Clinical trial analysis is one of the main business directions and services in IQVIA, and reviewing past similar studies is one of the most critical steps before starting a commercial clinical trial. The current review process is manual and time-consuming, requiring a clinical trial analyst to manually search through an extensive clinical trial database and then review all candidate studies. Therefore, it is of great interest to develop an automatic retrieval algorithm to select similar studies by giving new study information. To achieve this goal, we propose a novel group-based trial similarity learning network named GTSLNet, consisting of two kinds of similarity learning modules. The pair-wise section-level similarity learning module aims to compare the query trial and the candidate trial from the abstract semantic level via the proposed section transformer. Meanwhile, a word-level similarity learning module uses the word similarly matrix to capture the low-level similarity information. Additionally, an aggregation module combines these similarities. To address potential false negatives and noisy data, we introduce a variance-regularized group distance loss function. Experiment results show that the proposed GTSLNet significantly and consistently outperforms state-of-the-art baselines.

Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. However, it is challenging to develop such a model because of the lack of a benchmark dataset. To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.

Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug development. By pre-estimating the adverse reactions, researchers and drug development companies can greatly prevent the potential ADR risks and tragedies. However, the current ADR prediction methods suffer from several limitations. First, the prediction results are based on pure drug-related information, which makes them impossible to be directly applied for the personalized ADR prediction task. The lack of personalization of models also makes rare adverse events hard to be predicted. Therefore, it is of great interest to develop a new personalized ADR prediction method by introducing additional sources, e.g., patient health records. However, few methods have tried to use additional sources. In the meantime, the variety of different source formats and structures makes this task more challenging. To address the above challenges, we propose a novel personalized multi-sourced-based drug adverse reaction prediction model named pADR. pADR first works on every single source to transform them into proper representations. Next, a hierarchical multi-sourced Transformer is designed to automatically model the interactions between different sources and fuse them together for the final adverse event prediction. Experimental results on a new multi-sourced ADR prediction dataset show that PADR outperforms state-of-the-art drug-based baselines. Moreover, the case and ablation studies also illustrate the effectiveness of our proposed fusion strategies and the reasonableness of each module design.

Patient enrollment is critical to the success of a clinical trial. In practice, before launching a trial, one of the top priorities is to predict the enrollment rate for different countries, so that one can select clinical sites from the countries with the highest enrollment rates to accelerate patient recruitment. However, based on the limited trial information, estimating the enrollment rate is still a challenge. To deal with this problem, we adopt a very recent tensor factorization approach that aims to approximate the joint Cumulative Distribution Function (CDF) of trial enrollment data. We can always sketch a multivariate CDF in terms of multidimensional empirical cumulative probability array, i.e., a finite grid-sampled CDF tensor, and introduce a low-rank parametrization by a Canonical Polyadic Decomposition (CPD) model. The proposed model is unassuming of the structure of the data and identifiable under mild conditions by virtue of the uniqueness of CPD. At the same time, it affords both efficient sample likelihood estimation and closed-form inference. Such model can be leveraged for reliable enrollment estimation, delivering probability estimates of a specific trial meeting an expected enrollment rate or probability estimates of being within a certain interval, as well as country recommendation. Experimental results demonstrate an improved performance of the proposed method in the enrollment rate prediction task over the best baselines by up to 12.2% in mean squared error on a real country-level trial dataset, while also offering direct means of quantifying uncertainty in the predictions based on the fitted model. The improved performance and versatility highlight the societal and financial benefits of the proposed approach, which could be transformational in modern healthcare.

OBJECTIVES: Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. MATERIALS AND METHODS: This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. RESULTS: We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. CONCLUSION: Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to control the overall prediction risk with classification with rejection options. However, existing works overlook the different significance of different classes. We introduce Set-classifier with class-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiple labels to each example. Given the output of a black-box model on the validation set, SCRIB constructs a set-classifier that controls the class-specific prediction risks. The key idea is to reject when the set classifier returns more than one label. We validated SCRIB on several medical applications, including sleep staging on electroencephalogram(EEG) data, X-ray COVID image classification, and atrial fibrillation detection based on electrocardiogram (ECG) data.SCRIB obtained desirable class-specific risks, which are 35%-88% closer to the target risks than baseline methods.

