BACKGROUND Traditional surveillance systems produce estimates of influenza-like illness (ILI) inc... more BACKGROUND Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users’ activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. OBJECTIVE Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. METHODS We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models—random forest, elastic net, and support vector machine (SVM). RESULTS For national ILI incidence r...
Introduction: Out-of-hospital cardiac arrest (OHCA) is a major public health issue. The prognosis... more Introduction: Out-of-hospital cardiac arrest (OHCA) is a major public health issue. The prognosis is closely related to the time from collapse to return of spontaneous circulation. Resuscitation efforts are frequently initiated at the request of emergency call center professionals who are specifically trained to identify critical conditions over the phone. However, 25% of OHCAs are not recognized during the first call. Therefore, it would be interesting to develop automated computer systems to recognize OHCA on the phone. The aim of this study was to build and evaluate machine learning models for OHCA recognition based on the phonetic characteristics of the caller’s voice. Methods: All patients for whom a call was done to the emergency call center of Rennes, France, between 01/01/2017 and 01/01/2019 were eligible. The predicted variable was OHCA presence. Predicting variables were collected by computer-automatized phonetic analysis of the call. They were based on the following voice...
International Journal of Environmental Research and Public Health
Digital health, e-health, telemedicine—this abundance of terms illustrates the scientific and tec... more Digital health, e-health, telemedicine—this abundance of terms illustrates the scientific and technical revolution at work, made possible by high-speed processing of health data, artificial intelligence (AI), and the profound upheavals currently taking place and yet to come in health systems [...]
Studies in health technology and informatics, 2021
Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Au... more Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Automating monitoring from clinical data warehouses is an opportunity to dynamically monitor devices and patient outcomes allowing improve clinical practices. Our objective was to assess quantitative and qualitative concordance between claim data and device supply data in order to create an e-cohort of patients undergoing a hip replacement. We performed a single-centre cohort pilot study, from one clinical data warehouse of a French University Hospital, from January 1, 2010 to December 31, 2019. We included all adult patients undergoing a hip arthroplasty, and with at least one hip medical device provided. Patients younger than 18 years or opposed to the reuse of their data were excluded from the analysis. Our primary outcome was the percentage of hospital stays with both hip arthroplasty and hip device provided. The patient and stay characteristics assessed in this study were: age, sex, l...
Background Traditionally, dengue surveillance is based on case reporting to a central health agen... more Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-rel...
Studies in health technology and informatics, 2020
Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article... more Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automatic methods based on neural network approaches for the automatic detection of the ICD-10 codes. The results show that we need 1) more examples per class given the number of classes to assign, and 2) a better word/concept vector representation of documents in order to accurately assign codes.
Studies in health technology and informatics, 2004
BACKGROUND The prognosis of life for patients with heart failure remains poor. By using data mini... more BACKGROUND The prognosis of life for patients with heart failure remains poor. By using data mining methods, the purpose of this study was to evaluate the most important criteria for predicting patient survival and to profile patients to estimate their survival chances together with the most appropriate technique for health care. METHODS Five hundred and thirty three patients who had suffered from cardiac arrest were included in the analysis. We performed classical statistical analysis and data mining analysis using mainly Bayesian networks. RESULTS The mean age of the 533 patients was 63 (+/- 17) and the sample was composed of 390 (73 %) men and 143 (27 %) women. Cardiac arrest was observed at home for 411 (77 %) patients, in a public place for 62 (12 %) patients and on a public highway for 60 (11 %) patients. The belief network of the variables showed that the probability of remaining alive after heart failure is directly associated to five variables: age, sex, the initial cardiac...
