1
|
Tamburella F, Lena E, Mascanzoni M, Iosa M, Scivoletto G. Harnessing Artificial Neural Networks for Spinal Cord Injury Prognosis. J Clin Med 2024; 13:4503. [PMID: 39124769 PMCID: PMC11313443 DOI: 10.3390/jcm13154503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. Materials: a database of 1256 SCI patients admitted for rehabilitation was analyzed. Clinical and demographic data and SCI characteristics were used to predict functional outcomes using both ANN and linear regression models. The former was structured with input, hidden, and output layers, while the linear regression identified significant variables affecting outcomes. Both approaches aimed to evaluate and compare their accuracy for rehabilitation outcomes measured by the Spinal Cord Independence Measure (SCIM) score. Results: Both ANN and linear regression models identified key predictors of functional outcomes, such as age, injury level, and initial SCIM scores (correlation with actual outcome: R = 0.75 and 0.73, respectively). When also alimented with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors, like motor completeness and complications during hospitalization, showing an improvement in its accuracy (R = 0.87). Conclusions: ANN seemed to be not widely superior to classical statistics in general, but, taking into account complex and non-linear relationships among variables, emphasized the impact of complications during the hospitalization on recovery, particularly respiratory issues, deep vein thrombosis, and urological complications. These results suggested that the management of complications is crucial for improving functional recovery in SCI patients.
Collapse
Affiliation(s)
- Federica Tamburella
- Department of Life Sciences, Health and Health Professions, Link Campus University, 00165 Rome, Italy;
- Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy; (E.L.); (M.M.); (G.S.)
| | - Emanuela Lena
- Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy; (E.L.); (M.M.); (G.S.)
| | - Marta Mascanzoni
- Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy; (E.L.); (M.M.); (G.S.)
| | - Marco Iosa
- Department of Psychology, Sapienza University of Rome, 00183 Rome, Italy
- Smart Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Giorgio Scivoletto
- Spinal Center, Spinal Rehabilitation Laboratory, IRCCS Fondazione S. Lucia, 00179 Rome, Italy; (E.L.); (M.M.); (G.S.)
| |
Collapse
|
2
|
Caruana A, Bandara M, Musial K, Catchpoole D, Kennedy PJ. Machine learning for administrative health records: A systematic review of techniques and applications. Artif Intell Med 2023; 144:102642. [PMID: 37783537 DOI: 10.1016/j.artmed.2023.102642] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 10/04/2023]
Abstract
Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of EHR analytics emphasise that the data-modality of the EHR limits the breadth of suitable machine learning techniques, and pursuable healthcare applications. Despite emphasising the importance of data modality, the literature fails to analyse which techniques and applications are relevant to AHRs. AHRs contain uniquely well-structured, categorically encoded records which are distinct from other data-modalities captured by EHRs, and they can provide valuable information pertaining to how patients interact with the healthcare system. This paper systematically reviews AHR-based research, analysing 70 relevant studies and spanning multiple databases. We identify and analyse which machine learning techniques are applied to AHRs and which health informatics applications are pursued in AHR-based research. We also analyse how these techniques are applied in pursuit of each application, and identify the limitations of these approaches. We find that while AHR-based studies are disconnected from each other, the use of AHRs in health informatics research is substantial and accelerating. Our synthesis of these studies highlights the utility of AHRs for pursuing increasingly complex and diverse research objectives despite a number of pervading data- and technique-based limitations. Finally, through our findings, we propose a set of future research directions that can enhance the utility of AHR data and machine learning techniques for health informatics research.
Collapse
Affiliation(s)
- Adrian Caruana
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia.
