Ontology-based clinical information extraction from physician’s free-text notes

Yehia, Engy; Boshnak, Hussein; AbdelGaber, Sayed; Abdo, A.; Elzanfaly, Doaa S.

doi:10.1016/j.jbi.2019.103276

Cited by 34 publications

(9 citation statements)

References 27 publications

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“…In the NLP module, POS Tagging is used to detect the tag related to each word, afterward a set of rules is defined and applied to eliminate junk verbs from the given sentences like "admitted" and "associated". The information extraction module base verb Removing produces a useful method to focus on categorizing the noun phrases [37]. The POS tags gained for every word in the clinical texts help distinguish the meaningful words to be selected and considered for analysis.…”

Section: Preprocessing Of Covid-19 Clinical Textsmentioning

confidence: 99%

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Mahdi¹,

Yuhaniz²

2023

Computers, Materials &Amp; Continua

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The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data. In this research, we proposed a modification term weighting scheme: RTF-C-IEF and compare the proposed model with four extraction methods: TF, TF-IDF, TF-IHF, and TF-IEF. The experiment was conducted on two new datasets for COVID-19 patients. The first dataset was collected from government hospitals in Iraq with 3053 clinical records, and the second dataset with 1446 clinical reports, was collected from several different websites. Based on the experimental results using several popular classifiers applied to the datasets of Covid-19, we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments. Further, the modified RTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments. Thus, the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.

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Section: Preprocessing Of Covid-19 Clinical Textsmentioning

confidence: 99%

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Mahdi¹,

Yuhaniz²

2023

Computers, Materials &Amp; Continua

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“…Often, studies aiming to find the most complete and accurate format are based on surveys of a small number of doctors (Williams 2003). It is possible to develop such a model to extract specific information (Wang et al 2012;Yehia et al 2019). However, it is necessary to define such a list of questions and entities for each record type manually.…”

Section: Related Workmentioning

confidence: 99%

Citywide quality of health information system through text mining of electronic health records

et al. 2021

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A system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, and individual behavior of physicians. The unstructured (textual) data of EHR is rarely used to assess the citywide quality of healthcare. Within the study, we analyze EHR data, particularly textual unstructured data, as a reflection of the complex multi-agent system of healthcare in the city of Saint Petersburg, Russia. Through analyzing the data collected by the Medical Information and Analytical Center, a method was proposed and evaluated for identifying a common structure, understanding the diversity, and assessing information quality in EHR data through the application of natural language processing techniques.

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“…This system, however, relied on clinical notes in a structured form and complete free text could not be analyzed. Other already existing algorithms for extracting information from clinical notes used domain ontologies to recognize and detect named entities [4]. Zhang et al focussed on the use of Statistical Language Modeling, where a probability is assigned to a specific group of words within the examined set of notes [5].…”

Section: State-of-the-artmentioning

confidence: 99%

Automated Extraction of Time References from Clinical Notes in a Heart Failure Telehealth Network

Wiesmüller

Eggerth

Kreiner

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Heart failure (HF) is one of the biggest concerns for health care systems in developed countries. To support the long-term treatment of HF patients, the Austrian Institute of Technology implemented a HF telehealth network called "HerzMobil". While most data within this network are stored in a structured format, health care professionals can also communicate via clinical notes in free text format. These notes are hardly ever analyzed automatically, even though a large number contains valuable information for the patient's treatment process. With currently more than 20,000 notes stored in the system, an automatic approach is beneficial to spare manual screening time. One important step in this process concerns the extraction of time references from the notes. This information could, for example, be used to match the time references with events from the same note. Therefore, two Python scripts were developed to: extract time references from the notes (Script A) and subsequently calculate the corresponding dates (Script B). Script A was compared to an already existing Python library and achieved superior results for all calculated key figures. The time calculation algorithm of Script B achieved an accuracy of 75.34%. These scripts could be implemented in the HerzMobil network to provide additional information for the treatment process and further improve the telehealth system.

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Ontology-based clinical information extraction from physician’s free-text notes

Cited by 34 publications

References 27 publications

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Citywide quality of health information system through text mining of electronic health records

Automated Extraction of Time References from Clinical Notes in a Heart Failure Telehealth Network

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