Population-level disease prediction estimates the number of potential patients of particular diseases in some location at a future time based on (frequently updated) historical disease statistics. Existing approaches often assume the existing disease statistics are reliable and will not change. However, in practice, data collection is often time-consuming and has time delays, with both historical and current disease statistics being updated continuously. In this work, we propose a real-time population-level disease prediction model which captures data latency (PopNet) and incorporates the updated data for improved predictions. To achieve this goal, PopNet models real-time data and updated data using two separate systems, each capturing spatial and temporal effects using hybrid graph attention networks and recurrent neural networks. PopNet then fuses the two systems using both spatial and temporal latency-aware attentions in an end-to-end manner. We evaluate PopNet on real-world disease datasets and show that PopNet consistently outperforms all baseline disease prediction and general spatial-temporal prediction models, achieving up to 47% lower root mean squared error and 24% lower mean absolute error compared with the best baselines.

The goal of molecular optimization is to generate molecules similar to a target molecule but with better chemical properties. Deep generative models have shown great success in molecule optimization. However, due to the iterative local generation process of deep generative models, the resulting molecules can significantly deviate from the input in molecular similarity and size, leading to poor chemical properties. The key issue here is that the existing deep generative models restrict their attention on substructure-level generation without considering the entire molecule as a whole. To address this challenge, we propose Molecule-Level Reward functions (MOLER) to encourage (1) the input and the generated molecule to be similar, and to ensure (2) the generated molecule has a similar size to the input. The proposed method can be combined with various deep generative models. Policy gradient technique is introduced to optimize reward-based objectives with small computational overhead. Empirical studies show that MOLER achieves up to 20.2% relative improvement in success rate over the best baseline method on several properties, including QED, DRD2 and LogP.

Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple drug properties while maintaining similarity to the input molecule. Existing generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties. To address such challenges, we propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution. MIMOSA first pretrains two property agnostic graph neural networks (GNNs) for molecule topology and substructure-type prediction, where a substructure can be either atom or single ring. For each iteration, MIMOSA uses the GNNs’ prediction and employs three basic substructure operations (add, replace, delete) to generate new molecules and associated weights. The weights can encode multiple constraints including similarity and drug property constraints, upon which we select promising molecules for next iteration. MIMOSA enables flexible encoding of multiple property- and similarity-constraints and can efficiently generate new molecules that satisfy various property constraints and achieved up to 49.1% relative improvement over the best baseline in terms of success rate.

Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR. Unlike standard tensor factorization methods which cannot predict slabs ahead, STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations of a widely adopted epidemiological model. We use latent instead of location/attribute-level epidemiological dynamics to capture common epidemic profile sub-types and improve collaborative learning and prediction. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic. Finally, we evaluate the predictive ability of our method and show superior performance compared to the baselines, achieving up to 21% lower root mean square error and 25% lower mean absolute error for county-level prediction.

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i.d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to 27.2% lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings.

Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all medications for the current visit, which often overlaps with medications from previous visits. A more clinically relevant task is to identify medication changes. In this paper, we propose a new recurrent residual networks, named MICRON, for medication change prediction. MICRON takes the changes in patient health records as input and learns to update a hid- den medication vector and the medication set recurrently with a reconstruction design. The medication vector is like the memory cell that encodes longitudinal information of medications. Unlike traditional methods that require the entire patient history for prediction, MICRON has a residual-based inference that allows for sequential updating based only on new patient features (e.g., new diagnoses in the recent visit), which is efficient. We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. MICRON also requires fewer parameters, which significantly reduces the training time to 38.3s per epoch with 1.5× speed-up.