BACKGROUND The risk of cancer is higher in patients with renal diseases and diabetes compared wit... more BACKGROUND The risk of cancer is higher in patients with renal diseases and diabetes compared with the general population. The aim of this study was to assess in dialyzed patients, the association between diabetes and the risk to develop a cancer after dialysis start. METHODS All patients who started dialysis in the French region of Poitou-Charentes between 2008 and 2015 were included. Their baseline characteristics were extracted from the French Renal Epidemiology and Information Network and were linked to data relative to cancer occurrence from the Poitou-Charentes General Cancer Registry using a procedure developed by the INSHARE platform. The association between diabetes and the risk of cancer was assessed using the Fine & Gray model that takes into account the competing risk of death. RESULTS Among the 1634 patients included, 591 (36.2 %) had diabetes and 91 (5.6 %) patients developed a cancer (n = 24 before or at dialysis start, and n = 67 after dialysis start). The risk to de...
Integration of drugs-related data into a clinical data warehouse and its good use is a key issue,... more Integration of drugs-related data into a clinical data warehouse and its good use is a key issue, prescriptions being at the center of patient care in a hospital. Using an independent drugdatabase, Thériaque, and a standardized definition of prescriptions and medications the CIO (InterOperable Classification), we were able to implement a brand new facet to Roogle, our search engine. We enabled the user to search for a drug prescription either by selecting corresponding ATC codes, or directly with a search form including autocompletion and duplicates management. Althought a lot of work is still to be done, these developments pave the way to whole new ways to create cohorts of patients for clinical studies, or to find potential adverse effects related to a specific drug. Mots-clés : Informatique médicale ; Conférence ; Entrepôt de données ; Médicaments ; Prescription : Big data
Studies in health technology and informatics, 2011
UNLABELLED Semantic interoperability based on ontologies allows systems to combine their informat... more UNLABELLED Semantic interoperability based on ontologies allows systems to combine their information and process them automatically. The ability to extract meaningful fragments from ontology is a key for the ontology re-use and the construction of a subset will help to structure clinical data entries. The aim of this work is to provide a method for extracting a set of concepts for a specific domain, in order to help to define data elements of an oncologic EHR. METHOD a generic extraction algorithm was developed to extract, from the NCIT and for a specific disease (i.e. prostate neoplasm), all the concepts of interest into a sub-ontology. We compared all the concepts extracted to the concepts encoded manually contained into the multi-disciplinary meeting report form (MDMRF). RESULTS We extracted two sub-ontologies: sub-ontology 1 by using a single key concept and sub-ontology 2 by using 5 additional keywords. The coverage of sub-ontology 2 to the MDMRF concepts was 51%. The low rate ...
Background : Implementing clinical data warehouse (CDW) brings out legal and ethical issues. Meth... more Background : Implementing clinical data warehouse (CDW) brings out legal and ethical issues. Methods : the authors make a review of recent litterature on these points in Pubmed, Google scholar, Google, Science direct. Il includes scientific papers, texts of laws, and operational procedures. Then they propose an organisation for CDW implementation. Results : use of health personal data required patient’s consent and an authorization of competent authorithies. Rights of information and opposition are due to the patient. To assure data protection in CDW measures rely on data deidentification and hierarchical management of access. Conclusion : patient information remains difficult. Deidentification procedures are to be improved and
Studies in health technology and informatics, 2012
Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situa... more Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situations. K-nearest neighbors algorithm (K-NN) have been used in CBR systems to define new cases status according to characteristics of past nearest cases. We proposed a new hybrid approach combining logistic regression (LR) with K-NN to optimize CBR classification. First, we analyzed the knowledge database by LR procedures and the Pearson residuals of the LR model were used to define cases' utility of the knowledge database into K-NN. Secondly, we compared the classification performances of LR model and K-NNs coupled or not with LR. Our results showed that the information provided by the residuals could be used to optimize the settings of K-NN and to improve CBR classification.
Studies in Health Technology and Informatics, 2021
The development of precision medicine in oncology to define profiles of patients who could benefi... more The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to targeted therapies. This study aimed to develop an automated algorithm based on natural language processing to detect patients and tumor characteristics to reduce the time-consuming prescreening for trial inclusions. Hence, 640 anonymized multidisciplinary team meeting (MTM) reports concerning lung cancer were extracted from one teaching hospital data warehouse in France and annotated. To automate the extraction of 52 bioclinical information corresponding to 8 major eligibility criteria, regular expressions were implemented and evaluated. The performance parameters were satisfying: macroaverage F1-score 93%; rates reached 98% for precision and 92% for recall. In MTM, fill rates variabilities among patients and tumors information remained i...
