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1. Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

   https://doi.org/10.3389/fdgth.2021.640685  

   Shickel, Benjamin; Davoudi, Anis; Ozrazgat-Baslanti, Tezcan; Ruppert, Matthew; Bihorac, Azra; Rashidi, Parisa (February 2021, Frontiers in Digital Health)

   Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients. 

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2. Blockchain Framework for Secured On-Demand Patient Health Records Sharing

   Abouali, Meryem; Sharma, Kartikeya; Ajayi, Oluwaseyi; Saadawi, Tarek (December 2021, IEEE UEMCON2021)

   The healthcare sector is constantly improving patient health record systems. However, these systems face a significant challenge when confronted with patient health record (PHR) data due to its sensitivity. In addition, patient’s data is stored and spread generally across various healthcare facilities and among providers. This arrangement of distributed data becomes problematic whenever patients want to access their health records and then share them with their care provider, which yields a lack of interoperability among various healthcare systems. Moreover, most patient health record systems adopt a centralized management structure and deploy PHRs to the cloud, which raises privacy concerns when sharing patient information over a network. Therefore, it is vital to design a framework that considers patient privacy and data security when sharing sensitive information with healthcare facilities and providers. This paper proposes a blockchain framework for secured patient health records sharing that allows patients to have full access and control over their health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system, and the NuCypher protocol, which functions as key management and blockchain-based proxy re-encryption to create a secured on-demand patient health records sharing system effectively. Results show that the proposed framework is more secure than other schemes, and the PHRs will not be accessible to unauthorized providers or users. In addition, all encrypted data will only be accessible to and readable by verified entities set by the patient. 

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3. Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

   https://doi.org/10.24963/ijcai.2021/486  

   Lu, Chang; Reddy, Chandan K; Chakraborty, Prithwish; Kleinberg, Samantha; Ning, Yue (August 2021, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence)

   Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep-learning-based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured features. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention manipulating strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies. 

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4. Bayesian mixed-effect higher-order hidden Markov models with applications to predictive healthcare using electronic health records

   https://doi.org/10.1080/24725854.2024.2302368  

   Liao, Ying; Xiang, Yisha; Zhao, Zhigen; Ai, Di (February 2024, IISE Transactions)

   Yu_Ding_Georgia_Institute_of_Technology (Ed.)

   The disease progression dynamics observed in electronic health records often reflect patients’ health condition evolution, holding the promise of enabling the development of clinical predictive models. These dynamics, however, generally display significant variability among patients, due to some critical factors (e.g., gender and age) and patient-level heterogeneity. Moreover, future health state may not only depend on the current state, but also more distant history states due to the complicated disease progression. To capture this complex transition behavior and address mixed effects in clinical prediction problems, we propose a novel and flexible Bayesian Mixed-Effect Higher-Order Hidden Markov Model (MHOHMM), and develop a classifier based on MHOHMMs. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the k-fold cross-validation approach. An effective two-stage Markov chain Monte Carlo (MCMC) sampling algorithm is designed for model inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classification method. The practical utility of the proposed framework is demonstrated by a case study on the acute hypotensive episode prediction for intensive care unit patients. Our results show that the MHOHMM-based framework provides good prediction performance. 

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5. Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data

   https://doi.org/10.1055/s-0040-1702214  

   Feller, Daniel J.; Bear Don't Walk IV, Oliver J.; Zucker, Jason; Yin, Michael T.; Gordon, Peter; Elhadad, Noémie (January 2020, Applied Clinical Informatics)

   Abstract Background Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that often impede disease management and result in sexually transmitted infections. Despite their importance, SBDH are inconsistently documented in electronic health records (EHRs) and typically collected only in an unstructured format. Evidence suggests that structured data elements present in EHRs can contribute further to identify SBDH in the patient record. Objective Explore the automated inference of both the presence of SBDH documentation and individual SBDH risk factors in patient records. Compare the relative ability of clinical notes and structured EHR data, such as laboratory measurements and diagnoses, to support inference. Methods We attempt to infer the presence of SBDH documentation in patient records, as well as patient status of 11 SBDH, including alcohol abuse, homelessness, and sexual orientation. We compare classification performance when considering clinical notes only, structured data only, and notes and structured data together. We perform an error analysis across several SBDH risk factors. Results Classification models inferring the presence of SBDH documentation achieved good performance (F1 score: 92.7–78.7; F1 considered as the primary evaluation metric). Performance was variable for models inferring patient SBDH risk status; results ranged from F1 = 82.7 for LGBT (lesbian, gay, bisexual, and transgender) status to F1 = 28.5 for intravenous drug use. Error analysis demonstrated that lexical diversity and documentation of historical SBDH status challenge inference of patient SBDH status. Three of five classifiers inferring topic-specific SBDH documentation and 10 of 11 patient SBDH status classifiers achieved highest performance when trained using both clinical notes and structured data. Conclusion Our findings suggest that combining clinical free-text notes and structured data provide the best approach in classifying patient SBDH status. Inferring patient SBDH status is most challenging among SBDH with low prevalence and high lexical diversity. 

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