Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

Objectives In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.Methods We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review ", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. The review was registered on PROSPERO.ResultsFrom a total of 648 studies initially retrieved, 68 articles met the inclusion criteria.Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.Conclusions Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

Smart city environments, when applied to healthcare, improve the quality of people\u27s lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians\u27 daily work. Recommender systems are an alternative to automatically integrate the data generated in such environments with predictive models and recommend actions, content, or services. The data produced by smart devices are accurate and reliable for predictive and decision-making contexts. This study main purpose is to assist patients and doctors in the early detection of disease or prediction of postoperative worsening through constant monitoring. To achieve this objective, this study proposes an architecture for recommender systems applied to healthcare, which can prioritize emergency cases. The architecture brings an ensemble approach for prediction, which adopts multiple Machine Learning algorithms. The methodology used to carry out the study followed three steps. First, a systematic literature mapping, second, the construction and development of the architecture, and third, the evaluation through two case studies. The results demonstrated the feasibility of the proposal. The predictions are promising and adherent to the application context for accurate datasets with a low amount of noises or missing values

A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data

The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$$ CI: 0.870-0.935) in predicting ICU admission.Comment: 14 page

Machine Learning Framework for Real-World Electronic Health Records Regarding Missingness, Interpretability, and Fairness

Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations. This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing rates using multiple subsets with lower missing rates. The second method introduces the integration of medical knowledge graphs and double attention mechanism with the long short-term memory (LSTM) model to enhance interpretability by providing knowledge-based model interpretation. The third method develops an LSTM variant that integrates medical knowledge graphs and additional time-aware gates to handle multi-variable temporal missing issues and interpretability concerns. Finally, a transformer-based model is proposed to learn unbiased and fair representations of diverse subpopulations using domain classifiers and three attention mechanisms

Kidney Ailment Prediction under Data Imbalance

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases. In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related to class imbalance problem. This occurs when at least one of the classes are represented by significantly smaller number of samples than the others in the training set. The problem with imbalanced dataset is that the classifiers tend to classify all samples as majority class, ignoring the minority class samples. The second issue is on the specific type of data to be used for a given problem. Here, we focused on predicting kidney diseases based on patient information extracted from laboratory and questionnaire data. Most recent approaches for predicting kidney diseases or other chronic diseases rely on the usage of prescription drugs. In this study, we focus on biomarker and anthropometry data of patients to analyze and predict kidney-related diseases. In this research, we adopted a learning approach which involves repeated random data sub-sampling to tackle the class imbalance problem. This technique divides the samples into multiple sub-samples, while keeping each training sub-sample completely balanced. We then trained classification models on the balanced data to predict the risk of kidney failure. Further, we developed an intelligent fusion mechanism to combine information from both the biomarker and anthropometry data sets for improved prediction accuracy and stability. Results are included to demonstrate the performance

