Text mining and NLP for health text

Quantifiable information: the service turns large volumes of unstructured text into categories, themes and metrics that feed statistical analyses. It applies when data are trapped in free text, such as abstracts from a literature search, intervention records, field notes or open-ended survey responses: text that is impractical to read item by item and goes to waste because it is not treated as data. On that corpus, the analysis identifies thematic patterns that neither individual reading nor keyword search can reveal, quantifies lexical, thematic or sentiment orientation, and derives variables that enter subsequent models. The text stops being a qualitative appendix and becomes a source of analysable variables. What the service provides is not a narrative summary of what the text says, but its conversion into metrics you can work with statistically.

Three main lines today: sentiment analysis, topic modelling and document classification, on a corpus that has been preprocessed and represented numerically. This applies if you need to characterise the content or orientation of a set of health or scientific documents. Sentiment analysis uses published, documented lexicons such as NRC or Bing, always checking that they suit the language, domain and aim of the corpus, rather than hand-crafted rules that only work on one particular corpus. Topic modelling uses LDA models, with the number of topics selected by coherence and perplexity, to propose latent thematic groupings without prior labelling. Before modelling, the text is preprocessed through tokenisation, removal of standard and custom stop words, and lemmatisation, and is represented as a document-term matrix weighted by frequency and TF-IDF. What you get are replicable, documentable methods that are more defensible under review than ad hoc rules.

Variables and interpretive documentation, not just a narrative report: the output is a set of numeric metrics derived from the text, such as the dominant topic, the sentiment score or the assigned category, which go straight into subsequent analyses as predictors or outcomes. They come with the topic labelling, the term review and the preprocessing documentation needed to interpret them. This applies when the text has to be integrated with the rest of a study's quantitative analysis, rather than left as a separate qualitative appendix. These derived variables are fed into regression models, clustering or network analysis, so the content of the text becomes part of the statistical model as derived variables, with the caveats of the method that generates them. This makes it possible, for example, to relate a record's dominant topic to an outcome variable, or to use the sentiment score as a predictor. The work is documented in Quarto with reproducible code. What you receive is text converted into analysable data and integrated into the pipeline, not a description of what the text says.

The preprocessing is adapted to the health domain, precisely because generic preprocessing can mishandle its technical vocabulary, abbreviations and structures. This applies to health and scientific text corpora, where a generic treatment introduces noise or loses relevant terms. The adaptation includes custom stop words for the field, handling of controlled vocabularies such as MeSH where the corpus justifies it, and tokenisation that respects clinical and epidemiological terms, along with lemmatisation tuned to the domain. On that basis the representations are built, with a document-term matrix, TF-IDF, n-grams and term co-occurrence networks, which then feed the analysis. One point of scope: this adaptation applies to aggregated health text, such as abstracts, records or documentation, not to the quirks of a particular health record system. What you get is preprocessing that respects the domain's terminology, not a generic pipeline that treats it as noise.

No: the service is NLP on aggregated health and scientific text, such as abstracts, intervention records, aggregated clinical documentation, field notes or open-ended responses, not clinical NLP on individual health records. This limit needs stating because "health text" can be read both ways, and they are different jobs. What is out of scope: patient-by-patient extraction of clinical entities from individual records, using named entity recognition and coding to terminologies such as SNOMED-CT, which is a line of work with its own technical, ethical and data governance requirements. What is in scope: quantitative analysis of health and scientific text corpora to uncover themes, quantify orientation or derive variables. The boundary is clear: analysis of aggregated health text corpora, yes; mining of individual health records with NER and clinical coding, no. If you need that second line of work, you will find an explicit limit here, not a vague promise.

No. Bibliometrics studies the literature as a field: who publishes, how authors and institutions are connected, and how the thematic structure changes; its result is a map of that field. Text mining examines the content of the corpus and turns it into variables, such as topics, sentiment or categories, that can be used in statistical analysis. Both can be applied to the same set of abstracts, but they answer different questions. If you want to understand how a scientific field is organised and evolving, you need bibliometrics; if you want to know what the texts say and quantify that content, you need text mining. When the question calls for both perspectives, we combine the bibliometric map with content analysis while keeping their conclusions distinct.

Today the service relies on validated, published methods, such as lexicon-based sentiment analysis (NRC, Bing) and topic modelling with LDA; techniques based on language models are being incorporated and are not yet offered as an established capability. Being transparent about this matters, because the line between what is available and what is planned affects your decision. We are exploring and progressively incorporating techniques such as embeddings, zero-shot classification and biomedical entity extraction, which will extend the service's capabilities once they are validated. The criterion holds in both cases: validated techniques applied to real health research problems, not experimenting with tools for novelty's sake. If a project specifically requires one of those techniques, the scope should be defined case by case, according to how far the technique has been validated internally and how methodologically demanding the study is. What we offer today are the established methods; what is coming is described as what it is, ongoing development.

You need the text corpus to be analysed, such as abstracts, text records, field notes or open-ended responses, with an indication of its language and context, so that the corpus can be defined and the preprocessing strategy designed. The client provides and owns the text; the service defines the corpus, assesses its language and quality, designs the preprocessing, builds the numerical representation, applies sentiment analysis or topic modelling and derives the variables. What you receive is the set of text-derived variables, such as the dominant topic, the sentiment score or the categories, ready to integrate into the statistical analysis, together with interpretable visualisations (of sentiment, thematic evolution and topic distributions) and the documented Quarto code that regenerates the result. What is delivered is text converted into reproducible data that fits into the study's pipeline, on a corpus that remains the client's.