Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy

BACKGROUND

Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive models from the routine follow-up clinical interviews, which are irregular sequences of multivariate time series data.

OBJECTIVES

To demonstrate that deep learning can incorporate patient time series follow-up data to improve prediction of major bleeding.

METHODS

We used the baseline and follow-up data that were collected over 8 years in a longitudinal cohort study of 2542 patients, of whom 118 had major bleeding. Four supervised neural network-based machine-learning models were trained on the baseline, follow-up, or both datasets using 70% of the data. The performance of these models was evaluated, along with modified versions of 6 previously developed clinical models, on the remaining 30% of the data.

RESULTS

An ensemble of feedforward and recurrent neural networks that used the baseline and follow-up data was the best-performing model, achieving a sensitivity and a specificity of 61% and 82%, respectively, in identifying major bleeding, and it outperformed the previously developed clinical models in terms of area under the receiver operating characteristic curve (82%) and area under the precision-recall curve (14%).

CONCLUSION

Time series follow-up data can improve major bleeding prediction in patients on extended anticoagulation therapy.

Longitudinal machine learning uncouples healthy aging factors from chronic disease risks

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.

The added value of text from Dutch general practitioner notes in predictive modeling

OBJECTIVE

This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.

MATERIALS AND METHODS

We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems.

RESULTS

On average, over all the different text representations and prediction algorithms, models only using text data performed better or similar to models using structured data alone in 2 prediction tasks. Additionally, in these 2 tasks, the combination of structured and text data outperformed models using structured or text data alone. No large performance differences were found between the different text representations and prediction algorithms.

DISCUSSION

Our findings indicate that the use of unstructured data alone can result in well-performing prediction models for some clinical prediction problems. Furthermore, the performance improvement achieved by combining structured and text data highlights the added value. Additionally, we demonstrate the significance of clinical natural language processing research in languages other than English and the possibility of validating text-based prediction models across various EHR systems.

CONCLUSION

Our study highlights the potential benefits of incorporating unstructured data in clinical prediction models in a GP setting. Although the added value of unstructured data may vary depending on the specific prediction task, our findings suggest that it has the potential to enhance patient care.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

Principles and challenges of modeling temporal and spatial omics data

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.

Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach

Background

In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic.

Methods

This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity.

Results

We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system's sensitivity while producing more stable predictions.

Conclusions

We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data.

Actual timing versus GPs' perceptions of optimal timing of advance care planning: a mixed-methods health record-based study

BACKGROUND

Timely initiation of advance care planning (ACP) in general practice is challenging, especially in patients with non-malignant conditions. Our aim was to investigate how perceived optimal timing of ACP initiation and its triggers relate to recorded actual timing in patients with cancer, organ failure, or multimorbidity.

METHODS

In this mixed-methods study in the Netherlands, we analysed health records selected from a database with primary care routine data and with a recorded ACP conversation in the last two years before death of patients who died with cancer, organ failure, or multimorbidity. We compared actual timing of ACP initiation as recorded in health records of 51 patients with the perceived optimal timing as determined by 83 independent GPs who studied these records. Further, to identify and compare triggers for GPs to initiate ACP, we analysed the health record documentation around the moments of the recorded actual timing of ACP initiation and the perceived optimal timing of ACP initiation. We combined quantitative descriptive statistics with qualitative content analysis.

RESULTS

The recorded actual timing of ACP initiation was significantly closer to death than the perceived optimal timing in patients with cancer (median 88 vs. 111 days before death (p = 0.049)), organ failure (227 vs. 306 days before death (p = 0.02)) and multimorbidity (113 vs. 338 days before death (p = 0.006)). Triggers for recorded actual versus perceived optimal timing were similar across the three groups, the most frequent being 'expressions of patients' reflections or wishes' (14% and 14% respectively) and 'appropriate setting' (10% and 13% respectively).

CONCLUSION

ACP in general practice was initiated and recorded later in the illness trajectory than considered optimal, especially in patients with organ failure or multimorbidity. As triggers were similar for recorded actual and perceived optimal timing, we recommend that GPs initiate ACP shortly after a trigger is noticed the first time, rather than wait for additional or more evident triggers when the illness is in an advanced stage.

Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)

Background

Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.

Objective

This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.

