AU2021101498A4

AU2021101498A4 - A method for evaluating mental health disorders to provide a medicine for the same

Info

Publication number: AU2021101498A4
Application number: AU2021101498A
Authority: AU
Inventors: Rajesh Bose; Sandip Roy
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-03-23
Filing date: 2021-03-23
Publication date: 2021-05-13
Anticipated expiration: 2029-03-23

Abstract

The present disclosure seeks to provide a method for evaluating mental health disorders to provide a medicine for the same. The target is to create a podium which will be able to categorize the dataset into reappearance and non-reappearance. It should be noted that no reappearance on the basis of age, sex, drug and therapy time formerly and later of providing drugs to the patients. It is also speculated, which drug should be provided to the patient if the patient has a recurrence of such ailment even after the remission of it. On the basis of the recurrence issue, the suggested algorithm of the present disclosure proposes to intake the drug on the basis of the assorted forms which are filled by the victim of such ailments while visiting hospitals. 13 100 > Receiving a set of queries filled by a user through a user 102 int erface Calculating entropy of a database input on the basis of 1G4 gender, treat and age for classifying the database in consonance with maximum entropy Predicting recurrence and non-recurrence impIemented for 106 the speculation of the drug upon considering maximum entropy using a machine learning technique Figure1 - OWaum maanwn '"**"." mole ...e e&MAPY. OEMM.Am~ea.. " a*4 Figure2

Description

100 >

Receiving a set of queries filled by a user through a user 102 int erface

Calculating entropy of a database input on the basis of 1G4 gender, treat and age for classifying the database in consonance with maximum entropy

Predicting recurrence and non-recurrence impIemented for 106 the speculation of the drug upon considering maximum entropy using a machine learning technique

Figure1

-

...eOWaum maanwn '"**"."mole

OEMM.Am~ea.. e&MAPY. " a*4

Figure2

AMETHOD FOR EVALUATING MENTAL HEALTH DISORDERSTO PROVIDE A MEDICINE FOR THE SAME FIELD OF THE INVENTION

The present disclosure relates to a method for evaluating mental health disorders to provide a medicine for the same. It seeks to create a podium which will be able to categorize the dataset into reappearance and non-reappearance. The suggested algorithm also speculates which drug should be provided to the patient if the patient has a recurrence of such ailment even after the remission of it.

