A multiple measurements case-based reasoning method for predicting recurrent status of liver cancer patients

Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes

BACKGROUND

Acute diseases present severe complications that develop rapidly, exhibit distinct phenotypes, and have profound effects on patient outcomes. Predictive analytics can enhance physicians' care and management of patients with acute diseases by predicting crucial complication phenotypes for a timely diagnosis and treatment. However, effective phenotype predictions require several challenges to be overcome. First, patient data collected in the early stages of an acute disease (eg, clinical data and laboratory results) are less informative for predicting phenotypic outcomes. Second, patient data are temporal and heterogeneous; for example, patients receive laboratory tests at different time intervals and frequencies. Third, imbalanced distributions of patient outcomes create additional complexity for predicting complication phenotypes.

OBJECTIVE

To predict crucial complication phenotypes among patients with acute diseases, we propose a novel, deep learning-based method that uses recurrent neural network-based sequence embedding to represent disease progression while considering temporal heterogeneities in patient data. Our method incorporates a latent regulator to alleviate data insufficiency constraints by accounting for the underlying mechanisms that are not observed in patient data. The proposed method also includes cost-sensitive learning to address imbalanced outcome distributions in patient data for improved predictions.

METHODS

From a major health care organization in Taiwan, we obtained a sample of 10,354 electronic health records that pertained to 6545 patients with peritonitis. The proposed method projects these temporal, heterogeneous, and clinical data into a substantially reduced feature space and then incorporates a latent regulator (latent parameter matrix) to obviate data insufficiencies and account for variations in phenotypic expressions. Moreover, our method employs cost-sensitive learning to further increase the predictive performance.

RESULTS

We evaluated the efficacy of the proposed method for predicting two hepatic complication phenotypes in patients with peritonitis: acute hepatic encephalopathy and hepatorenal syndrome. The following three benchmark techniques were evaluated: temporal multiple measurement case-based reasoning (MMCBR), temporal short long-term memory (T-SLTM) networks, and time fusion convolutional neural network (CNN). For acute hepatic encephalopathy predictions, our method attained an area under the curve (AUC) value of 0.82, which outperforms temporal MMCBR by 64%, T-SLTM by 26%, and time fusion CNN by 26%. For hepatorenal syndrome predictions, our method achieved an AUC value of 0.64, which is 29% better than that of temporal MMCBR (0.54). Overall, the evaluation results show that the proposed method significantly outperforms all the benchmarks, as measured by recall, F-measure, and AUC while maintaining comparable precision values.

CONCLUSIONS

The proposed method learns a short-term temporal representation from patient data to predict complication phenotypes and offers greater predictive utilities than prevalent data-driven techniques. This method is generalizable and can be applied to different acute disease (illness) scenarios that are characterized by insufficient patient clinical data availability, temporal heterogeneities, and imbalanced distributions of important patient outcomes.

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

BACKGROUND

Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers.

METHODS

MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method.

RESULTS

The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change.

CONCLUSIONS

The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach

Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009–2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.

Predicting hepatocellular carcinoma recurrences: A data-driven multiclass classification method incorporating latent variables

Hepatocellular carcinoma (HCC), a malignant form of cancer, is frequently treated with surgical resections, which have relatively high recurrence rates. Effective recurrence predictions enable physicians' timely detections and adequate therapeutic measures that can greatly improve patient care and outcomes. Toward that end, predictions of early versus late HCC recurrences should be considered separately to reflect their distinct onset time horizons, clinical causes, underlying clinical etiology, and pathogenesis. We propose a novel Bayesian network-based method to predict different HCC recurrence outcomes by considering the respective recurrence evolution paths. Typical patient information obtained in early stages is insufficiently informative to predict recurrence outcomes accurately, due to the lack of subsequent patient progression information. Our method alleviates such information deficiency constraints by incorporating an independent latent variable, dominant recurrence type, to regulate recurrence outcome predictions (early, late, or no recurrence). We use a real-world HCC data set to evaluate the proposed method, relative to three prevalent benchmark techniques. Overall, the results show that our method consistently and significantly outperforms all the benchmark techniques in terms of accuracy, precision, recall, and F-measures. For increased robustness, we use another data set to perform an out-of-sample evaluation and obtain similar results. This study thus contributes to HCC recurrence research and offers several implications for clinical practice.

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.