NLP for Medical Domain

Abstract:In addition to the medical education in the Federal Republic of Germany which includes a compulsory Medical Informatics course there exists a formal program for professional qualification of physicians in Medical Informatics. After two years of clinical practice and 1.5 years of professional training at an authorized institution, a physician may receive in addition to the medical degree a “supplement Medical Informatics”. The qualification requirements are described in detail. Physicians with the additional Medical Informatics qualification perform responsible tasks in their medical domain and serve as partners for fully specialized Medical Informatics ex-’ perts in the solution of practical Medical Informatics problems. The formal qualification is available for more than 10 years, has become increasingly attractive, and is expected to grow with respect to future Medical Informatics developments.

Download Full-text

Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

Applied Sciences ◽

10.3390/app11020796 ◽

2021 ◽

Vol 11 (2) ◽

pp. 796

Author(s):

Alhanoof Althnian ◽

Duaa AlSaeed ◽

Heyam Al-Baity ◽

Amani Samha ◽

Alanoud Bin Dris ◽

...

Keyword(s):

Empirical Evaluation ◽

Classification Performance ◽

Support Vector ◽

Robust Model ◽

Original Distribution ◽

C4.5 Decision Tree ◽

Dataset Size ◽

Overall Performance ◽

Medical Domain ◽

The Impact

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

Download Full-text

Socialising Disease: Medical Categories and Inclusion of the Aged

The Sociological Review ◽

10.1111/1467-954x.00222 ◽

2000 ◽

Vol 48 (3) ◽

pp. 383-407 ◽

Cited By ~ 11

Author(s):

Joanna Latimer

Keyword(s):

Older People ◽

Hospital Care ◽

Social Inclusion ◽

Social Problems ◽

Older Person ◽

Medical Unit ◽

Life Chances ◽

Acute Medical Unit ◽

Medical Domain

When older peoples' troubles are categorised as social rather than medical, hospital care can be denied them. Drawing on an ethnography of older people admitted as emergencies to an acute medical unit, the article demonstrates how medical categories can provide shelter for older people. By holding their clinical identity on medical rather than social grounds, physicians who specialise in gerontology in the acute medical domain can help prevent the over-socialising of an older person's health troubles. As well as helping the older person to draw certain resources to themselves, such as treatment and care, this inclusion in positive medical categories can provide shelter for the older person, to keep at bay their effacement as ‘social problems'. These findings suggest that contemporary sociological critique of biomedicine may underestimate how medical categorising, as the obligatory passage through which to access important resources and life chances, can constitute a process of social inclusion.

Download Full-text