mlsanityData to 14 Oct 2026

MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression

arXiv 2305.11742 ↗First posted 19 May 2023cs.LG cs.AIcited by 6

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Abstract (arXiv)

Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96 AUC-ROC and 0.81 AUC-PR, which exceeds the previous benchmark.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

14 of 31 references.

References · 14

  1. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  2. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  3. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
  4. 2017LSTM Fully Convolutional Networks for Time Series Classificationcited by 136
  5. 2018Multivariate LSTM-FCNs for Time Series Classificationcited by 120
  6. 2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
  7. 2019Using Clinical Notes with Time Series Data for ICU Managementcited by 38
  8. 2021Heterogeneous Similarity Graph Neural Network on Electronic Health Recordscited by 30
  9. 2022When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentationcited by 13
  10. 2023BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learningcited by 13
  11. 2023BotShape: A Novel Social Bots Detection Approach via Behavioral Patternscited by 11
  12. 2023FineEHR: Refine Clinical Note Representations to Improve Mortality Predictioncited by 9
2 more
  1. 2022Mitigating Health Disparities in EHR via Deconfoundercited by 7
  2. 2022Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patientscited by 1

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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  2. 2024Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detectioncited by 8
  3. 2023An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithmscited by 5
  4. 2024Debiasing Machine Unlearning with Counterfactual Examplescited by 3
  5. 2023A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfoundercited by 1
  6. 2025SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasetscited by 1