| | - Madhushi Bandara
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Katarzyna Musial
- Complex Adaptive Systems Lab, Data Science Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia
| | - Daniel Catchpoole
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Biospecimen Research Services, The Children's Cancer Research Unit, The Children's Hospital at Westmead, Australia
| | - Paul J Kennedy
- Australian Artificial Intelligence Institute, Faculty of Engineering and IT, University of Technology Sydney, Australia; Joint Research Centre in AI for Health and Wellness, University of Technology Sydney, Australia, and Ontario Tech University, Canada
| |
Collapse
|
3
|
Analysis of length of stay for patients admitted to Korean hospitals based on the Korean National Health Insurance Service Database. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
|
4
|
Cerasa A, Tartarisco G, Bruschetta R, Ciancarelli I, Morone G, Calabrò RS, Pioggia G, Tonin P, Iosa M. Predicting Outcome in Patients with Brain Injury: Differences between Machine Learning versus Conventional Statistics. Biomedicines 2022; 10:biomedicines10092267. [PMID: 36140369 PMCID: PMC9496389 DOI: 10.3390/biomedicines10092267] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/29/2022] Open
Abstract
Defining reliable tools for early prediction of outcome is the main target for physicians to guide care decisions in patients with brain injury. The application of machine learning (ML) is rapidly increasing in this field of study, but with a poor translation to clinical practice. This is basically dependent on the uncertainty about the advantages of this novel technique with respect to traditional approaches. In this review we address the main differences between ML techniques and traditional statistics (such as logistic regression, LR) applied for predicting outcome in patients with stroke and traumatic brain injury (TBI). Thirteen papers directly addressing the different performance among ML and LR methods were included in this review. Basically, ML algorithms do not outperform traditional regression approaches for outcome prediction in brain injury. Better performance of specific ML algorithms (such as Artificial neural networks) was mainly described in the stroke domain, but the high heterogeneity in features extracted from low-dimensional clinical data reduces the enthusiasm for applying this powerful method in clinical practice. To better capture and predict the dynamic changes in patients with brain injury during intensive care courses ML algorithms should be extended to high-dimensional data extracted from neuroimaging (structural and fMRI), EEG and genetics.
Collapse
Affiliation(s)
- Antonio Cerasa
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, 87036 Rende, Italy
- S. Anna Institute, 88900 Crotone, Italy
- Correspondence:
| | - Gennaro Tartarisco
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
| | - Roberta Bruschetta
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
- Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
| | - Irene Ciancarelli
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
- San Raffaele Sulmona Institute, 67039 Sulmona, Italy
| | | | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy, 98164 Messina, Italy
| | | | - Marco Iosa
- IRCCS Centro Neurolesi “Bonino-Pulejo”, 98123 Messina, Italy
- Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
- Santa Lucia Foundation IRCSS, 00179 Rome, Italy
| |
Collapse
|
5
|
Leveraging electronic health record data to inform hospital resource management : A systematic data mining approach. Health Care Manag Sci 2021; 24:716-741. [PMID: 34031792 DOI: 10.1007/s10729-021-09554-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 02/02/2021] [Indexed: 10/21/2022]
Abstract
Early identification of resource needs is instrumental in promoting efficient hospital resource management. Hospital information systems, and electronic health records (EHR) in particular, collect valuable demographic and clinical patient data from the moment patients are admitted, which can help predict expected resource needs in early stages of patient episodes. To this end, this article proposes a data mining methodology to systematically obtain predictions for relevant managerial variables by leveraging structured EHR data. Specifically, these managerial variables are: i) Diagnosis categories, ii) procedure codes, iii) diagnosis-related groups (DRGs), iv) outlier episodes and v) length of stay (LOS). The proposed methodology approaches the problem in four stages: Feature set construction, feature selection, prediction model development, and model performance evaluation. We tested this approach with an EHR dataset of 5,089 inpatient episodes and compared different classification and regression models (for categorical and continuous variables, respectively), performed temporal analysis of model performance, analyzed the impact of training set homogeneity on performance and assessed the contribution of different EHR data elements for model predictive power. Overall, our results indicate that inpatient EHR data can effectively be leveraged to inform resource management on multiple perspectives. Logistic regression (combined with minimal redundancy maximum relevance feature selection) and bagged decision trees yielded best results for predicting categorical and numerical managerial variables, respectively. Furthermore, our temporal analysis indicated that, while DRG classes are more difficult to predict, several diagnosis categories, procedure codes and LOS amongst shorter-stay patients can be predicted with higher confidence in early stages of patient stay. Lastly, value of information analysis indicated that diagnoses, medication and structured assessment forms were the most valuable EHR data elements in predicting managerial variables of interest through a data mining approach.