Imbalance Learning and Its Application on Medical Datasets

To gain more valuable information from the increasing large amount of data, data mining has been a hot topic that attracts growing attention in this two decades. One of the challenges in data mining is imbalance learning, which refers to leaning from imbalanced datasets. The imbalanced datasets is dominated by some classes (majority) and other under-represented classes (minority). The imbalanced datasets degrade the learning ability of traditional methods, which are designed on the assumption that all classes are balanced and have equal misclassification costs, leading to the poor performance on the minority classes. This phenomenon is usually called the class imbalance problem. However, it is usually the minority classes of more interest and importance, such as sick cases in the medical dataset. Additionally, traditional methods are optimized to achieve maximum accuracy, which is not suitable for evaluating the performance on imbalanced datasets. From the view of data space, class imbalance could be classified as extrinsic imbalance and intrinsic imbalance. Extrinsic imbalance is caused by external factors, such as data transmission or data storage, while intrinsic imbalance means the dataset is inherently imbalanced due to its nature.  As extrinsic imbalance could be fixed by collecting more samples, this thesis mainly focus on on two scenarios of the intrinsic imbalance,  machine learning for imbalanced structured datasets and deep learning for imbalanced image datasets.  Normally, the solutions for the class imbalance problem are named as imbalance learning methods, which could be grouped into data-level methods (re-sampling), algorithm-level (re-weighting) methods and hybrid methods. Data-level methods modify the class distribution of the training dataset to create balanced training sets, and typical examples are over-sampling and under-sampling. Instead of modifying the data distribution, algorithm-level methods adjust the misclassification cost to alleviate the class imbalance problem, and one typical example is cost sensitive methods. Hybrid methods usually combine data-level methods and algorithm-level methods. However, existing imbalance learning methods encounter different kinds of problems. Over-sampling methods increase the minority samples to create balanced training sets, which might lead the trained model overfit to the minority class. Under-sampling methods create balanced training sets by discarding majority samples, which lead to the information loss and poor performance of the trained model. Cost-sensitive methods usually need assistance from domain expert to define the misclassification costs which are task specified. Thus, the generalization ability of cost-sensitive methods is poor. Especially, when it comes to the deep learning methods under class imbalance, re-sampling methods may introduce large computation cost and existing re-weighting methods could lead to poor performance. The object of this dissertation is to understand features difference under class imbalance, to improve the classification performance on structured datasets or image datasets. This thesis proposes two machine learning methods for imbalanced structured datasets and one deep learning method for imbalance image datasets. The proposed methods are evaluated on several medical datasets, which are intrinsically imbalanced.  Firstly, we study the feature difference between the majority class and the minority class of an imbalanced medical dataset, which is collected from a Chinese hospital. After data cleaning and structuring, we get 3292 kidney stone cases treated by Percutaneous Nephrolithonomy from 2012 to 2019. There are 651 (19.78% ) cases who have postoperative complications, which makes the complication prediction an imbalanced classification task. We propose a sampling-based method SMOTE-XGBoost and implement it to build a postoperative complication prediction model. Experimental results show that the proposed method outperforms classic machine learning methods. Furthermore, traditional prediction models of Percutaneous Nephrolithonomy are designed to predict the kidney stone status and overlook complication related features, which could degrade their prediction performance on complication prediction tasks. To this end, we merge more features into the proposed sampling-based method and further improve the classification performance. Overall, SMOTE-XGBoost achieves an AUC of 0.7077 which is 41.54% higher than that of S.T.O.N.E. nephrolithometry, a traditional prediction model of Percutaneous Nephrolithonomy. After reviewing the existing machine learning methods under class imbalance, we propose a novel ensemble learning approach called Multiple bAlance Subset Stacking (MASS). MASS first cuts the majority class into multiple subsets by the size of the minority set, and combines each majority subset with the minority set as one balanced subsets. In this way, MASS could overcome the problem of information loss because it does not discard any majority sample. Each balanced subset is used to train one base classifier. Then, the original dataset is feed to all the trained base classifiers, whose output are used to generate the stacking dataset. One stack model is trained by the staking dataset to get the optimal weights for the base classifiers. As the stacking dataset keeps the same labels as the original dataset, which could avoid the overfitting problem. Finally, we can get an ensembled strong model based on the trained base classifiers and the staking model. Extensive experimental results on three medical datasets show that MASS outperforms baseline methods.  The robustness of MASS is proved over implementing different base classifiers. We design a parallel version MASS to reduce the training time cost. The speedup analysis proves that Parallel MASS could reduce training time cost greatly when applied on large datasets. Specially, Parallel MASS reduces 101.8% training time compared with MASS at most in our experiments.  When it comes to the class imbalance problem of image datasets, existing imbalance learning methods suffer from the problem of large training cost and poor performance.  After introducing the problem of implementing resampling methods on image classification tasks, we demonstrate issues of re-weighting strategy using class frequencies through the experimental result on one medical image dataset.  We propose a novel re-weighting method Hardness Aware Dynamic loss to solve the class imbalance problem of image datasets. After each training epoch of deep neural networks, we compute the classification hardness of each class. We will assign higher class weights to the classes have large classification hardness values and vice versa in the next epoch. In this way, HAD could tune the weight of each sample in the loss function dynamically during the training process. The experimental results prove that HAD significantly outperforms the state-of-the-art methods. Moreover, HAD greatly improves the classification accuracies of minority classes while only making a small compromise of majority class accuracies. Especially, HAD loss improves 10.04% average precision compared with the best baseline, Focal loss, on the HAM10000 dataset. At last, I conclude this dissertation with our contributions to the imbalance learning, and provide an overview of potential directions for future research, which include extensions of the three proposed methods, development of task-specified algorithms, and fixing the challenges of within-class imbalance.2021-06-0

