6 research outputs found
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks
Coding diagnosis and procedures in medical records is a crucial process in
the healthcare industry, which includes the creation of accurate billings,
receiving reimbursements from payers, and creating standardized patient care
records. In the United States, Billing and Insurance related activities cost
around $471 billion in 2012 which constitutes about 25% of all the U.S hospital
spending. In this paper, we report the performance of a natural language
processing model that can map clinical notes to medical codes, and predict
final diagnosis from unstructured entries of history of present illness,
symptoms at the time of admission, etc. Previous studies have demonstrated that
deep learning models perform better at such mapping when compared to
conventional machine learning models. Therefore, we employed state-of-the-art
deep learning method, ULMFiT on the largest emergency department clinical notes
dataset MIMIC III which has 1.2M clinical notes to select for the top-10 and
top-50 diagnosis and procedure codes. Our models were able to predict the
top-10 diagnoses and procedures with 80.3% and 80.5% accuracy, whereas the
top-50 ICD-9 codes of diagnosis and procedures are predicted with 70.7% and
63.9% accuracy. Prediction of diagnosis and procedures from unstructured
clinical notes benefit human coders to save time, eliminate errors and minimize
costs. With promising scores from our present model, the next step would be to
deploy this on a small-scale real-world scenario and compare it with human
coders as the gold standard. We believe that further research of this approach
can create highly accurate predictions that can ease the workflow in a clinical
setting.Comment: This is a shortened version of the Capstone Project that was accepted
by the Faculty of Indiana University, in partial fulfillment of the
requirements for the degree of Master of Science in Health Informatics in Dec
201
The quality of vital signs measurements and value preferences in electronic medical records varies by hospital, specialty, and patient demographics
We aimed to assess the frequency of value preferences in recording of vital signs in electronic healthcare records (EHRs) and associated patient and hospital factors. We used EHR data from Oxford University Hospitals, UK, between 01-January-2016 and 30-June-2019 and a maximum likelihood estimator to determine the prevalence of value preferences in measurements of systolic and diastolic blood pressure (SBP/DBP), heart rate (HR) (readings ending in zero), respiratory rate (multiples of 2 or 4), and temperature (readings of 36.0 °C). We used multivariable logistic regression to investigate associations between value preferences and patient age, sex, ethnicity, deprivation, comorbidities, calendar time, hour of day, days into admission, hospital, day of week and speciality. In 4,375,654 records from 135,173 patients, there was an excess of temperature readings of 36.0 °C above that expected from the underlying distribution that affected 11.3% (95% CI 10.6–12.1%) of measurements, i.e. these observations were likely inappropriately recorded as 36.0 °C instead of the true value. SBP, DBP and HR were rounded to the nearest 10 in 2.2% (1.4–2.8%) and 2.0% (1.3–5.1%) and 2.4% (1.7–3.1%) of measurements. RR was also more commonly recorded as multiples of 2. BP digit preference and an excess of temperature recordings of 36.0 °C were more common in older and male patients, as length of stay increased, following a previous normal set of vital signs and typically more common in medical vs. surgical specialities. Differences were seen between hospitals, however, digit preference reduced over calendar time. Vital signs may not always be accurately documented, and this may vary by patient groups and hospital settings. Allowances and adjustments may be needed in delivering care to patients and in observational analyses and predictive tools using these factors as outcomes or exposures
Introduction à l’apprentissage automatique en pharmacométrie : concepts et applications
L’apprentissage automatique propose des outils pour faire face aux problématiques d’aujourd’hui et de demain. Les récentes percées en sciences computationnelles et l’émergence du phénomène des mégadonnées ont permis à l’apprentissage automatique d’être mis à l’avant plan tant dans le monde académique que dans la société. Les récentes réalisations de l’apprentissage automatique dans le domaine du langage naturel, de la vision et en médecine parlent d’eux-mêmes. La liste des sciences et domaines qui bénéficient des techniques de l’apprentissage automatique est longue.
Cependant, les tentatives de coopération avec la pharmacométrie et les sciences connexes sont timides et peu nombreuses. L’objectif de ce projet de maitrise est d’explorer le potentiel de l’apprentissage automatique en sciences pharmaceutiques. Cela a été réalisé par l’application de techniques et des méthodes d’apprentissage automatique à des situations de pharmacologie clinique et de pharmacométrie. Le projet a été divisé en trois parties. La première partie propose un algorithme pour renforcer la fiabilité de l’étape de présélection des covariables d’un modèle de pharmacocinétique de population. Une forêt aléatoire et l’XGBoost ont été utilisés pour soutenir la présélection des covariables. Les indicateurs d’importance relative des variables pour la forêt aléatoire et pour l’XGBoost ont bien identifié l’importance de toutes les covariables qui avaient un effet sur les différents paramètres du modèle PK de référence. La seconde partie confirme qu’il est possible d’estimer des concentrations plasmatiques avec des méthodes différentes de celles actuellement utilisés en pharmacocinétique. Les mêmes algorithmes ont été sélectionnés et leur ajustement pour la tâche était appréciable. La troisième partie confirme la possibilité de faire usage des méthodes d'apprentissage automatique pour la prédiction de relations complexes et typiques à la pharmacologie clinique. Encore une fois, la forêt aléatoire et l’XGBoost ont donné lieu à un ajustement appréciable.Machine learning offers tools to deal with current problematics. Recent breakthroughs in computational sciences and the emergence of the big data phenomenon have brought machine learning to the forefront in both academia and society. The recent achievements of machine learning in natural language, computational vision and medicine speak for themselves. The list of sciences and fields that benefit from machine learning techniques is long.
However, attempts to cooperate with pharmacometrics and related sciences are timid and limited. The aim of this Master thesis is to explore the potential of machine learning in pharmaceutical sciences. This has been done through the application of machine learning techniques and methods to situations of clinical pharmacology and pharmacometrics. The project was divided into three parts. The first part proposes an algorithm to enhance the reliability of the covariate pre-selection step of a population pharmacokinetic model. Random forest and XGBoost were used to support the screening of covariates. The indicators of the relative importance of the variables for the random forest and for XGBoost recognized the importance of all the covariates that influenced the various parameters of the PK model of reference. The second part exemplifies the estimation of plasma concentrations using machine learning methods. The same algorithms were selected and their fit for the task was appreciable. The third part confirms the possibility to apply machine learning methods in the prediction of complex relationships, as some typical clinical pharmacology relationships. Again, random forest and XGBoost got a nice adjustment