Medication recommendation is an essential task of AI for healthcare. Existing works focused on recommending drug combinations for patients with complex health conditions solely based on their electronic health records. Thus, they have the following limitations: (1) some important data such as drug molecule structures have not been utilized in the recommendation process. (2) drug-drug interactions (DDI) are modeled implicitly, which can lead to sub-optimal results. To address these limitations, we propose a DDI-controllable drug recommendation model named SafeDrug to leverage drugs’ molecule structures and model DDIs explicitly. SafeDrug is equipped with a global message passing neural network (MPNN) module and a local bipartite learning module to fully encode the connectivity and functionality of drug molecules. SafeDrug also has a controllable loss function to control DDI level in the recommended drug combinations effectively. On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches. Moreover, SafeDrug also requires much fewer parameters than previous deep learning based approaches, leading to faster training by about 14% and around 2× speed-up in inference.

Drug discovery aims at finding promising drug molecules for treating target diseases. Existing computational drug discovery methods mainly depend on molecule databases, ignoring valuable data collected from clinical trials. In this work, we propose PRIME to leverage high-quality drug molecules and drug-disease relations in historical clinical trials to narrow down the molecular search space in drug discovery. PRIME also introduces time dependency constraints to model evolving drug-disease relations using a probabilistic deep learning model that can quantify model uncertainty. We evaluated PRIME against leading models on both de novo design and drug repurposing tasks. Results show that compared with the best baselines, PRIME achieves 25.9% relative improvement (i.e., reduction) in average hit-ranking on drug repurposing and 47.6% relative improvement in success rate on de novo design.

Successful health risk prediction demands accuracy and reliability of the model. Existing predictive models mainly depend on mining electronic health records (EHR) with advanced deep learning techniques to improve model accuracy. However, they all ignore the importance of publicly available online health data, especially socioeconomic status, environmental factors, and detailed demographic information for each location, which are all strong predictive signals and can definitely augment precision medicine. To achieve model reliability, the model needs to provide accurate prediction and uncertainty score of the prediction. However, existing uncertainty estimation approaches often failed in handling high-dimensional data, which are present in multi-sourced data.

SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/kexinhuang12345/DeepPurpose. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Drug-target interaction (DTI) prediction is a foundational task for in-silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (i) existing molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain and (ii) existing methods focus on limited labeled data while ignoring the value of massive unlabeled molecular data. RESULTS: We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (i) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction and (ii) an augmented transformer encoder to better extract and capture the semantic relations among sub-structures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real-world data and show it improved DTI prediction performance compared to state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: The model scripts are available at https://github.com/kexinhuang12345/moltrans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. RESULTS: To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. AVAILABILITY AND IMPLEMENTATION: The code is available in Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OBJECTIVE: The COVID-19 pandemic has created many challenges that need immediate attention. Various epidemiological and deep learning models have been developed to predict the COVID-19 outbreak, but all have limitations that affect the accuracy and robustness of the predictions. Our method aims at addressing these limitations and making earlier and more accurate pandemic outbreak predictions by (1) using patients' EHR data from different counties and states that encode local disease status and medical resource utilization condition; (2) considering demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into deep learning models. MATERIALS AND METHODS: We proposed a spatio-temporal attention network (STAN) for pandemic prediction. It uses an attention-based graph convolutional network to capture geographical and temporal trends and predict the number of cases for a fixed number of days into the future. We also designed a physical law-based loss term for enhancing long-term prediction. STAN was tested using both massive real-world patient data and open source COVID-19 statistics provided by Johns Hopkins university across all U.S. counties. RESULTS: STAN outperforms epidemiological modeling methods such as SIR and SEIR and deep learning models on both long-term and short-term predictions, achieving up to 87% lower mean squared error compared to the best baseline prediction model. CONCLUSIONS: By using information from real-world patient data and geographical data, STAN can better capture the disease status and medical resource utilization information and thus provides more accurate pandemic modeling. With pandemic transmission law based regularization, STAN also achieves good long-term prediction performance.