Studies in Health Technology and Informatics, 2021
Surveillance and traceability of medical devices (MD) is a challenge in health care systems. In t... more Surveillance and traceability of medical devices (MD) is a challenge in health care systems. In the perspective of reusing EHR data to automate the monitoring of medical devices, we carried out a comparison of the main MD knowledge bases (MD-KDB) currently available in France. Four MD-KDBs (ANSM, Gudid, Exhausmed and CIOdm) were compared quantitatively and through an example of a shoulder prosthesis. The number of MDs registered differs from one MD-KDB to another. Domain terminologies used in MD-KDBs differ in terms of granularity and in the ease of querying. Waiting EUDAMED, the European MD-KDB, it seems necessary so far to use and combine information coming from several MD-KDBs to address MD monitoring.
Studies in Health Technology and Informatics, 2021
HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimb... more HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimbursed by the social security in France. Yet, PrEP is underused due to the difficulty to identify people at risk of HIV infection outside the “sexual health” care path. We developed and validated an automated algorithm that re-uses Electronic Health Record (EHR) data available in eHOP, the Clinical Data Warehouse of Rennes University Hospital (France). Using machine learning methods, we developed five models to predict incident HIV infections with 162 variables that might be exploited to predict HIV risk using EHR data. We divided patients aged 18 or more having at least one hospital admission between 2013 and 2019 in two groups: cases (patients with known HIV infection in the study period) and controls (patients without known HIV infection and no PrEP in the study period, but with at least one HIV risk factor). Among the 624,708 admissions, we selected 156 cases (incident HIV infection) ...
Background Artificial intelligence (AI) has the potential to transform our healthcare systems sig... more Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timi...
BACKGROUND Traditional surveillance systems produce estimates of influenza-like illness (ILI) inc... more BACKGROUND Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users’ activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. OBJECTIVE Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. METHODS We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models—random forest, elastic net, and support vector machine (SVM). RESULTS For national ILI incidence r...
Introduction: Out-of-hospital cardiac arrest (OHCA) is a major public health issue. The prognosis... more Introduction: Out-of-hospital cardiac arrest (OHCA) is a major public health issue. The prognosis is closely related to the time from collapse to return of spontaneous circulation. Resuscitation efforts are frequently initiated at the request of emergency call center professionals who are specifically trained to identify critical conditions over the phone. However, 25% of OHCAs are not recognized during the first call. Therefore, it would be interesting to develop automated computer systems to recognize OHCA on the phone. The aim of this study was to build and evaluate machine learning models for OHCA recognition based on the phonetic characteristics of the caller’s voice. Methods: All patients for whom a call was done to the emergency call center of Rennes, France, between 01/01/2017 and 01/01/2019 were eligible. The predicted variable was OHCA presence. Predicting variables were collected by computer-automatized phonetic analysis of the call. They were based on the following voice...
International Journal of Environmental Research and Public Health
Digital health, e-health, telemedicine—this abundance of terms illustrates the scientific and tec... more Digital health, e-health, telemedicine—this abundance of terms illustrates the scientific and technical revolution at work, made possible by high-speed processing of health data, artificial intelligence (AI), and the profound upheavals currently taking place and yet to come in health systems [...]
Studies in health technology and informatics, 2021
Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Au... more Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Automating monitoring from clinical data warehouses is an opportunity to dynamically monitor devices and patient outcomes allowing improve clinical practices. Our objective was to assess quantitative and qualitative concordance between claim data and device supply data in order to create an e-cohort of patients undergoing a hip replacement. We performed a single-centre cohort pilot study, from one clinical data warehouse of a French University Hospital, from January 1, 2010 to December 31, 2019. We included all adult patients undergoing a hip arthroplasty, and with at least one hip medical device provided. Patients younger than 18 years or opposed to the reuse of their data were excluded from the analysis. Our primary outcome was the percentage of hospital stays with both hip arthroplasty and hip device provided. The patient and stay characteristics assessed in this study were: age, sex, l...
