GB2614862A - Clinical Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) in adults - Google Patents
Clinical Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) in adults Download PDFInfo
- Publication number
- GB2614862A GB2614862A GB2305959.5A GB202305959A GB2614862A GB 2614862 A GB2614862 A GB 2614862A GB 202305959 A GB202305959 A GB 202305959A GB 2614862 A GB2614862 A GB 2614862A
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- GB
- United Kingdom
- Prior art keywords
- model
- machine learning
- adhd
- clinical
- outcome
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Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Abstract
A computer system and method for performing a clinical diagnosis of ADHD in adults is described. A machine learning model is trained on clinical data pertaining to ADHD diagnosed adult patients. The clinical data comprises a set of clinical parameters. The machine learning model is trained on the clinical data to predict as an outcome a positive diagnosis of ADHD or a negative diagnosis of ADHD based on corresponding input clinical parameters. A computer implemented knowledge model is configured to implement a sequence of rules, the rules assessing the input clinical parameters and generating as an outcome either a positive diagnosis of ADHD, a negative diagnosis of ADHD or an indication that expert input is required. A user interface receives a set of input clinical parameters corresponding to a patient to be diagnosed, supplies those input parameters to each of the machine learning model and the knowledge model and presents an output decision to a user. An output model receives an outcome from the machine learning model and an outcome from the knowledge model and generates the output decision based on the outcome of the machine learning model and the outcome of the knowledge model.
Claims (26)
1. A computer system for performing a clinical diagnosis of ADHD in adults, the computer system comprising: a machine learning model which has been trained on clinical data pertaining to ADHD diagnosed adult patients, the clinical data comprising a set of clinical parameters, the machine learning model having been trained on the clinical data to predict as an outcome a positive diagnosis of ADHD or a negative diagnosis of ADHD based on corresponding input clinical parameters; a computer implemented knowledge model configured to implement a sequence of rules, the rules assessing the input clinical parameters and generating as an outcome either a positive diagnosis of ADHD, a negative diagnosis of ADHD or an indication that expert input is required; a user interface configured to receive a set of input clinical parameters corresponding to a patient to be diagnosed and to supply those input parameters to each of the machine learning model and the knowledge model and to present at least an output decision to a user; and an output model connected to receive an outcome from the machine learning model and an outcome from the knowledge model and to generate the output decision based on the outcome of the machine learning model and the outcome of the knowledge model.
2. The computer system of claim 1 wherein the machine learning model is configured to provide a confidence score with the predicted outcome, the confidence score indicating the degree of confidence associated with the outcome predicted by the machine learning model.
3. The computer system of claim 1 or 2, wherein the output model is configured to generate as the output decision a positive diagnosis of ADHD when the outcome of the machine learning model and the outcome of the knowledge model both indicate a positive diagnosis.
4. The computer system of claim 1 or 2, wherein the output model is configured to generate the output decision as a negative diagnosis when the machine learning model and the knowledge model both indicate a negative diagnosis of ADHD.
5. The computer system of claim 1 or 2, wherein the output model is configured to generate as the output decision that expert input is required when the outcome of the machine learning model differs from the outcome of the knowledge model.
6. The computer system of any preceding claim wherein the user interface is configured to display to a user the outcomes of one or both of the machine learning model and the knowledge model.
7. The computer system of claim 6, wherein the user interface is configured to display a respective reasoning output associated with the outcome of one or both of the machine learning model and the knowledge model.
8. The computer system of any preceding claim wherein the sequence of rules of the knowledge model comprise a first rule which compares at least one clinical parameter score pertaining to a structured questionnaire carried out by a clinician with a corresponding threshold and indicates a negative diagnosis if the clinical parameter score is less than the threshold.
9. The computer system of any of claim 8, wherein the sequence of rules of the knowledge model may comprises a second rule which determines whether multiple clinical parameters are indicated relative to respective thresholds and if so, generates an outcome that expert opinion is needed.
10. The computer system of claim 8 or 9, wherein the sequence of rules of the knowledge model comprises a third rule which compares the at least one clinical parameter score with a corresponding threshold and which indicates a positive diagnosis of ADHD if the clinical parameter score exceeds the corresponding threshold.
11. A method of operating a diagnostic tool comprising entering into respective fields of a graphical user interface input patient case data comprising clinical parameters indicative of a state of a patientâ s mental health, and causing the clinical parameters to be evaluated by a computer system according to any preceding claim.
12. The method of claim 11, wherein clinical parameters are selected from a set of indicators which comprise at least one indicator which represents a score derived from a structured questionnaire.
13. The method of claim 11 or 12, wherein the indicators comprise one or more of the following: an indicator of a depression state, and indication of an anxiety state, an indication of a bipolar state, an indication of alcohol use, an indication of substance abuse, an indication of brain injury, and an indication of a personality disorder.
14. The method of claim 12 or 13, comprising determining for each indicator whether or not its value exceeds a threshold associated with a respective one of the machine learning model and knowledge model, and applying the value of the indicator to the respective model only if it exceeds the respective threshold.
15. The method of any of claims 11 to 14, comprising applying a subset of clinical parameters to each of the knowledge model and machine learning model, the respective subset applied to each model having been selected based on a determination of which clinical parameters are statistically relevant when used in that particular model.
16. The method of claim 15, wherein the subset of clinical parameters applied to the machine learning model differs from, and overlaps with, the subset of indicators applied to the knowledge model.
17. The method of claim 15, wherein the subset of clinical parameters applied to the machine learning model differs from, and does not overlap with , the subset of indicators applied to the knowledge model.