Methods

The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.

Results

We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.

Conclusions

We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

Current Trends in Readmission Prediction: An Overview of Approaches

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.

AI-based clinical decision-making systems in palliative medicine: ethical challenges

BACKGROUND

Improving palliative care (PC) is demanding due to the increase in people with PC needs over the next few years. An early identification of PC needs is fundamental in the care approach: it provides effective patient-centred care and could improve outcomes such as patient quality of life, reduction of the overall length of hospitalisation, survival rate prolongation, the satisfaction of both the patients and caregivers and cost-effectiveness.

METHODS

We reviewed literature with the objective of identifying and discussing the most important ethical challenges related to the implementation of AI-based data processing services in PC and advance care planning.

RESULTS

AI-based mortality predictions can signal the need for patients to obtain access to personalised communication or palliative care consultation, but they should not be used as a unique parameter to activate early PC and initiate an ACP. A number of factors must be included in the ethical decision-making process related to initiation of ACP conversations, among which are autonomy and quality of life, the risk of worsening healthcare status, the commitment by caregivers, the patients' psychosocial and spiritual distress and their wishes to initiate EOL discussions CONCLUSIONS: Despite the integration of artificial intelligence (AI)-based services into routine healthcare practice could have a positive effect of promoting early activation of ACP by means of a timely identification of PC needs, from an ethical point of view, the provision of these automated techniques raises a number of critical issues that deserve further exploration.

Health-adjusted life expectancy (HALE) in Chongqing, China, 2017: An artificial intelligence and big data method estimating the burden of disease at city level

BACKGROUND

A universally applicable approach that provides standard HALE measurements for different regions has yet to be developed because of the difficulties of health information collection. In this study, we developed a natural language processing (NLP) based HALE estimation approach by using individual-level electronic medical records (EMRs), which made it possible to calculate HALE timely in different temporal or spatial granularities.

METHODS

We performed diagnostic concept extraction and normalisation on 13•99 million EMRs with NLP to estimate the prevalence of 254 diseases in WHO Global Burden of Disease Study (GBD). Then, we calculated HALE in Chongqing, 2017, by using the life table technique and Sullivan's method, and analysed the contribution of diseases to the expected years "lost" due to disability (DLE).

FINDINGS

Our method identified a life expectancy at birth (LE0) of 77•9 years and health-adjusted life expectancy at birth (HALE0) of 71•7 years for the general Chongqing population of 2017. In particular, the male LE0 and HALE0 were 76•3 years and 68•9 years, respectively, while the female LE0 and HALE0 were 80•0 years and 74•4 years, respectively. Cerebrovascular diseases, cancers, and injuries were the top three deterioration factors, which reduced HALE by 2•67, 2•15, and 1•19 years, respectively.

INTERPRETATION

The results demonstrated the feasibility and effectiveness of EMRs-based HALE estimation. Moreover, the method allowed for a potentially transferable framework that facilitated a more convenient comparison of cross-sectional and longitudinal studies on HALE between regions. In summary, this study provided insightful solutions to the global ageing and health problems that the world is facing.

FUNDING

National Key R and D Program of China (2018YFC2000400).

An interpretable risk prediction model for healthcare with pattern attention

BACKGROUND

The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models' performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events.

METHODS

In this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don't need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks.

RESULTS

We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality.

CONCLUSIONS

PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients' health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions.

AVAILABILITY

The code for this paper is available at: https://github.com/yinchangchang/PAVE .

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

BACKGROUND

The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed.

OBJECTIVE

This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects.

METHODS

Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed.

RESULTS

Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work.

CONCLUSIONS

Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing

The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

Data-Driven Lexical Normalization for Medical Social Media

In the medical domain, user-generated social media text is increasingly used as a valuablecomplementary knowledge source to scientific medical literature. The extraction of this knowledge iscomplicated by colloquial language use and misspellings. However, lexical normalization of suchdata has not been addressed effectively. This paper presents a data-driven lexical normalizationpipeline with a novel spelling correction module for medical social media. Our method significantlyoutperforms state-of-the-art spelling correction methods and can detect mistakes with an F1 of 0.63despite extreme imbalance in the data. We also present the first corpus for spelling mistake detectionand correction in a medical patient forum.