A Framework for Applying Natural Language Processing in Digital Health Interventions

Funk, Burkhardt; Sadeh‐Sharvit, Shiri; Fitzsimmons‐Craft, Ellen E.; Trockel, Mickey; Monterubio, Grace E.; Goel, Neha J.; Balantekin, Katherine N.; Eichen, Dawn M.; Flatt, Rachael E.; Firebaugh, Marie Laure; Jacobi, Corinna; Graham, Andrea K.; Hoogendoorn, Mark; Wilfley, Denise E.; Taylor, C. Barr

doi:10.2196/13855

Cited by 40 publications

(29 citation statements)

References 37 publications

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“…Natural language processing is another branch of AI that can be applied in COVID-19 drug development. Its methods can be beneficial for extracting meaning from text using machine learning approaches and searching for external biomedical content in drug discovery [ 31 , 32 ]. Another AI approach is machine or computer vision, which is the use of algorithms to enable computers to comprehend the content of images.…”

Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

122

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Background Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. Objective The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. Methods We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. Results Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. Conclusions We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.

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Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

122

View full text Add to dashboard Cite

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“…In fact, bag-of-words models represent each transcript by a real-valued vector whose dimension is equal to the size of the vocabulary of the whole data set of transcripts. They are widely used for text classification tasks, including studies in digital health [ 29 , 40 - 42 ]. We computed bag-of-words models using the TfIdfVectorizer() function in the Python sklearn library [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

“…POS tagging is the process of assigning a POS tag to each word in a given corpus [ 38 , 39 ]; the algorithm that performs the tagging is called a POS tagger; a set of all tags is called a tagset. POS tagging enables including information from a word’s context (ie, its relationships with close and related words in a document) in text classification tasks [ 29 ]. In this study, we used the POS tagger provided in the core model for the German language “de_core_news_sm,” which is available in the Python library SpaCy [ 44 ], to generate the POS tags for all tokens retrieved from the corpus of 2214 transcripts.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, word embeddings are generally real-vector representations of textual data of much lower dimension than, for example, those in bag-of-words models. They have emerged as a common technique to compute representations of textual data, including studies in digital health [ 29 , 40 ]. In this study, given the limited number of available transcripts, we opted for pretrained German word embeddings using the SpaCy core model for the German language “de_core_news_sm.” The model is “German multi-task CNN trained on the TIGER and WikiNER corpus” [ 46 ] and each word embedding has 300 dimensions.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, they could support the design of computer systems to detect social reminiscence in the everyday lives of older adults in real time and classify different reminiscence functions. These systems lie at the core of digital health intervention programs [ 29 ] aimed at improving older adults’ well-being by promoting self-reflection, as suggested by the healthy aging model. They also provide users with coping strategies for dysfunctional reminiscence [ 13 ], which has a negative emotional valence and affects physical and mental health [ 30 - 32 ].…”

Section: Introductionmentioning

confidence: 99%

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Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Ferrario¹,

Demiray²,

Yordanova³

et al. 2020

J Med Internet Res

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Background Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations. Objective The aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts. Methods The methods in this study comprise (1) collecting and coding of transcripts of older adults’ conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. Results Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs. Conclusions This study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults’ everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults’ well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health.

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Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

Dasgupta

Bakshi

Mukherjee

et al. 2022

WIREs Data Min & Knowl

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World is now experiencing a major health calamity due to the coronavirus disease (COVID‐19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x‐ray or computed tomography (CT) or high‐resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug–protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI‐based technologies for rapid and efficient detection from CT images complementing quantitative real‐time polymerase chain reaction and immunodiagnostic assays. AI‐based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID‐19. This article is categorized under: Application Areas > Health Care Algorithmic Development > Biological Data Mining Technologies > Machine Learning

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A Framework for Applying Natural Language Processing in Digital Health Interventions

Cited by 40 publications

References 37 publications

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

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