Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani, Amir Masoud; Yousefpoor, Efat; Yousefpoor, Mohammad Sadegh; Mehmood, Zahid; Haider, Amir; Hosseinzadeh, Mehdi; Naqvi, Rizwan Ali

doi:10.3390/math9222970

Cited by 92 publications

(53 citation statements)

References 153 publications

(326 reference statements)

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“…Further, even if we practically use AUC as an evaluation criterion, we acknowledge its limits in the setting of AI, especially in case of unbalanced dataset. Note that other criteria can also be used to evaluate ML models, such as model relevance, efficiency, and interpretability [56]. However, to achieve high-quality and high-quantity data sets, it is of paramount importance the screening of each step of the process, from data collection to ML model selection and its algorithm (Rev #2, comm #3, comm #4).…”

Section: Discussionmentioning

confidence: 99%

Machine learning in perioperative medicine: a systematic review

Valente

Bertorelli

Pifferi

et al. 2022

J Anesth Analg Crit Care

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Background Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: “risk prediction,” “surgery,” “machine learning,” “intensive care unit (ICU),” and “anesthesia” “perioperative.” We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. Conclusions The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.

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Section: Discussionmentioning

confidence: 99%

Machine learning in perioperative medicine: a systematic review

Valente

Bertorelli

Pifferi

et al. 2022

J Anesth Analg Crit Care

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“…The CH node (for example, CH i ) is responsible for generating the cluster key and sending it to the cluster member nodes (for Then, CH i encrypts this cluster key (k cluster ) using k initial and broadcasts the encrypted key to CM j (see lines 2-3 of Algorithm 2). This process is expressed in Equation (3).…”

Section: Secure Intra-cluster Communicationmentioning

confidence: 99%

“…They are equipped with a small battery (3,4). Recharging this battery is very difficult because sensors are usually scattered in insecure and inaccessible environments (5,6).…”

Section: Introductionmentioning

confidence: 99%

SecAODV: A Secure Healthcare Routing Scheme Based on Hybrid Cryptography in Wireless Body Sensor Networks

et al. 2022

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In recent decades, the use of sensors has dramatically grown to monitor human body activities and maintain the health status. In this application, routing and secure data transmission are very important to prevent the unauthorized access by attackers to health data. In this article, we propose a secure routing scheme called SecAODV for heterogeneous wireless body sensor networks. SecAODV has three phases: bootstrapping, routing between cluster head nodes, and communication security. In the bootstrapping phase, the base station loads system parameters and encryption functions in the memory of sensor nodes. In the routing phase, each cluster head node calculates its degree based on several parameters, including, distance, residual energy, link quality, and the number of hops, to decide for rebroadcasting the route request (RREQ) message. In the communication security phase, a symmetric cryptography method is used to protect intra-cluster communications. Also, an asymmetric cryptography method is used to secure communication links between cluster head nodes. The proposed secure routing scheme is simulated in the network simulator version 2 (NS2) simulator. The simulation results are compared with the secure multi tier energy-efficient routing scheme (SMEER) and the centralized low-energy adaptive clustering hierarchy (LEACH-C). The results show that SecAODV improves end-to-end delay, throughput, energy consumption, packet delivery rate (PDR), and packet loss rate (PLR).

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“…However, real-world optimization issues are very complicated because they are large and dynamic. As a result, it is necessary to use computational intelligence-based methods such as reinforcement learning (RL) to solve these issues 28 , 29 . Today, RL algorithms are being popular rapidly because they can successfully find optimal response at a proper time.…”

Section: Introductionmentioning

confidence: 99%

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Rahmani

Ali

Malik

et al. 2022

Sci Rep

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Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Cited by 92 publications

References 153 publications

Machine learning in perioperative medicine: a systematic review

Machine learning in perioperative medicine: a systematic review

SecAODV: A Secure Healthcare Routing Scheme Based on Hybrid Cryptography in Wireless Body Sensor Networks

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

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