Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Gong, Jen J.; Naumann, Tristan; Szolovits, Peter; Guttag, John V.

doi:10.1145/3097983.3098064

Cited by 32 publications

(25 citation statements)

References 6 publications

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“…Given the assessment of the current state of the patient (ie, patient profile, physiology, neuroimaging, blood biomarkers, and physiologic testing) along with the therapeutic scheme, a digital intervention would use ML or predictive systems to infer disease evolution or remission to be able to guide subsequent therapy scheme planning [ 83 ]. Such a trajectory can also support the detection of behavior change in patients [ 84 ].…”

Section: Resultsmentioning

confidence: 99%

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

Kondylakis¹,

Axenie²,

Bastola³

et al. 2020

J Med Internet Res

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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.

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Section: Resultsmentioning

confidence: 99%

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

Kondylakis¹,

Axenie²,

Bastola³

et al. 2020

J Med Internet Res

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“…Additionally, unusual or faulty cases, such as readings with multiple decimal places are designated as discrete, making our model robust to and aware of consistent errors which potentially correlate with patient outcomes. Unlike prior work 22 , we make use of missing and numerical values. Missing readings are considered as separate discrete events so that our model can capitalise on 'informative missingness' 6,24 .…”

Section: Methodsmentioning

confidence: 99%

“…Developing a system which obviates the need for such a delay would be highly advantageous in terms of local training and deployment. Attempts to incorporate a broader set of variables have used pretrained word embeddings from outside of the medical context and have avoided numerical values 22 . In the literature, we have found no examples of models which perform zero variable selection, data processing, or model ensembling.…”

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confidence: 99%

Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

Deasy

Lió

Ercole

2020

Sci Rep

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Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient’s stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83–0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.

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“…Authors in [30] discussed problems due to the lack of consistency in how semantically equivalent information is encoded in different ICU databases. Authors in [26] discussed the problem of imbalanced ICU data, which occurs when one of the possible patient outcomes is significantly underrepresented in the data.…”

Section: B Icu Domains and Sub-populationsmentioning

confidence: 99%

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Alves

Laender

Veloso

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning.

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Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Cited by 32 publications

References 6 publications

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

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

Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

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