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.

Interventions for hidradenitis suppurativa: a Cochrane systematic review incorporating GRADE assessment of evidence quality

More than 50 interventions have been used to treat hidradenitis suppurativa (HS), and so therapy decisions can be challenging. Our objective was to summarize and appraise randomized controlled trial (RCT) evidence for HS interventions in adults. Searches were conducted in Medline, Embase, CENTRAL, LILACS, five trials registers and abstracts from eight dermatology conferences until 13 August 2015. Two review authors independently assessed study eligibility, extracted data and assessed methodological quality. Primary outcomes were quality of life and adverse effects of the interventions. Twelve trials, from 1983 to 2015, investigating 15 different interventions met our inclusion criteria. The median trial duration was 16 weeks and the median number of participants was 27. Adalimumab 40 mg weekly improved the Dermatology Life Quality Index (DLQI) by 4·0 points, which equates to the minimal clinically important difference for the scale, compared with placebo (95% confidence interval -6·5 to -1·5 points). Evidence quality was reduced to 'moderate' because the results are based on only a single study. Adalimumab 40 mg every other week was ineffective in a meta-analysis of two studies comprising 124 participants. Infliximab 5 mg kg(-1) improved the DLQI score by 8·4 points after 8 weeks in a moderate-quality study completed by 33 of 38 participants. Etanercept 50 mg twice weekly was ineffective. Inclusion of a gentamicin sponge prior to primary closure did not improve outcomes. Other interventions, including topical and oral antibiotics, were investigated by relatively small studies, preventing treatment recommendations due to imprecision. More, larger RCTs are required to investigate most HS interventions, particularly oral treatments and surgical therapy. Moderate-quality evidence suggests that adalimumab given weekly and infliximab are effective, whereas adalimumab every other week is ineffective.

Factors associated with pruritic papular eruption of human immunodeficiency virus infection in the antiretroviral therapy era

BACKGROUND

Pruritic papular eruption (PPE) of HIV is common in HIV-infected populations living in the tropics. Its aetiology has been attributed to insect bite reactions and it is reported to improve with antiretroviral therapy (ART). Its presence after at least 6 months of ART has been proposed as one of several markers of treatment failure.

OBJECTIVES

To determine factors associated with PPE in HIV-infected persons receiving ART.

METHODS

A case-control study nested within a 500-person cohort from a teaching hospital in Mbarara, Uganda. Forty-five cases and 90 controls were enrolled. Cases had received ART for ≥ 15 months and had an itchy papular rash for at least 1 month with microscopic correlation by skin biopsy. Each case was individually matched with two controls for age, sex and ART duration.

RESULTS

Twenty-five of 45 cases (56%) had microscopic findings consistent with PPE. At skin examination and biopsy (study enrolment), a similar proportion of PPE cases and matched controls had plasma HIV RNA < 400 copies mL(-1) (96% vs. 85%, P = 0·31). The odds of having PPE increased fourfold with every log increase in viral load at ART initiation (P = 0·02) but not at study enrolment. CD4 counts at ART initiation and study enrolment, and CD4 gains and CD8(+) T-cell activation measured 6 and 12 months after ART commencement were not associated with PPE. Study participants who reported daily insect bites had greater odds of being cases [odds ratio (OR) 8·3, P < 0·001] or PPE cases (OR 8·6, P = 0·01).

CONCLUSIONS

Pruritic papular eruption in HIV-infected persons receiving ART for ≥ 15 months was associated with greater HIV viraemia at ART commencement, independent of CD4 count. Skin biopsies are important to distinguish between PPE and other itchy papular eruptions.

Management of breast cancer with nanoparticle albumin-bound (nab)-paclitaxel combination regimens: a clinical review

BACKGROUND

Monotherapy with nanoparticle albumin-bound (nab)-paclitaxel has demonstrated improved efficacy and safety compared with solvent-based paclitaxel and docetaxel.

DESIGN

A comprehensive review of all reported studies of nab-paclitaxel combinations with other agents in all breast cancer settings was undertaken.

RESULTS

Most studies reviewed are small, phase II and non-comparative. Combinations studied included nab-paclitaxel plus trastuzumab and/or bevacizumab (with or without additional cytotoxic agents), gemcitabine, capecitabine, carboplatin, or lapatinib. The majority of metastatic and neoadjuvant studies demonstrated satisfactory efficacy and safety for nab-paclitaxel combinations, although conclusions regarding comparison with solvent-based taxane (SBT) regimens are not possible. The two adjuvant studies confirmed the safety of nab-paclitaxel combinations in this setting.

CONCLUSIONS

Although results of this review indicate that nab-paclitaxel may be an appropriate substitute for SBTs in combination regimens, additional research is required to confirm the place and cost effectiveness of these combinations before nab-paclitaxel could be recommended routinely in all settings.