OBJECTIVE: The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks. MATERIALS AND METHODS: We construct the radiography (x-ray) imaging data from 2 publicly available sources, which include 5508 chest x-ray images across 2874 patients with 4 classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a FLANNEL (Focal Loss bAsed Neural Network EnsembLe) model, a flexible module to ensemble several convolutional neural network models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. RESULTS: FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score, with 6% relative increase on the COVID-19 identification task, in which it achieves precision of 0.7833 ± 0.07, recall of 0.8609 ± 0.03, and F1 score of 0.8168 ± 0.03. DISCUSSION: Ensemble learning that combines multiple independent basis classifiers can increase the robustness and accuracy. We propose a neural weighing module to learn the importance weight for each base model and combine them via weighted ensemble to get the final classification results. In order to handle the class imbalance challenge, we adapt focal loss to our multiple classification task as the loss function. CONCLUSION: FLANNEL effectively combines state-of-the-art convolutional neural network classification models and tackles class imbalance with focal loss to achieve better performance on COVID-19 detection from x-rays.

Massive electronic health records (EHRs) enable the success of learning accurate patient representations to support various predictive health applications. In contrast, doctor representation was not well studied despite that doctors play pivotal roles in healthcare. How to construct the right doctor representations? How to use doctor representation to solve important health analytic problems? In this work, we study the problem on clinical trial recruitment, which is about identifying the right doctors to help conduct the trials based on the trial description and patient EHR data of those doctors. We propose Doctor2Vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials. In particular, Doctor2Vec utilizes a dynamic memory network where the doctor's experience with patients are stored in the memory bank and the network will dynamically assign weights based on the trial representation via an attention mechanism. Validated on large real-world trials and EHR data including 2,609 trials, 25K doctors and 430K patients, Doctor2Vec demonstrated improved performance over the best baseline by up to 8.7% in PR-AUC. We also demonstrated that the Doctor2Vec embedding can be transferred to benefit data insufficiency settings including trial recruitment in less populated/newly explored country with 13.7% improvement or for rare diseases with 8.1% improvement in PR-AUC.

Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve detection? In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. CONAN combines ideas from both adversarial training and max-margin classification. It first learns self-attentive and hierarchical embedding for patient pattern characterization. Then, we develop a complementary generative adversarial networks (GAN) model to generate candidate positive and negative samples from the uncertain patients by encouraging a max-margin between classes. In addition, CONAN has a disease detector that serves as the discriminator during the adversarial training for identifying rare diseases. We evaluated CONAN on two disease detection tasks. For low prevalence inflammatory bowel disease (IBD) detection, CONAN achieved .96 precision recall area under the curve (PR-AUC) and 50.1% relative improvement over the best baseline. For rare disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22 PR-AUC with 41.3% relative improvement over the best baseline.

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and society. Although several computational models have been proposed for DDI prediction, there are still limitations: (1) specialized design of drug representation for DDI predictions is lacking; (2) predictions are based on limited labelled data and do not generalize well to unseen drugs or DDIs; and (3) models are characterized by a large number of parameters, thus are hard to interpret. In this work, we develop a ChemicAl SubstrucTurE Representation (CASTER) framework that predicts DDIs given chemical structures of drugs. CASTER aims to mitigate these limitations via (1) a sequential pattern mining module rooted in the DDI mechanism to efficiently characterize functional sub-structures of drugs; (2) an auto-encoding module that leverages both labelled and unlabelled chemical structure data to improve predictive accuracy and generalizability; and (3) a dictionary learning module that explains the prediction via a small set of coefficients which measure the relevance of each input sub-structures to the DDI outcome. We evaluated CASTER on two real-world DDI datasets and showed that it performed better than state-of-the-art baselines and provided interpretable predictions.

Automatic molecule optimization aims at generating a new molecule Y with more desirable properties based on an input molecule X. There are two learning strategies for molecule optimization: 1) Maximum Likelihood Estimation (MLE) methods that take a set of molecule pairs (X, Y ) as training data where X is the input molecule and Y is the enhanced molecule. However, such molecule pairs are not naturally available in molecule databases and has to be constructed using ad hoc heuristics, which limits the performance of MLE methods. 2) Reinforcement Learning (RL) methods, though bypass the need of molecule pairs as training data, suffer from poor exploration efficiency, especially in the early phase of learning. To address both challenges, we propose α-Molecular OPtimization (α-MOP), which uses α-divergence to unify both MLE and RL objectives automatically. In early phase it focuses more on maximum likelihood objective but gradually shifts more weight onto reinforcement learning objective. Evaluated on multiple datasets, α-MOP obtains success rate of 49.91% in QED, 49.32% in DRD2 and 56.43% in LogP, which outperforms both MLE and RL based molecule optimization methods.

Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks.

Generating clinical reports from raw recordings such as X-rays and electroencephalogram (EEG) is an essential and routine task for doctors. However, it is often time-consuming to write accurate and detailed reports. Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors’ preference. We propose CLinicAl Report Auto-completion (, an interactive method that generates reports in a sentence by sentence fashion based on doctors’ anchor words and partially completed sentences. earches for most relevant sentences from existing reports as the template for the current report. The retrieved sentences are sequentially modified by combining with the input feature representations to create the final report. In our experimental evaluation chieved 0.393 CIDEr and 0.248 BLEU-4 on X-ray reports and 0.482 CIDEr and 0.491 BLEU-4 for EEG reports for sentence-level generation, which is up to 35% improvement over the best baseline. Also via our qualitative evaluation, s shown to produce reports which have a significantly higher level of approval by doctors in a user study (3.74 out of 5 for s 2.52 out of 5 for the baseline).

Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease patterns from longitudinal patient data, but pay little attention to the disease progression stage itself. To fill the gap, we propose a Stage-aware neural Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-term memory (LSTM) module that extracts health stage variations unsupervisedly; (2) a stage-adaptive convolutional module that incorporates stage-related progression patterns into risk prediction. We evaluate StageNet on two real-world datasets and show that StageNet outperforms state-of-the-art models in risk prediction task and patient subtyping task. Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets. StageNet also achieves over 58% higher Calinski-Harabasz score (a cluster quality metric) for a patient subtyping task.

Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The core problem of patient-trial matching is to find qualified patients for a trial, where patient information is stored in electronic health records (EHR) while trial eligibility criteria (EC) are described in text documents available on the web. How to represent longitudinal patient EHR? How to extract complex logical rules from EC? Most existing works rely on manual rule-based extraction, which is time consuming and inflexible for complex inference. To address these challenges, we proposed a cross-modal inference learning model to jointly encode enrollment criteria (text) and patients records (tabular data) into a shared latent space for matching inference. pplies a pre-trained Bidirectional Encoder Representations from Transformers(BERT) model to encode clinical trial information into sentence embedding. And uses a hierarchical embedding model to represent patient longitudinal EHR. In addition, s augmented by a numerical information embedding and entailment module to reason over numerical information in both EC and EHR. These encoders are trained jointly to optimize patient-trial matching score. We evaluated n the trial-patient matching task with demonstrated on real world datasets. utperformed the best baseline by up to 12.4% in average F1.

OBJECTIVE: Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training 2 agents with different focuses: the primary agent studies the most recent visit of the patient to learn the current health status, and then the second-opinion agent considers the entire patient history to obtain a more global view. MATERIALS AND METHODS: Our approach Dr. Agent augments recurrent neural networks with 2 policy gradient agents. Moreover, Dr. Agent is customized with various patient demographics information and learns a dynamic skip connection to focus on the relevant information over time. We trained Dr. Agent to perform 4 clinical prediction tasks on the publicly available MIMIC-III (Medical Information Mart for Intensive Care) database: (1) in-hospital mortality prediction, (2) acute care phenotype classification, (3) physiologic decompensation prediction, and (4) forecasting length of stay. We compared the performance of Dr. Agent against 4 baseline clinical predictive models. RESULTS: Dr. Agent outperforms baseline clinical prediction models across all 4 tasks in terms of all metrics. Compared with the best baseline model, Dr. Agent achieves up to 15% higher area under the precision-recall curve on different tasks. CONCLUSIONS: Dr. Agent can comprehensively model the long-term dependencies of patients' health status while considering patients' demographics using 2 agents, and therefore achieves better prediction performance on different clinical prediction tasks.

OBJECTIVE: Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. MATERIALS AND METHODS: To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. RESULTS: Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. DISCUSSION: The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. CONCLUSION: Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.