Reconstrução e classificação de sequências de ADN desconhecidas

The continuous advances in DNA sequencing technologies and techniques in metagenomics require reliable reconstruction and accurate classification methodologies for the diversity increase of the natural repository while contributing to the organisms' description and organization. However, after sequencing and de-novo assembly, one of the highest complex challenges comes from the DNA sequences that do not match or resemble any biological sequence from the literature. Three main reasons contribute to this exception: the organism sequence presents high divergence according to the known organisms from the literature, an irregularity has been created in the reconstruction process, or a new organism has been sequenced. The inability to efficiently classify these unknown sequences increases the sample constitution's uncertainty and becomes a wasted opportunity to discover new species since they are often discarded. In this context, the main objective of this thesis is the development and validation of a tool that provides an efficient computational solution to solve these three challenges based on an ensemble of experts, namely compression-based predictors, the distribution of sequence content, and normalized sequence lengths. The method uses both DNA and amino acid sequences and provides efficient classification beyond standard referential comparisons. Unusually, it classifies DNA sequences without resorting directly to the reference genomes but rather to features that the species biological sequences share. Specifically, it only makes use of features extracted individually from each genome without using sequence comparisons. RFSC was then created as a machine learning classification pipeline that relies on an ensemble of experts to provide efficient classification in metagenomic contexts. This pipeline was tested in synthetic and real data, both achieving precise and accurate results that, at the time of the development of this thesis, have not been reported in the state-of-the-art. Specifically, it has achieved an accuracy of approximately 97% in the domain/type classification.Os contínuos avanços em tecnologias de sequenciação de ADN e técnicas em meta genómica requerem metodologias de reconstrução confiáveis e de classificação precisas para o aumento da diversidade do repositório natural, contribuindo, entretanto, para a descrição e organização dos organismos. No entanto, após a sequenciação e a montagem de-novo, um dos desafios mais complexos advém das sequências de ADN que não correspondem ou se assemelham a qualquer sequencia biológica da literatura. São três as principais razões que contribuem para essa exceção: uma irregularidade emergiu no processo de reconstrução, a sequência do organismo é altamente dissimilar dos organismos da literatura, ou um novo e diferente organismo foi reconstruído. A incapacidade de classificar com eficiência essas sequências desconhecidas aumenta a incerteza da constituição da amostra e desperdiça a oportunidade de descobrir novas espécies, uma vez que muitas vezes são descartadas. Neste contexto, o principal objetivo desta tese é fornecer uma solução computacional eficiente para resolver este desafio com base em um conjunto de especialistas, nomeadamente preditores baseados em compressão, a distribuição de conteúdo de sequência e comprimentos de sequência normalizados. O método usa sequências de ADN e de aminoácidos e fornece classificação eficiente além das comparações referenciais padrão. Excecionalmente, ele classifica as sequências de ADN sem recorrer diretamente a genomas de referência, mas sim às características que as sequências biológicas da espécie compartilham. Especificamente, ele usa apenas recursos extraídos individualmente de cada genoma sem usar comparações de sequência. Além disso, o pipeline é totalmente automático e permite a reconstrução sem referência de genomas a partir de reads FASTQ com a garantia adicional de armazenamento seguro de informações sensíveis. O RFSC é então um pipeline de classificação de aprendizagem automática que se baseia em um conjunto de especialistas para fornecer classificação eficiente em contextos meta genómicos. Este pipeline foi aplicado em dados sintéticos e reais, alcançando em ambos resultados precisos e exatos que, no momento do desenvolvimento desta dissertação, não foram relatados na literatura. Especificamente, esta ferramenta desenvolvida, alcançou uma precisão de aproximadamente 97% na classificação de domínio/tipo.Mestrado em Engenharia de Computadores e Telemátic

GRU-D-Weibull: A Novel Real-Time Individualized Endpoint Prediction

Accurate prediction models for individual-level endpoints and time-to-endpoints are crucial in clinical practice. In this study, we propose a novel approach, GRU-D-Weibull, which combines gated recurrent units with decay (GRU-D) to model the Weibull distribution. Our method enables real-time individualized endpoint prediction and population-level risk management. Using a cohort of 6,879 patients with stage 4 chronic kidney disease (CKD4), we evaluated the performance of GRU-D-Weibull in endpoint prediction. The C-index of GRU-D-Weibull was ~0.7 at the index date and increased to ~0.77 after 4.3 years of follow-up, similar to random survival forest. Our approach achieved an absolute L1-loss of ~1.1 years (SD 0.95) at the CKD4 index date and a minimum of ~0.45 years (SD0.3) at 4 years of follow-up, outperforming competing methods significantly. GRU-D-Weibull consistently constrained the predicted survival probability at the time of an event within a smaller and more fixed range compared to other models throughout the follow-up period. We observed significant correlations between the error in point estimates and missing proportions of input features at the index date (correlations from ~0.1 to ~0.3), which diminished within 1 year as more data became available. By post-training recalibration, we successfully aligned the predicted and observed survival probabilities across multiple prediction horizons at different time points during follow-up. Our findings demonstrate the considerable potential of GRU-D-Weibull as the next-generation architecture for endpoint risk management, capable of generating various endpoint estimates for real-time monitoring using clinical data.Comment: 30 pages, 7 figures, 4 supplementary figure