BACKGROUND OF THE INVENTION

Life demands from individuals to resolve different kinds of major problems of different and significant events. If individuals want to come to any conclusion of a particular event, they are inclined to depend upon their own skill, understanding, maturity, experience, analytical competence etc. Besides, sometimes they depend on the advice of other adepts regarding an issue. Nevertheless, various adepts' solutions of a particular issue may vary. The various doctors' solutions of a particular health issue of a patient may vary on account of their personal discernments. Confusions created by worry are a customary psychological ailment in the USA. It has made psychological health hazards, to 40 million grownups in the USA, who are eighteen year old or older than that. If you gather recent yesteryears' statistics, you will find that every year 1 8 .1% of the total number of people of the USA should be treated for the ailment created by worries. But just 3 6 .9 % of such patients get therapy for it. When further reports were accumulated, the number of individuals who are admitted in hospitals every year due to the ailments created by psychological issues, they are six times more inclined than the number of individuals not getting affected by the ailments created by worry, are three to five times more inclined to visit the physicians than to get admitted in hospitals. As reported in 2017, an observation was made that 197.3 million ( 9 5% UI 178.4-216.4) citizens were affected by psychological ailments in India, counting 45.7 million (42.4-49.8) in the midst of ailments created by dejection and 44.9 million (41.2-48.9) in the midst of ailments created by worry. At present, dejection reveals a customary psychological illness that make above 264 million individuals all over the globe. When one is affected by it, the symptoms as follows, are visible in one: unrelenting melancholy, frustration and many more. One won't have any kind of satisfaction or happiness in mind. This will also lead the dejected patients to some customary symptoms such as insomnia, loss of appetite, weariness and inattentiveness in one's concerned work. In this disclosure it is considered that dejection is an acute problem of the affliction n all over the globe. Besides, dejection gives a severe impact on the ailments which the whole world is utterly concerned about. On account of dejection a man is severely affected even by health, which leads him to be unable to perform his own work in future, as mental illness tells upon one's physical health too. If one is dejected, then it is seen that in one's lifespan, one is attacked with such syndrome with a number of times. It is also observed that dejection engulfs many individuals, making their life even at stake. Dejection shows different symptoms in different affected victims. It is seen that this turns out to be a persistent ailment for several patients. Along with that, this also shows a renewal habit for a great many individuals. It is also observed that generally, a man who ha gets affected by dejection gets such hazards four to five times in his lifespan. Deterioration related to dejection is in reality of different event which is visible in the lifespan of less than six months, once a patient gets diagnosed with severe dejection. The reappearance shows sometimes a few novel attribute which appear almost after a semester or more than the first diagnosis is made. In spite of having the timeframe, this will able to be deflating for sensing dejection indications like melancholy, weariness, and bad temper. If there is the recurrence or setback of dejection, the treatment of it is able to be made in numerous methods. One method is the amalgamation of treatments or doctor may refer to antidepressant treatment and psychological treatment. Several studies have been initiated, and they have provided a number of dreary reports that have spotted the predictions of worsening of dejection of one affected by this. Researchers have stated that dejection is a severely periodic ailment, and if one is affected by it once, it will generally be visible in one within 5 years of it. It is also stated that an average rate of dejection visible in one, also get five to nine different dispiriting periods in his total lifespan. There is much probability of a person affected by dejection once, of 5 0 %, to get into this trouble once more, even after diminution of the severity of this ailment. If the person gets affected by dejection twice, the hazard becomes 70% and if he gets affected by it thrice or more than that, then the hazard becomes almost 90%. In Major depressive disorder (MDD), the reappearance of the indications is generally visible in the affected individuals, every after two successive months and it occurs in the meantime of the different periods of time on which time benchmarks do not come across for a Major Depressive Episode (MDE). There is also a mandatory comeback of indications of minimum five among nine indications of dejection. Several victims affected by these kinds of considerable hazards of the reappearance afterwards, are with % lifespan the hazard of reappearance after the initial most important dismal events. Among this stat, an observation was made that seventy percent of them together with two MDE have reappearances of life, and ninety percent of these together with 3 or more than that have occurrence of additional periodic events. Moreover, seventy five percent to half of the victims affected by such disease in a year of absence of therapy have a relapse of such disease. Whenever any reappearance is observed, it conveys ten to twenty percent danger of turning out to be and consistent. On the contrary, persistent MDD augments hazards of noteworthy useful harm, suicide and co morbid corporeal wellbeing hazards sustaining serious physical condition and financial problems. On account of it, researchers have been at rest incessantly going on with their exertion diverse features for exploring the elucidation in the deterioration to the victims who are dejected.

In order to overcome the aforementioned issues, there exists a need to develop a method for evaluating mental health disorders to provide a medicine for the same.

SUMMARY OF THE INVENTION

The present disclosure seeks to provide a method for evaluating mental health disorders to provide a medicine for the same. The target is to create a podium which will be able to categorize the dataset into reappearance and non-reappearance. It should be noted that no reappearance on the basis of age, sex, drug and therapy time formerly and later of providing drugs to the patients. It also speculates, which drug should be provided to the patient if the patient has a recurrence of such ailment even after the remission of it. On the basis of the recurrence issue, the suggested algorithm of the present disclosure proposes to intake the drug on the basis of the assorted forms which are filled by the victim of such ailments while visiting hospitals.

In an embodiment, a method for evaluating mental health disorders to provide a medicine for the same, the method comprises: receiving a set of queries filled by a user through a user interface; calculating entropy of a database input on the basis of gender, treat and age for classifying the database in consonance with maximum entropy; and predicting recurrence and non-recurrence implemented for the speculation of the drug upon considering maximum entropy using a machine learning technique.

An objective of the present disclosure is to create a method for evaluating mental health disorders to provide a medicine for the same.

An objective of the present disclosure is to create a podium which will be able to categorize the dataset into reappearance and non reappearance.

Another objective of the present disclosure is that on the basis of the recurrence issue, the suggested algorithm of the present disclosure proposes to intake the drug on the basis of the assorted forms which are filled by the victim of such ailments while visiting hospitals.