18. A method of configuring a diagnostic tool for diagnosing ADHD in adults, the method comprising: generating a user interface for a diagnostic computer system, the user interface comprising a set of input fields, each for receiving a value of a clinical parameter, the value indicative of a patientâ s mental health state, wherein the set of input fields has been selected based on a determination of clinical parameters which have been found to be statistically significant in the diagnosis of ADHD in adults.
19. The method of claim 18, comprising determining the statistical relevance of each clinical parameter by using knowledge of a human expert skilled in the diagnosis of ADHD in adults.
20. The method of claim 18, wherein the determination of clinical parameters which have been found to be statistically significant comprises training a machine learning model using a collection of clinical parameters which have been determined to have some bearing in the diagnosis of ADHD in adults, and extracting features from the trained machine learning model using a machine learning feature selection process which determines features which are statistically relevant to decision outcomes of the machine learning model.
21. A computer implemented method of processing clinical data comprising: receiving input patient case data via a user interface of a computer system, the input data comprising a set of indicators, each indicator comprising a value indicative of a patientâ s mental health state; applying at least a respective subset of the indicators to each of two models of the computer system, the two models comprising a machine learning predictive model and a knowledge model; determining using the predictive machine learning model a first diagnostic outcome based on the indicators; determining using the knowledge model a second diagnostic outcome based on the indicator; combining the first and second outcomes to generate a decision output which indicates whether the indicators are indicative of a positive diagnosis of ADHD, whether the indicators are indicative of a negative diagnosis of ADHD, or whether the indicators indicate that expert opinion is required.
22. The computer implemented method of claim 21, wherein the subset of indicators applied to each model has been selected based on a determination of which indicators are statistically relevant when used in that particular model on a system for performing a diagnosis.
23. The computer-implemented method according to claim 21, wherein the set of indicators comprises scores derived from a structured questionnaire.
24. The computer implemented method according to claim 21, further comprising obtaining the input patient case data by clinical interview of a patient.
25. The computer implemented method according to claim 24, wherein the clinical interview is a structured clinical interview in accordance with one or more questionnaires comprising DIVA and optionally one or more selected from CAARS, DAST-10, IOWA, AUDIT, MDQ, GAD-7, PHQ-9, and HELPS.
26. The computer implemented method according to claim 24 or claim 25, wherein the patient is an adult.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GBGB2017793.7A GB202017793D0 (en) | 2020-11-11 | 2020-11-11 | Clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
| PCT/EP2021/081442 WO2022101368A1 (en) | 2020-11-11 | 2021-11-11 | Clinical diagnosis of attention deficit hyperactivity disorder (adhd) in adults |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| GB202305959D0 GB202305959D0 (en) | 2023-06-07 |
| GB2614862A true GB2614862A (en) | 2023-07-19 |
Family
ID=74046477
Family Applications (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GBGB2017793.7A Ceased GB202017793D0 (en) | 2020-11-11 | 2020-11-11 | Clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
| GBGB2102103.5A Ceased GB202102103D0 (en) | 2020-11-11 | 2021-02-15 | Clinical diagnosis of attention deficit hyperactivity disorder (ADHD) and autism in adults |
| GB2305959.5A Pending GB2614862A (en) | 2020-11-11 | 2021-11-11 | Clinical Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) in adults |
Family Applications Before (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GBGB2017793.7A Ceased GB202017793D0 (en) | 2020-11-11 | 2020-11-11 | Clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults |
| GBGB2102103.5A Ceased GB202102103D0 (en) | 2020-11-11 | 2021-02-15 | Clinical diagnosis of attention deficit hyperactivity disorder (ADHD) and autism in adults |
Country Status (2)
| Country | Link |
|---|---|
| GB (3) | GB202017793D0 (en) |
| WO (1) | WO2022101368A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116895367B (en) * | 2023-09-11 | 2023-12-22 | 北京智精灵科技有限公司 | Method and system for pushing hyperkinetic symptom training scheme based on brain function training |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190019581A1 (en) * | 2015-12-18 | 2019-01-17 | Cognoa, Inc. | Platform and system for digital personalized medicine |
| US20190147128A1 (en) * | 2016-06-14 | 2019-05-16 | 360 Knee Systems Pty Ltd | Graphical representation of a dynamic knee score for a knee surgery |
| WO2020198065A1 (en) * | 2019-03-22 | 2020-10-01 | Cognoa, Inc. | Personalized digital therapy methods and devices |
-
2020
- 2020-11-11 GB GBGB2017793.7A patent/GB202017793D0/en not_active Ceased
-
2021
- 2021-02-15 GB GBGB2102103.5A patent/GB202102103D0/en not_active Ceased
- 2021-11-11 WO PCT/EP2021/081442 patent/WO2022101368A1/en active Application Filing
- 2021-11-11 GB GB2305959.5A patent/GB2614862A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190019581A1 (en) * | 2015-12-18 | 2019-01-17 | Cognoa, Inc. | Platform and system for digital personalized medicine |
| US20190147128A1 (en) * | 2016-06-14 | 2019-05-16 | 360 Knee Systems Pty Ltd | Graphical representation of a dynamic knee score for a knee surgery |
| WO2020198065A1 (en) * | 2019-03-22 | 2020-10-01 | Cognoa, Inc. | Personalized digital therapy methods and devices |
Also Published As
| Publication number | Publication date |
|---|---|
| GB202017793D0 (en) | 2020-12-23 |
| WO2022101368A1 (en) | 2022-05-19 |
| WO2022101368A9 (en) | 2022-08-11 |
| GB202102103D0 (en) | 2021-03-31 |
| GB202305959D0 (en) | 2023-06-07 |
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