Background Traditionally, dengue surveillance is based on case reporting to a central health agen... more Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-rel...
Studies in health technology and informatics, 2020
Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article... more Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automatic methods based on neural network approaches for the automatic detection of the ICD-10 codes. The results show that we need 1) more examples per class given the number of classes to assign, and 2) a better word/concept vector representation of documents in order to accurately assign codes.
Studies in health technology and informatics, 2004
BACKGROUND The prognosis of life for patients with heart failure remains poor. By using data mini... more BACKGROUND The prognosis of life for patients with heart failure remains poor. By using data mining methods, the purpose of this study was to evaluate the most important criteria for predicting patient survival and to profile patients to estimate their survival chances together with the most appropriate technique for health care. METHODS Five hundred and thirty three patients who had suffered from cardiac arrest were included in the analysis. We performed classical statistical analysis and data mining analysis using mainly Bayesian networks. RESULTS The mean age of the 533 patients was 63 (+/- 17) and the sample was composed of 390 (73 %) men and 143 (27 %) women. Cardiac arrest was observed at home for 411 (77 %) patients, in a public place for 62 (12 %) patients and on a public highway for 60 (11 %) patients. The belief network of the variables showed that the probability of remaining alive after heart failure is directly associated to five variables: age, sex, the initial cardiac...
BACKGROUND The risk of cancer is higher in patients with renal diseases and diabetes compared wit... more BACKGROUND The risk of cancer is higher in patients with renal diseases and diabetes compared with the general population. The aim of this study was to assess in dialyzed patients, the association between diabetes and the risk to develop a cancer after dialysis start. METHODS All patients who started dialysis in the French region of Poitou-Charentes between 2008 and 2015 were included. Their baseline characteristics were extracted from the French Renal Epidemiology and Information Network and were linked to data relative to cancer occurrence from the Poitou-Charentes General Cancer Registry using a procedure developed by the INSHARE platform. The association between diabetes and the risk of cancer was assessed using the Fine & Gray model that takes into account the competing risk of death. RESULTS Among the 1634 patients included, 591 (36.2 %) had diabetes and 91 (5.6 %) patients developed a cancer (n = 24 before or at dialysis start, and n = 67 after dialysis start). The risk to de...
Integration of drugs-related data into a clinical data warehouse and its good use is a key issue,... more Integration of drugs-related data into a clinical data warehouse and its good use is a key issue, prescriptions being at the center of patient care in a hospital. Using an independent drugdatabase, Thériaque, and a standardized definition of prescriptions and medications the CIO (InterOperable Classification), we were able to implement a brand new facet to Roogle, our search engine. We enabled the user to search for a drug prescription either by selecting corresponding ATC codes, or directly with a search form including autocompletion and duplicates management. Althought a lot of work is still to be done, these developments pave the way to whole new ways to create cohorts of patients for clinical studies, or to find potential adverse effects related to a specific drug. Mots-clés : Informatique médicale ; Conférence ; Entrepôt de données ; Médicaments ; Prescription : Big data
Studies in health technology and informatics, 2011
UNLABELLED Semantic interoperability based on ontologies allows systems to combine their informat... more UNLABELLED Semantic interoperability based on ontologies allows systems to combine their information and process them automatically. The ability to extract meaningful fragments from ontology is a key for the ontology re-use and the construction of a subset will help to structure clinical data entries. The aim of this work is to provide a method for extracting a set of concepts for a specific domain, in order to help to define data elements of an oncologic EHR. METHOD a generic extraction algorithm was developed to extract, from the NCIT and for a specific disease (i.e. prostate neoplasm), all the concepts of interest into a sub-ontology. We compared all the concepts extracted to the concepts encoded manually contained into the multi-disciplinary meeting report form (MDMRF). RESULTS We extracted two sub-ontologies: sub-ontology 1 by using a single key concept and sub-ontology 2 by using 5 additional keywords. The coverage of sub-ontology 2 to the MDMRF concepts was 51%. The low rate ...