To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES

These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1 illustrates a flow chart of a method for evaluating mental health disorders to provide a medicine for the same, in accordance with an embodiment of the present disclosure. Figure 2 illustrates a block diagram of Judgment Analysis algorithm for the proposed model in accordance with an embodiment of the present disclosure. Figure 3illustrates (a) Aprocess flow diagram of the python-driven web application; (b) User input dataset; (c) Predicted Medicine between Random Forest, K-Nearest Neighbour and Judgement Analysis Algorithm; and (d)Accuracy between Random Forest, K-Nearest Neighbour and Judgement Analysis in accordance with an embodiment of the present disclosure

Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

Referring to Figure 1 illustrates a flow chart of a method for evaluating mental health disorders to provide a medicine for the same, in accordance with an embodiment of the present disclosure.

In an embodiment, the method 100 comprises: At step 102, receiving a set of queries filled by a user through a user interface; At step 104, calculating entropy of a database input on the basis of gender, treat and age for classifying the database in consonance with maximum entropy; and At step106 predicting recurrence and non-recurrence implemented for the speculation of the drug upon considering maximum entropy using a machine learning technique.

Figure 2 illustrates a block diagram of Judgment Analysis algorithm for the proposed model in accordance with an embodiment of the present disclosure. The suggested procedure of this disclosure shows that it has categorized the dataset in regards with the breakage and "Sex" data that contains most of the breakages, as shown in Figure 2. Next is the categorization of the dataset in regards with "Sex" and "Treat". This dataset contains most of the breakages. Then further categorization of the dataset in regards with the "Treat" and "Age" takes place. They comprise most of the breakages. After that, finally categorization of the dataset in regards with the "Age" is completed. At this time, the result is predicted with the help of the dataset. In the algorithm which has suggested the making of Figure 2, the input is from the consumer. Besides, the predict() function can be summoned and it fetches the result (Recurrence or Non Recurrence). Then get the utmost happenings can be retrieved. After that if the utmost happenings retrieved, are "Non Recurrence" then the drugs that are akin to consumer input are brought back. On the contrary, get back the drugs in which the result gets "Non Recurrence" which is something different from the consumer input. Here Judgment Analysis (JA) Algorithm (Algorithm 1) has been implemented for the speculation of the drug on the basis of decision trees that is depicted in Figure 2.

Figure 3 illustrates (a) A process flow diagram of the python-driven web application; (b) User input dataset; (c) Predicted Medicine between Random Forest, K-Nearest Neighbour and Judgement Analysis Algorithm; and (d)Accuracy between Random Forest, K-Nearest Neighbour and Judgement Analysis in accordance with an embodiment of the present disclosure. For the execution of suggested algorithms, in this disclosure, the configured python-controlled web application end user that we have shown in Figure 3a are constituted. Figure 2 has represented a tabular representation of Mock-up trial events from the users. Besides it has conjectured the drug between Random Forest, K-Nearest Neighbour and Judgment Analysis Algorithm that also Figure 3c has illustrated. Figure 3d has depicted the precision of this research's suggested Random Forest (ACCRF), K-Nearest Neighbour (ACCKNN), and Judgment Analysis (ACCJA) algorithms. It gives the evidence of the execution development of this subsisting literature.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

Claims

WE CLAIM

1. A method for evaluating mental health disorders to provide a medicine for the same, the method comprises:

Receiving a set of queries filled by a user through a user interface; calculating entropy of a database input on the basis of gender, treat and age for classifying the database in consonance with maximum entropy; and Predicting recurrence and non-recurrence implemented for the speculation of the drug upon considering maximum entropy using a machine learning technique.

2. The method as claimed in claim 1, wherein various classification of database in consonance with maximum entropy comprises:

classifying the database on the basis of gender if gender holds the maximum entropy using a first decision tree; classifying the database on the basis of treat if treat holds the maximum entropy using a second decision tree; and classifying the database on the basis of age if age holds the maximum entropy using a third decision tree.

3. The method as claimed in claim 1, wherein a set of queries includes, depressed status, duration of feeling of depression, age and gender, intake of medicine, name of medicine, and duration of medicine intake.

4. The method as claimed in claim 1, wherein for the configuration of replicas for having speculations of machine learning algorithms consisting of K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Random Forest are implemented.

5. The method as claimed in claim 1, wherein for non-recurrence prediction, the speculated drug becomes an identical drug which is carried on by a patient.

6. The method as claimed in claim 2, wherein the first decision tree is applied for the categorization with the application of gender, drug, and <Time, AcuteT>.

7. The method as claimed in claim 2, wherein the second decision tree is used for the categorization with the implementation of gender, drug, and age.

8. The method as claimed in claim 2, wherein the third decision tree is applied for the categorization with the implementation of age, drug, and <Time, AcuteT>.