Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test

Ortelli, Paola; Ferrazzoli, Davide; Versace, Viviana; Cian, Veronica; Zarucchi, Marianna; Gusmeroli, Anna; Canesi, Margherita; Frazzitta, Giuseppe; Volpe, Daniele; Ricciardi, Lucia; Nardone, Raffaele; Ruffini, Ingrid; Saltuari, Leopold; Sebastianelli, Luca; Belvisi, Daniele; Maestri, Roberto

doi:10.1038/s41531-022-00304-z

Cited by 9 publications

(10 citation statements)

References 48 publications

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“…It is a recent development, but gaining popularity in diverse fields. 19,20,21,22 . For the documentation and code examples please refer to PyCaret's GitHub site: https://pycaret.org/.…”

Section: Experiments and Methodologymentioning

confidence: 99%

Accurate Prediction of Global Mean Temperature through Data Analytics

Niyogi

Sriniva

2022

Preprint

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There is a need to predict how the Global Mean Temperature (GMT) will evolve in the next few decades so that all nations can plan their reduction in the use of fossil fuels. The ability to predict historical data is a necessary first step towards the actual goal of making long-range forecasts. This paper examines the advantage of statistical and simpler Machine Learning (ML) methods instead of directly using complex ML algorithms and Deep Learning Neural Networks (DNN). Systematic data preparation methods have been applied as they have often been shown to offer predictive advantages to ML methods. The GMT time series is treated both as a univariate time series and also cast as a regression problem. Some steps of data preparation were found to be effective. Some simple ML methods did as well or better than the more well-known ones showing merit in trying a large bouquet of algorithms as a first step. The GMT data were divided into training and testing datasets. Predictions for the annual GMT testing data were better than that published so far, with the lowest RMSE value of 0.02 degree C. RMSE for five-year mean GMT values for the test data ranged from 0.00002 to 0.00036 degree C.

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“…It is a recent development, but gaining popularity in diverse fields. 19,20,21,22 . For the documentation and code examples please refer to PyCaret's GitHub site: https://pycaret.org/.…”

Section: Experiments and Methodologymentioning

confidence: 99%

Accurate Prediction of Global Mean Temperature through Data Analytics

Niyogi

Sriniva

2022

Preprint

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“…It is a recent development, but gaining popularity in diverse fields. 17,18,19,20 Both the Time-series and Regression modules of PyCaret have been used for implementing formulations (a) and (c) respectively and they are discussed under separate heads with the same names.…”

Section: Experiments and Methodologymentioning

confidence: 99%

The Prediction of Global Mean Temperature through Machine Learning

Niyogi

Sriniva

2022

Preprint

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Global Mean Temperature (GMT) is a very important variable to be predicted based on its history. The fluctuations in GMT might not be describable by exact mathematical modelling since the underlying Physics is not yet understood fully, and a data-driven approach to this problem is an alternative worth exploring. A variety of data-driven algorithms in Machine Learning (ML) and Deep Learning Neural Networks (DNN) are available for predicting time series. However, advanced algorithms, like DNN are complex to implement and computationally expensive. In this article, only simple ML methods have been evaluated to predict GMT treating it both as a univariate time series and also casting it to a regression problem. The effectiveness of a large set of simpler ML methods along with different data preparation techniques to forecast the GMT and mean value of GMT over a span of years was examined. It was found that some simple methods did as well or better than the more well-known ones showing merit in trying a large bouquet of algorithms as a first step. Forecasts were satisfactory with an RMSE value of around 0.056 on average, with the lowest value of 0.02. RMSE for mean GMT values ranged from 0.00002 to 0.00036. This establishes a benchmark for the more advanced ML models to reach. Some steps of data preparation were shown to be effective. Application of DNN is recommended to examine if that is capable of predicting GMT with greater accuracy.

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“…The most commonly used data modalities were imaging (n = 33) [ 24 , 27 – 31 , 113 , 114 , 117 , 119 , 122 , 129 , 130 , 133 , 137 , 139 – 143 , 147 – 152 , 155 , 156 , 159 – 161 , 163 , 164 ], clinical characteristics (n = 17) [ 84 , 118 , 131 , 132 , 137 , 140 , 152 , 153 , 155 , 156 , 159 , 160 , 162 – 164 , 169 , 170 ], EEG (n = 11) [ 25 , 26 , 120 , 125 – 128 , 135 , 136 , 146 , 151 ], and neuropsychological profile (n = 10) [ 115 , 118 , 132 , 134 , 142 , 157 , 158 …”

Section: Observations and Findingsmentioning

confidence: 99%

“…ML techniques used across all reviewed studies were categorised into 12 categories, some of which overlap: (1) tree based methods (n = 32) [ 22 , 23 , 26 , 27 , 31 , 114 , 116 , 119 , 122 , 125 , 126 , 128 – 130 , 132 , 134 , 137 , 139 , 141 , 145 , 153 , 155 , 157 – 159 , 162 , 164 , 166 , 167 , 170 – 172 ], (2) Support Vector Machines (n = 30) [ 21 , 23 – 25 , 27 , 28 , 30 , 113 , 115 , 117 , 118 , 122 – 124 , 133 , 134 , 138 , 139 , 141 , 142 , 148 – 151 , 153 , 156 – 159 , 161 , …”

Section: Observations and Findingsmentioning

confidence: 99%

“…44 of the 58 supervised learning studies focused on classification activities, 40 of which focused on classification vs NC/HC [ 22 – 26 , 28 , 30 , 31 , 84 , 113 , 114 , 116 , 117 , 119 , 122 , 124 – 126 , 128 , 130 , 133 , 134 , 138 , 142 , 145 , 146 , 148 – 151 , 153 – 155 , 158 , 159 , 161 , 164 , 166 , 171 , 172 ], and four classifying against memory disorders [ 115 , 127 , 139 , 167 ]. 11 studies focused on prediction [ 129 , 131 , 132 , 137 , 140 , 141 , 152 , 156 , 162 , 163 , 170 ], and the remaining three on biomarker identification [ 27 , 123 , 169 ].…”

Section: Observations and Findingsmentioning

confidence: 99%

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Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Altham,

Zhang,

Pereira

2024

PLoS ONE

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Background Parkinson’s Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. Methods To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson’s Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. Results Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson’s Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson’s Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. Conclusions Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

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Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test

Cited by 9 publications

References 48 publications

Accurate Prediction of Global Mean Temperature through Data Analytics

Accurate Prediction of Global Mean Temperature through Data Analytics

The Prediction of Global Mean Temperature through Machine Learning

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

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