Automated information extraction from free-text EEG reports

In this study we have developed a supervised learning to automatically detect with high accuracy EEG reports that describe seizures and epileptiform discharges. We manually labeled 3,277 documents as describing one or more seizures vs no seizures, and as describing epileptiform discharges vs no epileptiform discharges. We then used Naïve Bayes to develop a system able to automatically classify EEG reports into these categories. Our system consisted of normalization techniques, extraction of key sentences, and automated feature selection using cross validation. As candidate features we used key words and special word patterns called elastic word sequences (EWS). Final feature selection was accomplished via sequential backward selection. We used cross validation to predict out of sample performance. Our automated feature selection procedure resulted in a classifier with 38 features for seizure detection, and 23 features for epileptiform discharge detection. The average [95% CI] area under the receiver operating curve was 99.05 [98.79, 99.32]% for detecting reports with seizures, and 96.15 [92.31, 100.00]% for detecting reports with epileptiform discharges. The methodology described herein greatly reduces the manual labor involved in identifying large cohorts of patients for retrospective neurophysiological studies of patients with epilepsy

Eljárás radiológiai leletek automatikus BNO kódolására

Cikkünkben egy amerikai kórházak és kutatóintézetek által, 2007 tavaszán rendezett nyílt verseny eredményeiről számolunk be. A verseny célja radiológiai leletek automatikus címkézése volt ICD-9-CM kódokkal (a Betegségek Nemzetközi Osztályozásával /BNO/ megegyező, számlázáshoz használt kódrendszer). A feladat érdekességét más, korábbi szövegfeldolgozási versenyekhez hasonlítva a szöveghez rendelendő kódok nagy száma, illetve a kódrendszer címkéi közti belső összefüggések adták (összesen 45 kód 96-féle különböző kombinációja fordult elő a korpuszban). A leletek automatikus osztályozását lehetővé tevő számítógépes eljárások fejlesztése létfontosságú, hiszen orvosi témájú szöveges dokumentumok kódolására, illetve a feladat során keletkező hibák javítására évi mintegy 25 milliárd dollárt fordítanak, pl. az Egyesült Államokban. A versenyre benyújtott rendszerek tanulsága, hogy a klinikai dokumentumok – emberi pontossághoz közelítő – eredményes feldolgozása nem lehetetlen célkitűzés a napjainkban rendelkezésre álló eszközökkel

Automated Detection of Radiology Reports that Document Non-routine Communication of Critical or Significant Results

The purpose of this investigation is to develop an automated method to accurately detect radiology reports that indicate non-routine communication of critical or significant results. Such a classification system would be valuable for performance monitoring and accreditation. Using a database of 2.3 million free-text radiology reports, a rule-based query algorithm was developed after analyzing hundreds of radiology reports that indicated communication of critical or significant results to a healthcare provider. This algorithm consisted of words and phrases used by radiologists to indicate such communications combined with specific handcrafted rules. This algorithm was iteratively refined and retested on hundreds of reports until the precision and recall did not significantly change between iterations. The algorithm was then validated on the entire database of 2.3 million reports, excluding those reports used during the testing and refinement process. Human review was used as the reference standard. The accuracy of this algorithm was determined using precision, recall, and F measure. Confidence intervals were calculated using the adjusted Wald method. The developed algorithm for detecting critical result communication has a precision of 97.0% (95% CI, 93.5–98.8%), recall 98.2% (95% CI, 93.4–100%), and F measure of 97.6% (ß = 1). Our query algorithm is accurate for identifying radiology reports that contain non-routine communication of critical or significant results. This algorithm can be applied to a radiology reports database for quality control purposes and help satisfy accreditation requirements

Distributed knowledge based clinical auto-coding system

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD- 10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously

Automatic construction of rule-based ICD-9-CM coding systems

Background: In this paper we focus on the problem of automatically constructing ICD-9-CM coding systems for radiology reports. ICD-9-CM codes are used for billing purposes by health institutes and are assigned to clinical records manually following clinical treatment. Since this labeling task requires expert knowledge in the field of medicine, the process itself is costly and is prone to errors as human annotators have to consider thousands of possible codes when assigning the right ICD-9-CM labels to a document. In this study we use the datasets made available for training and testing automated ICD-9-CM coding systems by the organisers of an International Challenge on Classifying Clinical Free Text Using Natural Language Processing in spring 2007. The challenge itself was dominated by entirely or partly rule-based systems that solve the coding task using a set of hand crafted expert rules. Since the feasibility of the construction of such systems for thousands of ICD codes is indeed questionable, we decided to examine the problem of automatically constructing similar rule sets that turned out to achieve a remarkable accuracy in the shared task challenge. Results: Our results are very promising in the sense that we managed to achieve comparable results with purely hand-crafted ICD-9-CM classifiers. Our best model got a 90.26 % F measure on the training dataset and an 88.93 % F measure on the challenge test dataset, using the micro-averaged Fβ=1 measure, the official evaluatio

Specializing for predicting obesity and its co-morbidities

AbstractWe present specializing, a method for combining classifiers for multi-class classification. Specializing trains one specialist classifier per class and utilizes each specialist to distinguish that class from all others in a one-versus-all manner. It then supplements the specialist classifiers with a catch-all classifier that performs multi-class classification across all classes. We refer to the resulting combined classifier as a specializing classifier.We develop specializing to classify 16 diseases based on discharge summaries. For each discharge summary, we aim to predict whether each disease is present, absent, or questionable in the patient, or unmentioned in the discharge summary. We treat the classification of each disease as an independent multi-class classification task. For each disease, we develop one specialist classifier for each of the present, absent, questionable, and unmentioned classes; we supplement these specialist classifiers with a catch-all classifier that encompasses all of the classes for that disease. We evaluate specializing on each of the 16 diseases and show that it improves significantly over voting and stacking when used for multi-class classification on our data