A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases
1.1. Medical Evaluation Process
- Search for information on the patient’s health status.
- Assessment of the collected information.
- Development of a treatment plan.
- Subjective data: symptoms.
- Objective data: signals.
1.2. Use of Expert Systems in Decision-Support Tools
1.3. Expert Systems in Health Applications
1.4. Current Approaches for the Detection of Hypoxemic Clinical Cases
1.4.1. Measurement Instruments Used in the Determination of Hypoxemic Clinical Cases
1.4.2. Clinical Decision Support Systems Applied to the Detection and Monitoring of Hypoxemic Cases
1.5. Prior Considerations
2. Materials and Methods
2.1. Definition of the Methodology
2.1.1. Previous Considerations
2.1.2. Conceptual Design and Description of the Methodology
- Personal information: name, identifier, date of birth, race, and sex.
- Respiratory diseases: respiratory conditions the patient has been diagnosed with, such as for example chronic obstructive pulmonary disease (COPD), asthma, etc.
- Harmful habits: If she is/is not a smoker, and in the former case for how long and how many cigarettes she usually smokes per day.
Data Reading and Interpretation Stage
Monitoring and Alert Generation Stage
- Wait: It is not required to assist the patient, as it is considered that her health status does not demand it.
- Warning: It is recommended to carry out new measurements within a pre-determined period of time by the system in order to reassess the patient’s health status. The healthcare professional might provide indications about the care to be given to the patient if it is necessary.
- Emergency: It is recommended to send the emergency systems immediately to assist the patient.
2.2. Implementation of the Methodology
2.2.1. Definition of the Global Hypoxemia Risk Concept
2.2.2. Definition and Calculation of Technical and Expert Risk
Behavior of the Expert Risk Parameter
- When the technical risk value is less than or equal to 50, a high decision factor value will be required to obtain a high global value risk, as a result of applying Equation (1). That is why as the expert risk value raises, the decision factor value must also grow fast. This means that if the technical risk values do not indicate an alert, the expert will be responsible in exclusive of raising her valuation of the hypoxemic seriousness level by means of her assessment, which directly will result in a high expert risk value.
- If the technical risk value is greater than 50, then it will not be required the decision factor value to be as high as before to obtain a high global hypoxemic risk value, and thus high global hypoxemic risk values may be obtained with smaller expert risk values. From this, it is derived that, in situations where the technical risk value is high, the alert will prevail on the expert’s considerations, thus forcing her to justify any low evaluation results that reduce the potential alert level.
Corrections on the Decision Factor Definition Curves
- Exponential zone: This zone applies to technical risk values less than or equal to 50. As it was explained before, while the exponential growth zone is not reached the decision factor value is almost constant. After that, once the exponential growth zone is reached, the decision factor grows fast. It might be the case that the expert wishes the growth zone to be reached before, or that a growth rate is produced that is higher than the one provided by the exponential function. In this way, the expert is allowed to perform modifications on the decision factor within a range of values of the expert risk. A new correction is proposed in this case, so that the expert has the capability to change the growth rate of the exponential function by establishing a linear transition zone in the equation that defines the decision factor. To do so, an expert risk value must be defined from which the decision factor values are intended to be modified and the desired growth rate is to be established, this rate being at all times delimited in the interval defined by . For example, in the case that the expert wishes to perform a correction starting for expert risk values higher than 30 with a maximum growth rate, the expression to be used is shown in Equation (8), that in essence represents a straight line.
- Logarithmic zone: In the first segment of the expert risk, for values up to 30–40, decision factor values are produced that cause the global hypoxemic value to be lower than the technical risk. That is why a correction is proposed aiming to avoid a potential undervaluation on the side of the expert that might avoid the generation of warning or emergency alert levels when the expert is not fully convinced of it. When percentage variations exist between the technical risk and the global hypoxemic risk larger than a threshold value, for example a 15%, the expert will be asked about how sure she is about her assessment, and the decision factor value will be corrected according to Equation (9).
2.2.3. Determination of the Global Hypoxemic Risk and the Alert Level
Corrections on the Exponential–Logarithmic Transition Zone of the Global Risk Surface
2.2.4. Alert Evaluation
- Wait: when the global risk value is contained in (0–60).
- Warning: when the global risk value is contained in (60–80).
- Emergency: when the global risk value is contained in (80–100).
3. Simulation and Results
- Oxygen blood level: 92%.
- Heart rate: 80 beat/min.
- Temperature: 37 °C.
- Assessment of measurements: medium-low risk level (3/10).
- Assessment of the patient’s medical history: In this case the risk level is medium-high, as the patient is affected by sleep apnea and chronical obstructive pulmonary disease (COPD) (7/10).
- Assessment of any other potential risk factors: The risk is high, as the patient has an at-risk job (10/10).
