin, is complex. Therapy decisions include various medical and life-style related information. Computerized decision support systems (CDSS) aim to improve the treatment process in patient's self-management but also in institutional care. Therefore, the personalization of the patient's diabetes treatment is possible at different levels. It can provide medication support and therapy control, which aid to correctly estimate the personal medication requirements and improves the adherence to therapy goals. It also supports long-term disease management, aiming to develop a personalization of care according to the patient's risk stratification. Personalization of therapy is also facilitated by using new therapy aids like food and activity recognition systems, lifestyle support tools and pattern recognition for insulin therapy optimization. In this work we cover relevant parameters to personalize diabetes therapy, how CDSS can support the therapy process and the role of machine learning in this context. Moreover, we identify open problems and challenges for the personalization of diabetes therapy with focus on decision support systems and machine learning technology.

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Biomedical Informatics for Computer‐Aided Decision Support Systems: A Survey

Cited by 55 publications

References 46 publications

Opportunities and obstacles for deep learning in biology and medicine

Opportunities and obstacles for deep learning in biology and medicine

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning: Some Open Problems and Challenges

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