Clinical Decision Support Systems: A Survey of NLP-Based Approaches from Unstructured Data

Reyes‐Ortiz, José A.; González-Beltrán, Beatriz A.; Gallardo-López, Lizbeth

doi:10.1109/dexa.2015.47

Cited by 20 publications

(8 citation statements)

References 18 publications

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“…NLP provides adequate mechanisms for the automated extraction of important facts from free text, which CDSS uses to generate the results and recommendations provided to healthcare professionals to assist them in the best decision-making process. [65][66][67] The clinical notes, including patients' health history, are crucial assets for resolving sensitive clinical problems that may be difficult to access from other EHR components, like lab results. NLP makes it easy to retrieve meaningful features from clinical notes that can be used to build machine learning models.…”

Section: Nlp Applications In Healthcarementioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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Section: Nlp Applications In Healthcarementioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Applications of audio analytics can range from customer sentiment analysis in recordings taken from call centers to media content monitoring for parental control. In the domain of healthcare, several MLbased approaches have been proposed to consider clinical decisions supported using text-free data [51]. In recent years, clinical speech and audio processing have offered new opportunities, such as automatic transcription of patient conversations, synthesis of clinical notes, as well as identification of disorders related to speech [52].…”

Section: Literature Reviewmentioning

confidence: 99%

Building an Arabic Dialectal Diagnostic Dataset for Healthcare

Mounsef¹,

Hasib²,

Raza³

2022

IJACSA

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Accurate diagnosis of patient conditions becomes challenging for medical practitioners in urban metropolitan cities. A variety of languages and spoken dialects impedes the diagnosis achieved through the exploratory journey a medical practitioner and patient go through. Natural language processing has been used in well-known applications, such as Google Translate, as a solution to reduce language barriers. Languages typically encountered in these applications provide the most commonly known, used or standardized dialect. The Arabic language can benefit from the common dialect, which is available in such applications. However, given the diversity of dialects in Arabic in the healthcare domain, there is a risk associated with incorrect interpretation of a dialect, which can impact the diagnosis or treatment of patients. Arabic language dialect corpuses published in recent research work can be applied to rule-based natural language applications. Our study aims to develop an approach to support medical practitioners by ensuring that the diagnosis is not impeded based on the misinterpretation of patient responses. Our initial approach reported in this work adopts the methods used by practitioners in the diagnosis carried out within the scope of the Emirati and Egyptian Arabic dialects. In this paper, we develop and provide a public Arabic Dialect Dataset (ADD), which is a corpus of audio samples related to healthcare. In order to train machine learning models, the dataset development is designed with multi-class labelling. Our work indicates that there is a clear risk of bias in datasets, which may come about when a large number of classes do not have enough training samples. Our crowd sourcing solution presented in this work may be an approach to overcome the sourcing of audio samples. Models trained with this dataset may be used to support the diagnosis made by medical practitioners.

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“…More recent work describes a clear need for CDS systems for analyzing unstructured data, such as the clinical narratives found in EMRs. 10 There are many powerful analytic tools that utilize various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions.…”

Section: Clinical Decision Support and Data Analyticsmentioning

confidence: 99%