Developing a machine learning-based short form of the positive and negative syndrome scale.

BACKGROUND AND HYPOTHESIS: The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS (PANSS-MLSF) that reproduces the PANSS scores. Moreover, the PANSS-MLSF estimated the removed-item scores. STUDY DESIGN: The PANSS-MLSF was developed using an artificial neural network, and the removed-item scores were estimated using the eXtreme Gradient Boosting classifier algorithm. The reliability of the PANSS-MLSF was examined using Cronbach's alpha. The concurrent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the PANSS. The convergent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the Clinical Global Impression-Severity, Mini-Mental State Examination, and Lawton Instrumental Activities of Daily Living Scale. The agreement of the estimated removed-item scores with their original scores was examined using Cohen's kappa. STUDY RESULTS: Our analysis included data from 573 patients with moderate severity. The two versions of the PANSS-MLSF comprised 15 items and 9 items were proposed. The PANSS-MLSF scores were similar to the PANSS scores (mean squared error=2.6-24.4 points). The reliability, concurrent validity, and convergent validity of the PANSS-MLSF were good. Moderate to good agreement between the estimated removed-item scores and the original item scores was found in 60% of the removed items. CONCLUSION: The PANSS-MLSF offers a viable way to reduce PANSS administration time, maintain score comparability, uphold reliability and validity, and even estimate scores for the removed items.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.

BACKGROUND: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE: This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS: This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS: From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS: We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.