scholarly journals Predicting early psychiatric readmission with natural language processing of narrative discharge summaries

2016 ◽  
Vol 6 (10) ◽  
pp. e921-e921 ◽  
Author(s):  
A Rumshisky ◽  
M Ghassemi ◽  
T Naumann ◽  
P Szolovits ◽  
V M Castro ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Psychiatric Readmission ◽  
Discharge Summaries

Health Natural Language Processing: Methodology Development and Applications (Preprint)

2020 ◽  
Author(s):  
Tianyong Hao ◽  
Zhengxing Huang ◽  
Likeng Liang ◽  
Heng Weng ◽  
Buzhou Tang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Technologies ◽  
Medical Decision ◽  
Eligibility Criteria ◽  
Text Data ◽  
Methodology Development ◽  
Domain Experts ◽  
Discharge Summaries

UNSTRUCTURED With the rapid growth of information technology, the necessity for processing massive amounts of health and medical data utilizing advanced information technologies has also grown. A large amount of valuable data exists in natural text such as free diagnosis text, discharge summaries, online health discussions, eligibility criteria of clinical trials, and so on. Health natural language processing automatically analyzes the commonalities and differences of large amounts of text data and recommend appropriate actions on behalf of domain experts to assist medical decision making. This editorial shares the methodology innovation of health natural language processing and its applications in medial domain.

scholarly journals Developing A Deep Learning Natural Language Processing Algorithm For Automated Reporting Of Adverse Drug Reactions

2021 ◽  
Author(s):  
Christopher McMaster ◽  
Julia Chan ◽  
David FL Liew ◽  
Elizabeth Su ◽  
Albert G Frauman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Adverse Events ◽  
Natural Language ◽  
Adverse Drug Reactions ◽  
Language Processing ◽  
Processing Algorithm ◽  
Drug Reactions ◽  
Discharge Summaries ◽  
Natural Language Processing Algorithm

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 150,000 unlabelled discharge summaries; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.934 (95% CI 0.931 - 0.955) for the task of identifying discharge summaries containing ADR mentions.

Studying Associations Between Heart Failure Self-Management and Rehospitalizations Using Natural Language Processing

2016 ◽  
Vol 39 (1) ◽  
pp. 147-165 ◽  
Author(s):  
Maxim Topaz ◽  
Kavita Radhakrishnan ◽  
Suzanne Blackley ◽  
Victor Lei ◽  
Kenneth Lai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hospital Readmissions ◽  
Discharge Summary ◽  
Self Management ◽  
Regression Analyses ◽  
Discharge Summaries ◽  
Natural Language Processing Algorithm

This study developed an innovative natural language processing algorithm to automatically identify heart failure (HF) patients with ineffective self-management status (in the domains of diet, physical activity, medication adherence, and adherence to clinician appointments) from narrative discharge summary notes. We also analyzed the association between self-management status and preventable 30-day hospital readmissions. Our natural language system achieved relatively high accuracy ( F-measure = 86.3%; precision = 95%; recall = 79.2%) on a testing sample of 300 notes annotated by two human reviewers. In a sample of 8,901 HF patients admitted to our healthcare system, 14.4% ( n = 1,282) had documentation of ineffective HF self-management. Adjusted regression analyses indicated that presence of any skill-related self-management deficit (odds ratio [OR] = 1.3, 95% confidence interval [CI] = [1.1, 1.6]) and non-specific ineffective self-management (OR = 1.5, 95% CI = [1.2, 2]) was significantly associated with readmissions. We have demonstrated the feasibility of identifying ineffective HF self-management from electronic discharge summaries with natural language processing.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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