Intrathecal chemotherapy alone is inadequate central nervous system prophylaxis in patients with intermediate-grade non-Hodgkin's lymphoma

Central nervous system (CNS) relapse of non-Hodgkin's lymphoma (NHL) is usually fatal despite therapy and effective prophylaxis is desirable. Patients at high-risk usually receive intrathecal (i.t.) prophylaxis, although its efficacy is unproven. We therefore analyzed the outcome of all patients with newly diagnosed "intermediate-grade" NHL receiving i.t. prophylaxis from 1991 to 1999. Twenty-six patients were identified and analyzed. All were free of CNS involvement at diagnosis with negative cerebrospinal fluid (CSF) cytology. Disease stage was IE in 7, and IV in 19, with a median of two extranodal sites involved. Serum lactate dehydrogenase was elevated in 65%, and the median International Prognostic Factors Index score was 3 (range 0-5). Anthracycline-based chemotherapy was used in all cases and included high-dose methotrexate +/- ara-C in six patients. The median number of i.t. treatments was 5 (range 1-12) and comprised methotrexate +/- steroid in 15, together with ara-C in 11. The actuarial 3-year CNS-relapse rate was 26 +/- 10%. Six CNS-relapses were observed and involved the spinal cord or brain parenchyma in two cases each, and the leptomeninges in four patients. Treatment-related variables associated with higher CNS-relapse rates (34-50%) were: delay of > or = 14 days from diagnosis to first i.t. injection, < 5 i.t. treatments, delay of i.t. prophylaxis until after attaining CR and systemic treatment lacking high-dose methotrexate +/- ara-C (each P < or = 0.17). I.t. CNS prophylaxis, as used here, was inadequate. Alternative approaches should be pursued.

Deafness from eighth cranial nerve involvement in a patient with large-cell transformation of mycosis fungoides

Central nervous system (CNS) involvement by mycosis fungoides (MF) is rare and is usually seen in advanced stages with lymph node or visceral involvement. We describe a patient with advanced stage MF in large-cell transformation who presented with profound hearing loss after chemotherapy. Despite an initial differential diagnosis of vincristine-related neurotoxicity based on clinical, audiometric, and MRI investigations, CSF examination revealed lymphomatous leptomeningeal involvement. This case illustrates the importance of an awareness of the possibility of CNS involvement by MF and underlines the need for a complete neurologic evaluation including CSF examination in a patient with underlying MF who presents with a new neurological problem.

Magnetic resonance imaging in the diagnostic management of diseases of the knee

Magnetic resonance imaging (MRI) of the knee is fast assuming a major role in evaluating disorders of the knee. The major advantage of MRI is that it is a totally non-invasive procedure with superior soft tissue contrast resolution and multi-planar capabilities. In contrast to arthrography, intra-articular as well as extra-articular structures in the knee can be evaluated by MRI. Indications include studying abnormalities of the menisci, ligament, patella and quadriceps mechanisms, articular cartilage, bone, bursa and other soft tissue. A review of the literature comparing MRI to arthrography and arthroscopy of the knee, especially in relation to meniscal injuries and anterior cruciate ligament tears, shows wide ranges in the accuracy rates of MRI. There are pitfalls in the interpretation of meniscal tears on MRI like normal variants, tears at the free edge and healed or previously repaired tears. Arthrography remains a useful diagnostic modality in some of these settings.

Use of magnetic resonance imaging in the evaluation of nasopharyngeal cancer

Computerised tomographic (CT) scanning is an established modality in the diagnosis and evaluation of tumours of the nasopharynx. Magnetic resonance (MR) imaging is a newer tool which has rapidly gained acceptance in the same regard due to its innate ability to better characterise and detail soft tissue structures, and its multiplanar scanning ability. This study reviews our experience, in a series of 30 cases, with the use of MR imaging in lesions of the nasopharynx, in particular nasopharyngeal carcinoma. Most of our patients showed involvement of the pharyngeal mucosal and parapharyngeal spaces, as expected. A large number also had intracranial extension. Our sample was probably biased as patients with neurological involvement are more likely to be assessed by MR than CT. Soft tissue infiltration, particularly intracranial extension, was very clearly delineated, especially when intravenous contrast (gadolinium DTPA) was administered. Bone erosion was not well demonstrated. In current clinical practice where radiotherapy is the most common form of treatment for nasopharyngeal carcinoma however, there does not in general, appear to be any clear advantage of MR over CT in the imaging of this particular disease, although there are some exceptions.

Magnetic resonance imaging of leptomeningeal metastases to the spine

Neurologic complications of malignancy can result from paraneoplastic manifestations, cerebral metastases or leptomeningeal involvement. Radiologic evaluation by computerised tomography (CT) has proven to be an invaluable tool in cancer patients with neurologic symptoms. Myelography and CT myelography have been the main modalities for evaluating leptomeningeal metastases. The advent of magnetic resonance imaging (MRI) with contrast has provided an alternative modality for evaluation of leptomeningeal disease. MRI as compared to CT myelography is non-invasive and at least as sensitive. The findings in two patients with leptomeningeal involvement are reported to illustrate the value of MRI.