Holistic health care (HHC) is a synonym for complete patient care, and as such an efficient clinical decision support system (CDSS) for HHC is critical to support the judgement of physician’s decision in response of patient’s physical, emotional, social, economic, and spiritual needs. The field of artificial intelligence (AI) has evolved considerably in the past decades and many AI applications have been deployed in various contexts. Therefore, this study aims to propose an AI-assisted CDSS model that predicts patients in need of HHC and applies an improved recurrent neural network (RNN) model, long short-term memory (LSTM) for the prediction. The data sources include in-patient’s comorbidity status and daily vital sign attributes such as blood pressure, heart rate, oxygen prescription, etc. A two-year dataset consisting of 121 thousand anonymized patient cases with 890 thousand physiological medical records was obtained from a medical center in Taiwan for system evaluation. Comparing with the rule-based expert system, the proposed AI-assisted CDSS improves sensitivity from 26.44% to 80.84% and specificity from 99.23% to 99.95%. The experimental results demonstrate that an AI-assisted CDSS could efficiently predict HHC patients.

User name and password are the most commonly used user authentication scheme in information systems. Strong passwords are secure but difficult to remember, so many users prefer easy-to-remember passwords. These weak passwords are also easily guessed by attackers, leading to online password guessing attacks, posing a serious security threat to information systems. Providing a reliable user authentication scheme to allow legitimate users to login while preventing online password guessing attacks is a challenge. We define a formal statistical model for the behavior of users and attackers, and differentiate users and attackers according to this model. The proposed solution computes the entropy of the passwords entered by the user and considers the user legitimate only if the entropy does not exceed a threshold. We show that entropy is an effective feature to distinguish legitimate users from attackers. We also show that the proposed user authentication scheme is effective in identifying password guessing attacks, even if the user chooses a common password. The new scheme adds an extra layer of protection to passwords, which is especially important for weak passwords. The scheme is a slight modification of the existing scheme, so it can be easily integrated into existing systems.

Background: Overcrowded emergency rooms might degrade the quality of care and overload the clinic staff. Assessing unscheduled return visits (URVs) to the emergency department (ED) is a quality assurance procedure to identify ED-discharged patients with a high likelihood of bounce-back, to ensure patient safety, and ultimately to reduce medical costs by decreasing the frequency of URVs. The field of machine learning (ML) has evolved considerably in the past decades, and many ML applications have been deployed in various contexts. Objective: This study aims to develop an ML-assisted framework that identifies high-risk patients who may revisit the ED within 72 hours after the initial visit. Furthermore, this study evaluates different ML models, feature sets, and feature encoding methods in order to build an effective prediction model. Methods: This study proposes an ML-assisted system that extracts the features from both structured and unstructured medical data to predict patients who are likely to revisit the ED, where the structured data includes patients’ electronic health records, and the unstructured data is their medical notes (subjective, objective, assessment, and plan). A 5-year dataset consisting of 184,687 ED visits, along with 324,111 historical electronic health records and the associated medical notes, was obtained from Kaohsiung Veterans General Hospital, a tertiary medical center in Taiwan, to evaluate the proposed system. Results: The evaluation results indicate that incorporating convolutional neural network–based feature extraction from unstructured ED physician narrative notes, combined with structured vital signs and demographic data, significantly enhances predictive performance. The proposed approach achieves an area under the receiver operating characteristic curve of 0.705 and a recall of 0.718, demonstrating its effectiveness in predicting URVs. These findings highlight the potential of integrating structured and unstructured clinical data to improve predictive accuracy in this context. Conclusions: The study demonstrates that an ML-assisted framework may be applied as a decision support tool to assist ED clinicians in identifying revisiting patients, although the model’s performance may not be sufficient for clinic implementation. Given the improvement in the area under the receiver operating characteristic curve, the proposed framework should be further explored as a workable decision support tool to pinpoint ED patients with a high risk of revisit and provide them with appropriate and timely care.