1/27/2024 0 Comments Machine learning text extractor![]() This dataset, named PadChest, includes more than 160,000 images obtained from 67,000 patients, covering six different position views and additional information on image acquisition and patient demography. For this task, all the chest-x rays that had been interpreted and reported by radiologists at the Hospital Universitario de San Juan (Alicante) from Jan 2009 to Dec 2017 were used to build a novel large-scale dataset in which each high-resolution radiograph is labeled with its corresponding metadata, radiological findings and pathologies. They allow for inexpensive screening of several pathologies including masses, pulmonary nodules, effusions, cardiac abnormalities and pneumothorax. In particular, chest x-ray is the most common medical imaging exam with over 35 million taken every year in the US alone (Kamel et al., 2017). ![]() Conventional radiology remains the most performed technique in radiodiagnosis services, with a percentage close to 75% (Radiología Médica, 2010). The second main task addressed in this thesis is related to knowledge extraction from medical reports associated with radiographs. Results show that representation learning using deep neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols and potentially assist practitioners when prescribing treatments. The semantic reasoning of the word-embedding representations obtained was also analyzed, being able to identify equivalent treatments for a type of tumor in an analogy with the drugs used to treat other tumors. For this, pretrained word embeddings were used as inputs in order to predict whether or not short free-text statements describing clinical information were considered eligible. A method based on deep neural networks is trained on a dataset of 6 million short free-texts to classify them between elegible or not elegible. For this, a model is built to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. In this thesis, a large medical corpora comprising all cancer clinical trials protocols in the last 18 years published by competent authorities was used to extract medical knowledge in order to help automatically learn patient’s eligibility in these trials. Given the clinical characteristics of particular patients, their type of cancer and the intended treatment, discovering whether or not they are represented in the corpus of available clinical trials requires the manual review of numerous eligibility criteria, which is impracticable for clinicians on a daily basis. The efficacy and safety of new treatments for patients with these characteristics are not, therefore, defined. This signifies that the results obtained in clinical trials cannot be extrapolated to patients if their clinical profiles were excluded from the clinical trial protocols. Patients are often excluded on the basis of comorbidity, past or concomitant treatments and the fact they are over a certain age, and those patients that are selected do not, therefore, mimic clinical practice. However, the eligibility criteria used in oncology trials are too restrictive. These trials are the basis employed for clinical practice guidelines and greatly assist clinicians in their daily practice when making decisions regarding treatment. Clinical trials provide the evidence needed to determine the safety and effectiveness of new medical treatments. The main results are a proof of concept of the capability of machine learning methods to discern which are regarded as inclusion or exclusion criteria in short free-text clinical notes, and a large scale chest x-ray image dataset labeled with radiological findings, diagnoses and anatomic locations. In particular, the proposed methods focus on cancer clinical trial protocols and chest x-rays reports. ![]() This thesis addresses the extraction of medical knowledge from clinical text using deep learning techniques. Natural Language Processing | Machine Learning | Artificial Intelligence | Neural Networks | Deep Learning | Computer Vision | Multilabel Text Classifiers | Clinical Research | Radiology | Chest X-Rays | Medical Image Dataset | Clinical Trials on Cancer | Medical Text Instituto Universitario de Investigación Informática ![]() Información del item - Informació de l'item - Item information Title:Įxtraction of medical knowledge from clinical reports and chest x-rays using machine learning techniques
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