Dataset Information

Cohort selection for clinical trials using deep learning models.

ABSTRACT: OBJECTIVE:The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task. MATERIALS AND METHODS:Cohort selection can be formulated as a multilabeling problem whose goal is to determine which criteria are met for each patient record. We explore several deep learning architectures such as a simple convolutional neural network (CNN), a deep CNN, a recurrent neural network (RNN), and CNN-RNN hybrid architecture. Although our architectures are similar to those proposed in existing deep learning systems for text classification, our research also studies the impact of using a fully connected feedforward layer on the performance of these architectures. RESULTS:The RNN and hybrid models provide the best results, though without statistical significance. The use of the fully connected feedforward layer improves the results for all the architectures, except for the hybrid architecture. CONCLUSIONS:Despite the limited size of the dataset, deep learning methods show promising results in learning useful features for the task of cohort selection. Therefore, they can be used as a previous filter for cohort selection for any clinical trial with a minimum of human intervention, thus reducing the cost and time of clinical trials significantly.

SUBMITTER: Segura-Bedmar I

PROVIDER: S-EPMC6798560 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Cohort selection for clinical trials using deep learning models.

Segura-Bedmar Isabel I Raez Pablo P

Journal of the American Medical Informatics Association : JAMIA 20191101 11

<h4>Objective</h4>The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task.<h4>Materials and methods</h4>Cohort selection can be formulated as a multilabeling problem w ...[more]

PMID: 31532478

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Project description:ObjectiveCohort selection for clinical trials is a key step for clinical research. We proposed a hierarchical neural network to determine whether a patient satisfied selection criteria or not.Materials and methodsWe designed a hierarchical neural network (denoted as CNN-Highway-LSTM or LSTM-Highway-LSTM) for the track 1 of the national natural language processing (NLP) clinical challenge (n2c2) on cohort selection for clinical trials in 2018. The neural network is composed of 5 components: (1) sentence representation using convolutional neural network (CNN) or long short-term memory (LSTM) network; (2) a highway network to adjust information flow; (3) a self-attention neural network to reweight sentences; (4) document representation using LSTM, which takes sentence representations in chronological order as input; (5) a fully connected neural network to determine whether each criterion is met or not. We compared the proposed method with its variants, including the methods only using the first component to represent documents directly and the fully connected neural network for classification (denoted as CNN-only or LSTM-only) and the methods without using the highway network (denoted as CNN-LSTM or LSTM-LSTM). The performance of all methods was measured by micro-averaged precision, recall, and F1 score.ResultsThe micro-averaged F1 scores of CNN-only, LSTM-only, CNN-LSTM, LSTM-LSTM, CNN-Highway-LSTM, and LSTM-Highway-LSTM were 85.24%, 84.25%, 87.27%, 88.68%, 88.48%, and 90.21%, respectively. The highest micro-averaged F1 score is higher than our submitted 1 of 88.55%, which is 1 of the top-ranked results in the challenge. The results indicate that the proposed method is effective for cohort selection for clinical trials.DiscussionAlthough the proposed method achieved promising results, some mistakes were caused by word ambiguity, negation, number analysis and incomplete dictionary. Moreover, imbalanced data was another challenge that needs to be tackled in the future.ConclusionIn this article, we proposed a hierarchical neural network for cohort selection. Experimental results show that this method is good at selecting cohort.

