Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression.

Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.

Incremental Learning of Human Activities in Smart Homes.

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques.

Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critical for suicide prevention. However, there are challenges associated with conventional suicide-risk screening methods. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between 2018 and 2022. The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection.

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier.

While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital's Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.

Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook.

Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study's objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews.

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals' Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

Facebook Reviews as a Supplemental Tool for Hospital Patient Satisfaction and Its Relationship with Hospital Accreditation in Malaysia.

Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman's correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions' quality of care in real time.

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes.

Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant's activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.