In Frontiers in public health

OBJECTIVE : Machine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions.

METHOD : Patient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared.

RESULTS : The accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94.

CONCLUSION : Among the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making.

Lyu Yuwen, Xu Qian, Yang Zhenchao, Liu Junrong

2023

assistance systems, machine learning algorithm, medical decision-making, patient choice tendency, prediction

In Interactive journal of medical research

BACKGROUND : Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.

OBJECTIVE : This paper aims to synthesize research and identify hotspots of big data in infectious disease epidemiology.

METHODS : Bibliometric data from 3054 documents that satisfied the inclusion criteria were retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.

RESULTS : The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. It also placed the US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.

CONCLUSIONS : Proposals for future studies are made based on these findings. This study will provide healthcare informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

Amusa Lateef Babatunde, Twinomurinzi Hossana, Phalane Edith, Phaswana-Mafuya Refilwe Nancy

2022-Nov-29

In European journal of neurology

BACKGROUND : Alzheimer's disease (AD) is the most common type of dementia. Amyloid-β (Aβ) positivity is the main diagnostic marker for AD. Aβ positron emission tomography and cerebrospinal fluid are widely used in the clinical diagnosis of AD. However, these methods only assess the concentrations of Aβ and the accessibility of these methods is thus relatively limited compared with structural magnetic resonance imaging (sMRI).

METHODS : We investigated whether regions of interest (ROIs) in sMRIs can be used to predict Aβ positivity for samples with normal cognition (NC), mild cognitive impairment (MCI) and dementia. We obtained 846 Aβ negative (Aβ-) and 865 Aβ positive (Aβ+) samples from the Alzheimer's Disease Neuroimaging Initiative database. To predict which samples are Aβ+, we built five machine learning models using ROIs and apolipoprotein E (APOE) genotypes as features. To test the performance of the machine learning models, we constructed a new cohort containing 97 Aβ- and 81 Aβ+ samples.

RESULTS : The best performing machine learning model combining ROIs and APOE had an accuracy of 0.798, indicating that it can help predict Aβ+. Furthermore, we searched ROIs that could aid our prediction and discovered that an average left entorhinal cortical region (L-ERC) thickness is an important feature. We also noted significant differences in L-ERC thickness between the Aβ- and Aβ+ samples even in the same diagnosis of NC, MCI, and dementia.

CONCLUSIONS : Our findings indicate that ROIs from sMRIs along with APOE can be used as an initial screening tool in the early diagnosis of AD.

Hwang Jeongyoung, Park Hee Kyung, Yoon Hai-Jeon, Jeong Jee Hyang, Lee Hyunju

2023-Mar-13

“Alzheimers disease”, amyloid-β, left entorhinal cortical region, machine learning, sMRI

In Journal of the American Geriatrics Society ; h5-index 64.0

BACKGROUND : Although 50 years represents middle age among uninfected individuals, studies have shown that persons living with HIV (PWH) begin to demonstrate elevated risk for serious falls and fragility fractures in the sixth decade; the proportions of these outcomes attributable to modifiable factors are unknown.

METHODS : We analyzed 21,041 older PWH on antiretroviral therapy (ART) from the Veterans Aging Cohort Study from 01/01/2010 through 09/30/2015. Serious falls were identified by Ecodes and a machine-learning algorithm applied to radiology reports. Fragility fractures (hip, vertebral, and upper arm) were identified using ICD9 codes. Predictors for both models included a serious fall within the past 12 months, body mass index, physiologic frailty (VACS Index 2.0), illicit substance and alcohol use disorders, and measures of multimorbidity and polypharmacy. We separately fit multivariable logistic models to each outcome using generalized estimating equations. From these models, the longitudinal extensions of average attributable fraction (LE-AAF) for modifiable risk factors were estimated.

RESULTS : Key risk factors for both outcomes included physiologic frailty (VACS Index 2.0) (serious falls [15%; 95% CI 14%-15%]; fractures [13%; 95% CI 12%-14%]), a serious fall in the past year (serious falls [7%; 95% CI 7%-7%]; fractures [5%; 95% CI 4%-5%]), polypharmacy (serious falls [5%; 95% CI 4%-5%]; fractures [5%; 95% CI 4%-5%]), an opioid prescription in the past month (serious falls [7%; 95% CI 6%-7%]; fractures [9%; 95% CI 8%-9%]), and diagnosis of alcohol use disorder (serious falls [4%; 95% CI 4%-5%]; fractures [8%; 95% CI 7%-8%]).

CONCLUSIONS : This study confirms the contributions of risk factors important in the general population to both serious falls and fragility fractures among older PWH. Successful prevention programs for these outcomes should build on existing prevention efforts while including risk factors specific to PWH.

Womack Julie A, Murphy Terrence E, Leo-Summers Linda, Bates Jonathan, Jarad Samah, Gill Thomas M, Hsieh Evelyn, Rodriguez-Barradas Maria C, Tien Phyllis C, Yin Michael T, Brandt Cynthia A, Justice Amy C

2023-Mar-13

HIV, LE-AAF, falls, fragility fractures

In Stroke ; h5-index 83.0

BACKGROUND : Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT using routine brain magnetic resonance imaging.

METHODS : Routine brain magnetic resonance imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images of patients suspected of CVT from April 2014 through December 2019 who were enrolled from a CVT registry, were collected. The images were divided into 2 data sets: a development set and a test set. Different DL algorithms were constructed in the development set using 5-fold cross-validation. Four radiologists with various levels of expertise independently read the images and performed diagnosis within the test set. The diagnostic performance on per-patient and per-segment diagnosis levels of the DL algorithms and radiologist's assessment were evaluated and compared.

RESULTS : A total of 392 patients, including 294 patients with CVT (37±14 years, 151 women) and 98 patients without CVT (42±15 years, 65 women), were enrolled. Of these, 100 patients (50 CVT and 50 non-CVT) were randomly assigned to the test set, and the other 292 patients comprised the development set. In the test set, the optimal DL algorithm (multisequence multitask deep learning algorithm) achieved an area under the curve of 0.96, with a sensitivity of 96% (48/50) and a specificity of 88% (44/50) on per-patient diagnosis level, as well as a sensitivity of 88% (129/146) and a specificity of 80% (521/654) on per-segment diagnosis level. Compared with 4 radiologists, multisequence multitask deep learning algorithm showed higher sensitivity both on per-patient (all P<0.05) and per-segment diagnosis levels (all P<0.001).

CONCLUSIONS : The CVT-detected DL algorithm herein improved diagnostic performance of routine brain magnetic resonance imaging, with high sensitivity and specificity, which provides a promising approach for detecting CVT.

Yang Xiaoxu, Yu Pengxin, Zhang Haoyue, Zhang Rongguo, Liu Yuehong, Li Haoyuan, Sun Penghui, Liu Xin, Wu Yu, Jia Xiuqin, Duan Jiangang, Ji Xunming, Yang Qi

2023-Mar-13

algorithm, area under the curve, brain, cerebral venous thrombosis, magnetic resonance imaging

In Journal of Alzheimer's disease : JAD

BACKGROUND : Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification.

OBJECTIVE : The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages.

METHODS : Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages.

RESULTS : The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments.

CONCLUSION : Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.

Hollenbenders Yasmin, Pobiruchin Monika, Reichenbach Alexandra

2023-Mar-06

Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease, brain atrophy, clustering, hidden Markov model, longitudinal data, magnetic resonance imaging, patient stratification, subtype