Introduction to Text Mining with Machine Learning

Žižka, Ján; Dařena, František; Svoboda, Arnošt

doi:10.1201/9780429469275-1

Cited by 5 publications

(11 citation statements)

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“…Text mining, sometimes referred to as natural language processing (NLP), is a way of converting unstructured text into structured data ( 23 ). This allows for automated analysis of large corpora that would otherwise be infeasible for a human researcher to analyze.…”

Section: Discussionmentioning

confidence: 99%

“…It was noted that different publications interchangeably used words such as “region,” “gyrus,” “area” and “cortex” when describing brain regions. We utilized pre-processing of data in each corpus to account for such linguistic idiosyncrasies ( 23 ).…”

Section: Limitationsmentioning

confidence: 99%

“…Unlike a meta-analysis or systematic review comprised of 5–20 papers in which a single paper may skew results, our dataset for text-mining would be 2–3 orders of magnitude larger. This is an important distinction for our choice of methodology and selection of data sources ( 23 ).…”

Section: Introductionmentioning

confidence: 99%

“…After establishing the corpora of documents and the dictionary of anatomical terms, text-mining analysis was performed on all four corpora. Each document was pre-processed to clean data for text mining, ensuring optimal machine readability ( 23 ). This included various steps such as:…”

Section: Introductionmentioning

confidence: 99%

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Mapping topography and network of brain injury in patients with disorders of consciousness

Arachige

Seneviratne²,

John³

et al. 2023

Front. Neurol.

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BackgroundThere is a growing interest in the topography of brain regions associated with disorders of consciousness. This has caused increased research output, yielding many publications investigating the topic with varying methodologies. The objective of this study was to ascertain the topographical regions of the brain most frequently associated with disorders of consciousness.MethodsWe performed a cross-sectional text-mining analysis of disorders of consciousness studies. A text mining algorithm built in the Python programming language searched documents for anatomical brain terminology. We reviewed primary PubMed studies between January 1st 2000 to 8th February 2023 for the search query “Disorders of Consciousness.” The frequency of brain regions mentioned in these articles was recorded, ranked, then built into a graphical network. Subgroup analysis was performed by evaluating the impact on our results if analyses were based on abstracts, full-texts, or topic-modeled groups (non-negative matrix factorization was used to create subgroups of each collection based on their key topics). Brain terms were ranked by their frequency and concordance was measured between subgroups. Graphical analysis was performed to explore relationships between the anatomical regions mentioned. The PageRank algorithm (used by Google to list search results in order of relevance) was used to determine global importance of the regions.ResultsThe PubMed search yielded 24,944 abstracts and 3,780 full texts. The topic-modeled subgroups contained 2015 abstracts and 283 full texts. Text Mining across all document groups concordantly ranked the thalamus the highest (Savage score = 11.716), followed by the precuneus (Savage score = 4.983), hippocampus (Savage score = 4.483). Graphical analysis had 5 clusters with the thalamus once again having the highest PageRank score (PageRank = 0.0344).ConclusionThe thalamus, precuneus and cingulate cortex are strongly associated with disorders of consciousness, likely due to the roles they play in maintaining awareness and involvement in the default mode network, respectively. The findings also suggest that other areas of the brain like the cerebellum, cuneus, amygdala and hippocampus also share connections to consciousness should be further investigated.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping topography and network of brain injury in patients with disorders of consciousness

Arachige

Seneviratne²,

John³

et al. 2023

Front. Neurol.

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“…The automation of the attribution process through Machine Learning (ML), such as scheduling and staff assignment, or resource optimization, has been recently analysed (El-Dash, 2007;Gutjahr and Reiter, 2010;Wu and Sun, 2006), and a set of classifier models was investigated to predict subcategories based on textual occupant-generated WOs (McArthur et al, 2018). Previous research also provides a prediction model that automatically assigns staff in response to task requests in unstructured textual WOs (Mo et al, 2020), applying different ML methods (Baek et al, 2021;Çınar et al, 2020;McArthur et al, 2018;Mo et al, 2020;Žižka et al, 2019). However, none of these studies refers to the cultural heritage fields, then further research is necessary to evaluate the applicability of these approaches to cultural heritage buildings, in order to improve the whole maintenance management pursuing a conservation goal.…”

Section: Introductionmentioning

confidence: 99%

Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user’s maintenance requests

2023

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Preventive conservation of cultural heritage can avoid or minimize future damage, deterioration, loss and consequently, any invasive intervention. Recently, Machine Learning methods were proposed to support preventive conservation and maintenance plans, based on their ability to predict the future state of the built heritage by collected data. Several data sources were used, such as structural data and images depicting the evolution of the deterioration state, but till now textual information, exchanged by people living or working in historical buildings to require maintenance interventions, was not used to support conservation programmes. This work proposes a method to support preventive conservation programs based on the analysis of data collected into CMMS (computer maintenance management software). In a Cultural Heritage building in Italy, hosting a University Campus, data about end-user’s maintenance requests collected for 34 months were analysed, and LSTM neural networks were trained to predict the category of each request. Results show a prediction accuracy of 96.6%, thus demonstrating the potentialities of this approach in dynamically adapting the maintenance program to emerging issues.

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Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes

Chang

Yang

et al. 2022

CIN: Computers, Informatics, Nursing

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Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability.This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes.

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Introduction to Text Mining with Machine Learning

Cited by 5 publications

References 0 publications

Mapping topography and network of brain injury in patients with disorders of consciousness

Mapping topography and network of brain injury in patients with disorders of consciousness

Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user’s maintenance requests

Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes

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