Interpretation of the Results
- The proposed methodology allows to integrate all those aspects related with improvements in the reliability, placement and interpretation of the pulse-oximeter readings. In this case, the integration of this device is carried out by means of an inference system that fuzzifies its measurements. This makes possible to improve its qualitative interpretation. In the same way, suggestions related to its placement or to the threshold values may be directly implemented in the membership functions of the technical and expert risk antecedents, by means of the assessment of the information collected in all the related studies. They can even be reflected on the definition of the global risk ranges associated to the definition of the recommendations.
- All the works analyzed related to decision support systems aimed to the detection of hypoxemic cases are based on the integration of data and the determination of values for warning and alert thresholds. According to this approach, the determination of the global risk in the presented methodology is similar to those with the following considerations:
- The global risk is a function calculated from the inference of the technical risk and the expert risk, which allows to integrate quantitative and qualitative variables into the calculation process, thus incorporating the assessment of the healthcare team.
- The global risk ranges associated to the recommendations can always be modified by the healthcare team, being liable to alterations or modifications adapted to the collected dataset.
- The processing of the initial data of both inference systems is performed by means of the definition of membership functions, reducing the uncertainty in the process of quantification of its qualitative expression.
- The use of expert systems, a key feature of this methodology, makes possible as already explained the diversification and formalization of the information related to the detection of hypoxemic conditions, thus allowing the methodology to be used by different experts possessing different training and experience.
- The incorporation of corrections, as indicated in the results section, significantly extends the usefulness of the methodology. It has already been argued in this section that the benefits of this type of support tools to the decision process is currently under question. The presence of corrections reinforces its character to support and to help the diagnosis process, but always leaving the final decision to the healthcare team, which always may re-interpret the results provided by the expert systems.
- The interface, same as happens in the works by Karlen et al. , Keerthika and Ganesan , Isik and Güler , and Merone et al. , makes possible to implement a system for the application of the methodology. Even if it is has not being incorporated yet into mobile devices, such a realization would be simple. Additionally, the software artifact developed was conceived for its incorporation into clinical information systems, therefore being useful at times in which, either because of the large number of patients, or because of the widespread dissemination of a disease, said systems work at saturation conditions. A clear example of this is the health system collapse caused in many areas by the COVID-19 pandemic that has circulated worldwide in 2020.
- Both the concurrence in the inference of the expert systems and the global risk calculation and the corrections, provide an effective supervision of the uncertainty. It is convenient to highlight that, further away from the risk that is evaluated here, a more essential risk exists associated to this uncertainty that measures what is unknown or what cannot be assessed. It is clear that, for all expert systems, to evaluate what is uncertain is a key point that, in this case, gets even bigger because of the particular importance of the specific topic dealt with. The methodology does not only reduce the uncertainty from the collection and interpretation of the input data, but also makes possible to handle what is uncertain by means of the correction, thus building trust in the processes.
- Lastly, it must be highlighted that the presented methodology is modular, meaning that it will always be possible to incorporate it into other inference engines or prediction algorithms. This integrating capability, derived from its own design, allows other developments in the field of study to be integrated into it, if that is the case.
Conflicts of Interest
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|Rule 1: Design an artifact (presented methodology)|
|The artifact, i.e., the methodology detailed in Section 2.2, is a tool aimed to help in the process for assessing the health status of patients prone to developing respiratory diseases.|
|Rule 2: Relevance of the problem|
|The assessment of the health status of patients liable to develop potential medical hypoxemia cases is nowadays a topic of vital importance, of undoubtable relevance because respiratory diseases are globally the fifth most relevant cause of death [1,2], and more especially during 2020 due to the pandemic situation caused by COVID-19.|
|Rule 3: Design evaluation|
|The application of a new methodology is demonstrated in the practical case shown in Section 3.1.|
|Rule 4: Contributions to the field of research|
|The contributions to the field of new expert systems applied in the context of decision-making support in medicine are presented in Section 4 and Section 5 of this article.|
|Rule 5: Rigor in the research|
|The conceptual development of the presented methodology has been defined in Section 2.1, as was its classification within the field of research. In the same way, the mathematical groundings of this work are supported on the use of fuzzy-logic inference systems, and the definition of the risk functions and their combinations has adhered at all times to the appropriate mathematical rigor.|
|Rule 6: Design as a search process|
|In Section 1, the methodology was framed within the state of the art of similar applications, observing its differential aspects. These differences are discussed in Section 4.|
|Rule 7: Communication of the research|
|In Section 4 and Section 5, the main contributions of the new method are presented, as well as the future lines of work.|
|Date of Birth:||Race:||Sex(M/F):|
|Questions to Be Answered:||Yes||No|
|Have you been abroad in the last month?|
|If yes, please, name the region where have you been.|
|If yes, for how long, in months, have you been abroad?|
|Have you had any risk contact with people affected by—or that might be affected of—COVID-19?|
|Have you suffered—or are you suffering now—of fever, dry cough, or fatigue?|
|Have you had—or do you have now—breathing difficulties or a shortness of breath feeling?|
|Have you been—or are you now—affected by loss of smell or taste, headache, or any other discomfort or pain?|
|Do you have an occupation that involves risk to the respiratory system—for example, participation in mining operations?|
|If yes, please, explain which occupation it is|
|Technical Risk Fuzzy Inference System.|
|Input data||Range||Output data||Range|
|Oxygen saturation||70–100%||Technical Risk||0–100 1|
1 Actual range is 10–100, for more details see Section 2.2.1.
|Temperature||33–41 °C||Initial configuration|
|Fuzzy structure: Mamdani–type.|
Membership function type: trapezoidal.
