Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances PMC

Natural Language Processing NLP: What it is and why it matters

natural language processing overview

Such texts sources include social media and online fora [18–21] as well as doctor-patient interactions [22–24] and online therapy [25], to mention a few examples. However, although there have been a few shared tasks related to mental health [26–28] the field is still narrower than that of biomedical or general clinical NLP. By combining machine learning with natural language processing and text analytics.

natural language processing overview

Already in 1950, Alan Turing published an article titled “Computing Machinery and Intelligence” which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. The proposed test includes a task that involves the automated interpretation and generation of natural language. It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences.

Syntactic Text Processing

These include the Informatics for Integrating Biology and the Bedside (i2b2) challenges [5–9], the Conference and Labs of the Evaluation Forum (CLEF) eHealth challenges [10–13], and the Semantic Evaluation (SemEval) challenges [14–16]. These efforts have enabled a valuable platform for international NLP method development. Similarly, statistical-NLP methods correspond minimally to human thought processes. Our findings show that a large number of fields of study have been studied, including trending fields such as multimodality, responsible & trustworthy NLP, and natural language interfaces. We hope that this article provides a useful overview of the current NLP landscape and can serve as a starting point for a more in-depth exploration of the field.

At the heart of this move is the understanding that much (or most) of the work effected by language processing algorithms is too complex to be captured by rules constructed by human generalization, and it rather requires machine learning methods [66–69]. For example, early statistical part-of-speech tagging algorithms using Hidden Markov Models were shown to achieve performance comparable to humans, while a statistical parser has shown better performance than a broad-coverage rule-based parser [70]. Different researchers in the past have used different modalities and algorithms to diagnose patients with different mental illnesses such as AD, Parkinson disease (PD), etc. Fraser et al. [34] used the speech narratives of healthy individuals and patients diagnosed with AD to build a diagnostic system based on a logistic regression algorithm. DementiaBank is a widely used corpus that has the speech narratives of patients with AD along with those of healthy control normal individuals [35].

Sentiment Analysis

This not only improves the efficiency of work done by humans but also helps in interacting with the machine. Apart from the speech narratives in the English language, work has been done in many other regional languages also. Vincze et al. [37] used the speech narratives of patients in the Hungarian language.

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Commonsense reasoning bridges premises and hypotheses using world knowledge that is not explicitly provided in the text (Ponti et al., 2020), while numerical reasoning performs arithmetic operations (Al-Negheimish et al., 2021). Machine reading comprehension aims to teach machines to determine the correct answers to questions based on a given passage (Zhang et al., 2021). Responsible & trustworthy NLP is concerned with implementing methods that focus on fairness, explainability, accountability, and ethical aspects at its core (Barredo Arrieta et al., 2020). Green & sustainable NLP is mainly focused on efficient approaches for text processing, while low-resource NLP aims to perform NLP tasks when data is scarce. Additionally, robustness in NLP attempts to develop models that are insensitive to biases, resistant to data perturbations, and reliable for out-of-distribution predictions.

While nonspatial prepositions do not describe or point to a location, spatial prepositions identify locations that are mostly within proximity (i.e., not geographically distinct). Geospatial prepositions on the other hand describe locations that are geographically distinguishable from another. Related research works [6–9] have focused on geospatial identification and extraction from text. Table 7.1 gives a summary of AI-based techniques for diagnosing different types of headache disorders. All these suggestions can help students analyze of a research paper well, especially in the field of NLP and beyond. When doing a formal review, students are advised to apply all of the presented steps described in the article, without any changes.

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