Background : Implementing clinical data warehouse (CDW) brings out legal and ethical issues. Meth... more Background : Implementing clinical data warehouse (CDW) brings out legal and ethical issues. Methods : the authors make a review of recent litterature on these points in Pubmed, Google scholar, Google, Science direct. Il includes scientific papers, texts of laws, and operational procedures. Then they propose an organisation for CDW implementation. Results : use of health personal data required patient’s consent and an authorization of competent authorithies. Rights of information and opposition are due to the patient. To assure data protection in CDW measures rely on data deidentification and hierarchical management of access. Conclusion : patient information remains difficult. Deidentification procedures are to be improved and
Studies in health technology and informatics, 2012
Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situa... more Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situations. K-nearest neighbors algorithm (K-NN) have been used in CBR systems to define new cases status according to characteristics of past nearest cases. We proposed a new hybrid approach combining logistic regression (LR) with K-NN to optimize CBR classification. First, we analyzed the knowledge database by LR procedures and the Pearson residuals of the LR model were used to define cases' utility of the knowledge database into K-NN. Secondly, we compared the classification performances of LR model and K-NNs coupled or not with LR. Our results showed that the information provided by the residuals could be used to optimize the settings of K-NN and to improve CBR classification.
Studies in Health Technology and Informatics, 2021
The development of precision medicine in oncology to define profiles of patients who could benefi... more The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to targeted therapies. This study aimed to develop an automated algorithm based on natural language processing to detect patients and tumor characteristics to reduce the time-consuming prescreening for trial inclusions. Hence, 640 anonymized multidisciplinary team meeting (MTM) reports concerning lung cancer were extracted from one teaching hospital data warehouse in France and annotated. To automate the extraction of 52 bioclinical information corresponding to 8 major eligibility criteria, regular expressions were implemented and evaluated. The performance parameters were satisfying: macroaverage F1-score 93%; rates reached 98% for precision and 92% for recall. In MTM, fill rates variabilities among patients and tumors information remained i...
Studies in Health Technology and Informatics, 2021
Surveillance and traceability of medical devices (MD) is a challenge in health care systems. In t... more Surveillance and traceability of medical devices (MD) is a challenge in health care systems. In the perspective of reusing EHR data to automate the monitoring of medical devices, we carried out a comparison of the main MD knowledge bases (MD-KDB) currently available in France. Four MD-KDBs (ANSM, Gudid, Exhausmed and CIOdm) were compared quantitatively and through an example of a shoulder prosthesis. The number of MDs registered differs from one MD-KDB to another. Domain terminologies used in MD-KDBs differ in terms of granularity and in the ease of querying. Waiting EUDAMED, the European MD-KDB, it seems necessary so far to use and combine information coming from several MD-KDBs to address MD monitoring.
Studies in Health Technology and Informatics, 2021
HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimb... more HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimbursed by the social security in France. Yet, PrEP is underused due to the difficulty to identify people at risk of HIV infection outside the “sexual health” care path. We developed and validated an automated algorithm that re-uses Electronic Health Record (EHR) data available in eHOP, the Clinical Data Warehouse of Rennes University Hospital (France). Using machine learning methods, we developed five models to predict incident HIV infections with 162 variables that might be exploited to predict HIV risk using EHR data. We divided patients aged 18 or more having at least one hospital admission between 2013 and 2019 in two groups: cases (patients with known HIV infection in the study period) and controls (patients without known HIV infection and no PrEP in the study period, but with at least one HIV risk factor). Among the 624,708 admissions, we selected 156 cases (incident HIV infection) ...
Background Artificial intelligence (AI) has the potential to transform our healthcare systems sig... more Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timi...
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