Collapse
|
6
|
Chu J, Chen J, Chen X, Dong W, Shi J, Huang Z. Knowledge-aware multi-center clinical dataset adaptation: Problem, method, and application. J Biomed Inform 2021; 115:103710. [PMID: 33581323 DOI: 10.1016/j.jbi.2021.103710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 11/30/2022]
Abstract
Adaptable utilization of clinical data collected from multiple centers, prompted by the need to overcome the shifts between the dataset distributions, and exploit these different datasets for potential clinical applications, has received significant attention in recent years. In this study, we propose a novel approach to this task by infusing an external knowledge graph (KG) into multi-center clinical data mining. Specifically, we propose an adversarial learning model to capture shared patient feature representations from multi-center heterogeneous clinical datasets, and employ an external KG to enrich the semantics of the patient sample by providing both clinical center-specific and center-general knowledge features, which are trained with a graph convolutional autoencoder. We evaluate the proposed model on a real clinical dataset extracted from the general cardiology wards of a Chinese hospital and a well-known public clinical dataset (MIMIC III, pertaining to ICU clinical settings) for the task of predicting acute kidney injury in patients with heart failure. The achieved experimental results demonstrate the efficacy of our proposed model.
Collapse
Affiliation(s)
- Jiebin Chu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, China
| | - Jinbiao Chen
- College of Biomedical Engineering and Instrument Science, Zhejiang University, China
| | - Xiaofang Chen
- College of Biomedical Engineering and Instrument Science, Zhejiang University, China
| | - Wei Dong
- Department of Cardiology, Chinese PLA General Hospital, China
| | - Jinlong Shi
- Department of Medical Innovation Research, Medical Big Data Center, Chinese PLA General Hospital, China
| | - Zhengxing Huang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, China.
| |
Collapse
|
7
|
Casiraghi E, Malchiodi D, Trucco G, Frasca M, Cappelletti L, Fontana T, Esposito AA, Avola E, Jachetti A, Reese J, Rizzi A, Robinson PN, Valentini G. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:196299-196325. [PMID: 34812365 PMCID: PMC8545262 DOI: 10.1109/access.2020.3034032] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 05/06/2023]
Abstract
Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.
Collapse
Affiliation(s)
- Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
- CINI National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS)Università di Roma00185RomaItaly
| | - Dario Malchiodi
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
- CINI National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS)Università di Roma00185RomaItaly
- Data Science Research CenterUniversità degli Studi di Milano20133MilanItaly
| | - Gabriella Trucco
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | - Marco Frasca
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | - Luca Cappelletti
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | - Tommaso Fontana
- Dipartimento di ElettronicaInformazione e BioingegneriaPolitecnico di Milano20133MilanItaly
| | | | - Emanuele Avola
- Postgraduate School in RadiodiagnosticsUniversità degli Studi di Milano20122MilanItaly
| | - Alessandro Jachetti
- Accident and Emergency DepartmentFondazione IRCCS Ca Granda Ospedale Maggiore Policlinico20122MilanItaly
| | - Justin Reese
- Division of Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | - Alessandro Rizzi
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
| | | | - Giorgio Valentini
- Department of Computer Science “Giovanni degli Antoni,”Università degli Studi di Milano20133MilanItaly
- CINI National Laboratory of Artificial Intelligence and Intelligent Systems (AIIS)Università di Roma00185RomaItaly
- Data Science Research CenterUniversità degli Studi di Milano20133MilanItaly
| |
Collapse
|
8
|
|
9
|
Xu X, Jin T, Wei Z, Wang J. Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:5208072. [PMID: 29065617 PMCID: PMC5474282 DOI: 10.1155/2017/5208072] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 01/24/2017] [Accepted: 02/05/2017] [Indexed: 11/17/2022]
Abstract
Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model.
Collapse
Affiliation(s)
- Xiao Xu
- School of Software, Tsinghua University, Beijing, China
| | - Tao Jin
- School of Software, Tsinghua University, Beijing, China
| | - Zhijie Wei
- School of Software, Tsinghua University, Beijing, China
| | - Jianmin Wang
- School of Software, Tsinghua University, Beijing, China
| |
Collapse
|
10
|
Abstract
With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors.
Collapse
|