Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system.

PURPOSE: The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The purpose of this study is to utilize artificial neural network (ANN) modeling to predict survival outcomes according to initial treatment modality and to develop an online decision-making support system. METHODS: Data were collected retrospectively from 7267 patients diagnosed with PCa between January 1988 and December 2017. The analyses included 19 pretreatment clinicopathological covariates. Multilayer perceptron (MLP), MLP for N-year survival prediction (MLP-N), and long short-term memory (LSTM) ANN models were used to analyze progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS), according to initial treatment modality. The performances of the ANN and the Cox-proportional hazards regression models were compared using Harrell's C-index. RESULTS: The ANN models provided higher predictive power for 5- and 10-year progression to CRPC-free survival, CSS, and OS compared to the Cox-proportional hazards regression model. The LSTM model achieved the highest predictive power, followed by the MLP-N, and MLP models. We developed an online decision-making support system based on the LSTM model to provide individualized survival outcomes at 5 and 10 years, according to the initial treatment strategy. CONCLUSION: The LSTM ANN model may provide individualized survival outcomes of PCa according to initial treatment strategy. Our online decision-making support system can be utilized by patients and health-care providers to determine the optimal initial treatment modality and to guide survival predictions.

Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning.

Objective: Social anxiety disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships. This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of these symptoms and to elucidate how the main predictive digital phenotypes differed depending on the symptom severity. Method: We collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive features. Results: Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of severity. Conclusions: Leveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival.

PURPOSE: Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. MATERIALS AND METHODS: The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS). RESULTS: Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. CONCLUSION: The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.