Project description:Increased global production of sorghum has the potential to meet many of the demands of a growing human population. Developing automation technologies for field scouting is crucial for long-term and low-cost production. Since 2013, sugarcane aphid (SCA) Melanaphis sacchari (Zehntner) has become an important economic pest causing significant yield loss across the sorghum production region in the United States. Adequate management of SCA depends on costly field scouting to determine pest presence and economic threshold levels to spray insecticides. However, with the impact of insecticides on natural enemies, there is an urgent need to develop automated-detection technologies for their conservation. Natural enemies play a crucial role in the management of SCA populations. These insects, primary coccinellids, prey on SCA and help to reduce unnecessary insecticide applications. Although these insects help regulate SCA populations, the detection and classification of these insects is time-consuming and inefficient in lower value crops like sorghum during field scouting. Advanced deep learning software provides a means to perform laborious automatic agricultural tasks, including detection and classification of insects. However, deep learning models for coccinellids in sorghum have not been developed. Therefore, our objective was to develop and train machine learning models to detect coccinellids commonly found in sorghum and classify them according to their genera, species, and subfamily level. We trained a two-stage object detection model, specifically, Faster Region-based Convolutional Neural Network (Faster R-CNN) with the Feature Pyramid Network (FPN) and also one-stage detection models in the YOLO (You Only Look Once) family (YOLOv5 and YOLOv7) to detect and classify seven coccinellids commonly found in sorghum (i.e., Coccinella septempunctata, Coleomegilla maculata, Cycloneda sanguinea, Harmonia axyridis, Hippodamia convergens, Olla v-nigrum, Scymninae). We used images extracted from the iNaturalist project to perform training and evaluation of the Faster R-CNN-FPN and YOLOv5 and YOLOv7 models. iNaturalist is an imagery web server used to publish citizen's observations of images pertaining to living organisms. Experimental evaluation using standard object detection metrics, such as average precision (AP), AP@0.50, etc., has shown that the YOLOv7 model performs the best on the coccinellid images with an AP@0.50 as high as 97.3, and AP as high as 74.6. Our research contributes automated deep learning software to the area of integrated pest management, making it easier to detect natural enemies in sorghum.

Project description:BackgroundMultiple clinical trials failed to demonstrate the efficacy of hydrocortisone, ascorbic acid, and thiamine (HAT) in sepsis. These trials were dominated by patients with pulmonary sepsis and have not accounted for differences in the inflammatory responses across varying etiologies of injury/illness. Hydrocortisone, ascorbic acid, and thiamine have previously revealed tremendous benefits in animal peritonitis sepsis models (cecal ligation and puncture [CLP]) in contradiction to the various clinical trials. The impact of HAT remains unclear in pulmonary sepsis. Our objective was to investigate the impact of HAT in pneumonia, consistent with the predominate etiology in the discordant clinical trials. We hypothesized that, in a pulmonary sepsis model, HAT would act synergistically to reduce end-organ dysfunction by the altering the inflammatory response, in a unique manner compared with CLP.MethodsUsing Pseudomonas aeruginosa pneumonia, a pulmonary sepsis model (pneumonia [PNA]) was compared directly to previously investigated intra-abdominal sepsis models. Machine learning applied to early vital signs stratified animals into those predicted to die (pDie) versus predicted to live (pLive). Animals were then randomized to receive antibiotics and fluids (vehicle [VEH]) vs. HAT). Vitals, cytokines, vitamin C, and markers of liver and kidney function were assessed in the blood, bronchoalveolar lavage, and organ homogenates.ResultsPNA was induced in 119 outbred wild-type Institute of Cancer Research mice (predicted mortality approximately 50%) similar to CLP. In PNA, interleukin 1 receptor antagonist in 72-hour bronchoalveolar lavage was lower with HAT (2.36 ng/mL) compared with VEH (4.88 ng/mL; p = 0.04). The remaining inflammatory cytokines and markers of liver/renal function showed no significant difference with HAT in PNA. PNA vitamin C levels were 0.62 mg/dL (pDie HAT), lower than vitamin C levels after CLP (1.195 mg/dL). Unlike CLP, PNA mice did not develop acute kidney injury (blood urea nitrogen: pDie, 33.5 mg/dL vs. pLive, 27.6 mg/dL; p = 0.17). Furthermore, following PNA, HAT did not significantly reduce microscopic renal oxidative stress (mean gray area: pDie, 16.64 vs. pLive, 6.88; p = 0.93). Unlike CLP where HAT demonstrated a survival benefit, HAT had no impact on survival in PNA.ConclusionHydrocortisone, ascorbic acid, and thiamine therapy has minimal benefits in pneumonia. The inflammatory response induced by pulmonary sepsis is unique compared with the response during intra-abdominal sepsis. Consequently, different etiologies of sepsis respond differently to HAT therapy.

Project description:Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).

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Cohort selection for clinical trials using deep learning models.

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Cohort selection for clinical trials using deep learning models.

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