Defuzzification method: centroid .
|Implication method: MIN.|
Aggregation method: MAX.
Number of fuzzy rules: 45.
|Heart rate||15–180 b.p.m||Subset of the 45 fuzzy rules|
1. IF (O2_Saturation is Very_Low) AND (Pulse is Normal) THEN (Technical_Risk is R5)
2. IF (O2_Saturation is not Very_Low) AND (Temperature is Normal) AND (Heart_Rate is Normal) THEN (Technical_Risk is R1)
3. IF (O2_Saturation is not Very_Low) AND (Temperature is Normal) AND (Heart_Rate is High) THEN (Technical_Risk is R1)
|Example of combination of fuzzy rules 2 and 3|
|Expert Risk Fuzzy Inference System|
|Input data||Range||Output data||Range|
|Sensors’ measurement assessment||0–10||Expert Risk||0–100 2|
2 Actual range is 10–100, for more details see Sub-Section 2.2.1.
|History assessment||0–10||Initial configuration|
|Fuzzy structure: Mamdani–type.|
Membership function type: trapezoidal.
Defuzzification method: centroid .
|Implication method: MIN.|
Aggregation method: MAX.
Number of fuzzy rules: 29.
|Assessment of other factors||0–10||Subset of the 29 fuzzy rules|
1. IF (Sensors_Measurement_Assessment is Medium) AND (History_Assessment is Low) AND (Assessment_Other_Factors is Low) THEN (Expert_Risk is R1)
2. IF (Sensors_Measurement_Assessment is Low) AND (History_Assesment is High) AND (Assessment_Other_Factors is High) THEN (Expert_Risk is R5)
|Example of combination of fuzzy rules 1 and 2|
|Patient||O2 Conc.||Heart Rate (beat/min)||Temp. (°C)||History||Other Factors|
|1||92||80||37||55 y.o., sleep apnea and COPD||Mining job|
|2||87||50||38.5||60 y.o., smoker and sedentary||-|
|3||80||60||37.1||47 y.o., lung cancer||-|
|4||93||140||38||18 y.o., obesity and asthma||-|
|5||83||80||39||78 y.o., ex-smoker||-|
|7||95||56||36.5||15 y.o., asthma||-|
|8||90||72||36||35 y.o., smoker||Stone work job|
|9||89||55||35.9||93 y.o., ex-smoker||-|
|10||75||50||38.5||70 y.o., lung oedema||-|
|12||89||74||36.6||26 y.o., smoker||Risky contacts|
|13||92||56||37||45 y.o., sporty||-|
|14||87||83||37.1||44 y.o., post-surgery||-|
|16||65||50||35.8||87 y.o., palliative care||-|
|17||86||92||37.2||17 y.o., obesity||-|
|19||74||63||35.9||50 y.o., alcoholic and smoker||-|
|21||89||66||36.7||67 y.o., ex-smoker and sedentary||-|
|22||82||70||37.2||52 y.o., post-surgery||-|
|23||92||68||36.3||84 y.o., sporty||-|
|24||70||51||35.8||77 y.o., lung cancer||-|
|25||89||66||36.3||36 y.o., asthma||-|
|26||89||66||36.3||36 y.o., asthma||-|
|27||87||94||37.2||59 y.o., COPD||-|
|29||96||71||36.7||38 y.o., smoker||-|
|30||82||84||36.4||66 y.o., sleep apnea||-|
|Patient||RT||RE||RG||Recommended State||Actual State|
|Binary classification groups||Classification state||Number of Cases|
|Correctly classified patients||True positive (tp)||16|
|True negative (tn)||7|
|Wrongly classified patients||False positive (fp)||6|
|False negative (fn)||1|
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Comesaña-Campos, A.; Casal-Guisande, M.; Cerqueiro-Pequeño, J.; Bouza-Rodríguez, J.-B. A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases. Int. J. Environ. Res. Public Health 2020, 17, 8644. https://doi.org/10.3390/ijerph17228644
Comesaña-Campos A, Casal-Guisande M, Cerqueiro-Pequeño J, Bouza-Rodríguez J-B. A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases. International Journal of Environmental Research and Public Health. 2020; 17(22):8644. https://doi.org/10.3390/ijerph17228644Chicago/Turabian Style
Comesaña-Campos, Alberto, Manuel Casal-Guisande, Jorge Cerqueiro-Pequeño, and José-Benito Bouza-Rodríguez. 2020. "A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases" International Journal of Environmental Research and Public Health 17, no. 22: 8644. https://doi.org/10.3390/ijerph17228644